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This article demonstrates a method using tools from the field of Natural Language Processing (NLP) to aid in analyzing theatrical texts and similar works. The method deploys pre-trained large language model neural networks to gather metadata for a text that is amenable to downstream statistical analyses surfacing patterns of interest in character dialogue. We specifically focus on Shakespeare's works, collecting metadata in the form of sentiment and emotion scores for each line of his plays. In addition to sentiment and emotion scores produced by NLP models, we also directly gather metadata such as genre, line length, and character gender. We show how these metadata may be used to illuminate a number of interesting patterns in Shakespearean character which may be difficult to detect from a direct reading of the texts. We use these metadata to expose statistically significant relationships in Shakespeare between character gender and the emotional content of that character's dialogue, controlling for genre. We also present here the publicly available dataset that we have compiled to perform these analyses. The data collects text from Shakespeare's plays along with a variety of metadata useful for this and other forms of analysis of Shakespeare's works. The methodology demonstrated here may be extended to other varieties of metadata provided by large NLP models. [ABSTRACT FROM AUTHOR]

Background In medical education, artificial intelligence techniques such as natural language processing (NLP) are starting to be used to capture and analyze emotions through written text. Objective To explore the application of NLP techniques to understand resident and faculty emotions related to entrustable professional activity (EPA) assessments. Methods Open-ended text data from a survey on emotions toward EPA assessments were analyzed. Respondents were residents and faculty from pediatrics (Peds), general surgery (GS), and emergency medicine (EM), recruited for a larger emotions study in 2023. Participants wrote about their emotions related to receiving/completing EPA assessments. We analyzed the frequency of words rated as positive via a validated sentiment lexicon used in NLP studies. Specifically, we were interested if the count of positive words varied as a function of group membership (faculty, resident), specialty (Peds, GS, EM), gender (man, woman, nonbinary), or visible minority status (yes, no, omit). Results A total of 66 text responses (30 faculty, 36 residents) contained text data useful for sentiment analysis. We analyzed the difference in the count of words categorized as positive across group, specialty, gender, and being a visible minority. Specialty was the only category revealing significant differences via a bootstrapped Poisson regression model with GS responses containing fewer positive words than EM responses. Conclusions By analyzing text data to understand emotions of residents and faculty through an NLP approach, we identified differences in EPA assessment-related emotions of residents versus faculty, and differences across specialties. [ABSTRACT FROM AUTHOR]

Nowadays, the omnipresence of the Internet has fostered a subculture that congregates around the contemporary milieu. The subculture artfully articulates the intricacies of human feelings by ardently pursuing the allure of novelty, a fact that cannot be disregarded in the sentiment analysis. This paper aims to enrich data through the lens of subculture, to address the insufficient training data faced by sentiment analysis. To this end, a new approach of subculture‐based data augmentation (SCDA) is proposed, which engenders enhanced texts for each training text by leveraging the creation of specific subcultural expression generators. The extensive experiments attest to the effectiveness and potential of SCDA. The results also shed light on the phenomenon that disparate subcultural expressions elicit varying degrees of sentiment stimulation. Moreover, an intriguing conjecture arises, suggesting the linear reversibility of certain subcultural expressions. [ABSTRACT FROM AUTHOR]

TripAdvisor reviews and comparable data sources play an important role in many tasks in Natural Language Processing (NLP), providing a data basis for the identification and classification of subjective judgments, such as hotel or restaurant reviews, into positive or negative polarities. This study explores three important factors influencing variation in crowdsourced polarity judgments, focusing on TripAdvisor reviews in Spanish. Three hypotheses are tested: the role of Part Of Speech (POS), the impact of sentiment words such as "tasty", and the influence of neutral words like "ok" on judgment variation. The study's methodology employs one-word titles, demonstrating their efficacy in studying polarity variation of words. Statistical tests on mean equality are performed on word groups of our interest. The results of this study reveal that adjectives in one-word titles tend to result in lower judgment variation compared to other word types or POS. Sentiment words contribute to lower judgment variation as well, emphasizing the significance of sentiment words in research on polarity judgments, and neutral words are associated with higher judgment variation as expected. However, these effects cannot be always reproduced in longer titles, which suggests that longer titles do not represent the best data source for testing the ambiguity of single words due to the influence on word polarity by other words like negation in longer titles. This empirical investigation contributes valuable insights into the factors influencing polarity variation of words, providing a foundation for NLP practitioners that aim to capture and predict polarity judgments in Spanish and for researchers that aim to understand factors influencing judgment variation. [ABSTRACT FROM AUTHOR]

In the last few years, various topics, including sports, have seen social media platforms emerge as significant sources of information and viewpoints. Football fans use social media to express their opinions and sentiments about their favourite teams and players. Analysing these opinions can provide valuable information on the satisfaction of football fans with their teams. In this article, we present Soutcom, a scalable real‐time system that estimates the satisfaction of football fans with their teams. Our approach leverages the power of social media platforms to gather real‐time opinions and emotions of football fans and applies state‐of‐the‐art machine learning‐based sentiment analysis techniques to accurately predict the sentiment of Arabic posts. Soutcom is designed as a cloud‐based scalable system integrated with the X (formerly known as Twitter) API and a football data service to retrieve live posts and match data. The Arabic posts are analysed using our proposed bidirectional LSTM (biLSTM) model, which we trained on a custom dataset specifically tailored for the sports domain. Our evaluation shows that the proposed model outperforms other machine learning models such as Random Forest, XGBoost and Convolutional Neural Networks (CNNs) in terms of accuracy and F1‐score with values of 0.83 and 0.82, respectively. Furthermore, we analyse the inference time of our proposed model and suggest that there is a trade‐off between performance and efficiency when selecting a model for sentiment analysis on Arabic posts. [ABSTRACT FROM AUTHOR]

ChatGPT has demonstrated impressive abilities and impacted various aspects of human society since its creation, gaining widespread attention from different social spheres. This study aims to comprehensively assess public perception of ChatGPT on Reddit. The dataset was collected via Reddit, a social media platform, and includes 23,733 posts and comments related to ChatGPT. Firstly, to examine public attitudes, this study conducts content analysis utilizing topic modeling with the Latent Dirichlet Allocation (LDA) algorithm to extract pertinent topics. Furthermore, sentiment analysis categorizes user posts and comments as positive, negative, or neutral using Textblob and Vader in natural language processing. The result of topic modeling shows that seven topics regarding ChatGPT are identified, which can be grouped into three themes: user perception, technical methods, and impacts on society. Results from the sentiment analysis show that 61.6% of the posts and comments hold favorable opinions on ChatGPT. They emphasize ChatGPT's ability to prompt and engage in natural conversations with users, without relying on complex natural language processing. It provides suggestions for ChatGPT developers to enhance its usability design and functionality. Meanwhile, stakeholders, including users, should comprehend the advantages and disadvantages of ChatGPT in human society to promote ethical and regulated implementation of the system. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) constitutes a significant field within natural language processing (NLP). This study proposes a multi-strategy text data augmentation methodology to overcome challenges such as limited dataset sizes and the absence of comprehensive, high-quality corpora in aspect-level sentiment classification (ASC). Specifically, it expands the SemEval 2014 Restaurant and Laptop training datasets from 3017 to 4646 instances and from 1864 to 3693 instances, respectively. The methodology encompasses both word-level and sentence-level augmentations. Evaluations using advanced deep learning techniques, including Att-LSTM, SVM, CNN, and Bi-LSTM, were conducted on the enhanced SemEval 2014 Task 4 restaurant dataset and laptops dataset. The test dataset sizes are 920 and 668, respectively. The Att-LSTM, demonstrating superior performance, recorded F1 score improvements of 5.0% and 4.4% on the restaurant and laptop datasets, respectively, following the application of the multi-strategy augmentation methodology compared to others. This approach significantly enlarges the dataset and improves performance in ASC tasks. [ABSTRACT FROM AUTHOR]

Sentiment analysis is an essential task in natural language processing that involves identifying a text's polarity, whether it expresses positive, negative, or neutral sentiments. With the growth of social media and the Internet, sentiment analysis has become increasingly important in various fields, such as marketing, politics, and customer service. However, sentiment analysis becomes challenging when dealing with foreign languages, particularly without labelled data for training models. In this study, we propose an ensemble model of transformers and a large language model (LLM) that leverages sentiment analysis of foreign languages by translating them into a base language, English. We used four languages, Arabic, Chinese, French, and Italian, and translated them using two neural machine translation models: LibreTranslate and Google Translate. Sentences were then analyzed for sentiment using an ensemble of pre-trained sentiment analysis models: Twitter-Roberta-Base-Sentiment-Latest, bert-base-multilingual-uncased-sentiment, and GPT-3, which is an LLM from OpenAI. Our experimental results showed that the accuracy of sentiment analysis on translated sentences was over 86% using the proposed model, indicating that foreign language sentiment analysis is possible through translation to English, and the proposed ensemble model works better than the independent pre-trained models and LLM. [ABSTRACT FROM AUTHOR]

Severe mental disorders like Schizophrenia and related psychotic disorders (SRD) or Bipolar Disorder (BD) require pharmacological treatment for relapse prevention and quality of life improvement. Yet, treatment adherence is a challenge, partly due to patients' attitudes and beliefs towards their medication. Social media listening offers insights into patient experiences and preferences, particularly in severe mental disorders. All tweets posted between 2008 and 2022 mentioning the names of the main drugs used in SRD and BD were analyzed using advanced artificial intelligence techniques such as machine learning, and deep learning, along with natural language processing. In this 15-year study analyzing 893,289 tweets, second generation antipsychotics received more mentions in English tweets, whereas mood stabilizers received more tweets in Spanish. English tweets about economic and legal aspects displayed negative emotions, while Spanish tweets seeking advice showed surprise. Moreover, a recurring theme in Spanish tweets was the shortage of medications, evoking feelings of anger among users. This study's analysis of Twitter data, while insightful, may not fully capture the nuances of discussions due to the platform's brevity. Additionally, the wide therapeutic use of the studied drugs, complicates the isolation of disorder-specific discourse. Only English and Spanish tweets were examined, limiting the cultural breadth of the findings. This study emphasizes the importance of social media research in understanding user perceptions of SRD and BD treatments. The results provide valuable insights for clinicians when considering how patients and the general public view and communicate about these treatments in the digital environment. • Social Media listening might offer insights into adherence issues in severe mental disorders. • We analyzed 893,289 tweets from 2008 to 2022 with advance artificial intelligence techniques. • Differences in mentioned drugs in English and Spanish tweets revealed distinct themes and emotions. [ABSTRACT FROM AUTHOR]

The aspect-level sentiment analysis is widely used in public opinion analysis. However, the problem of context information loss and distortion with the increase of the model depth is rarely considered in previous research. Few studies have attempted to combine the feature extracted from different embedding models. Based on the correction strategy, the ensemble correction (EC) model proposed in this study can correct context information loss and distortion. Based on the ensemble learning strategy and the weight sharing strategy, EC can extract features from different word embedding models and can reduce computational complexity. Experiments on the resturant14, laptop14, resturant16 and twitter datasets show that the accuracies of the EC model are 0.8848, 0.8213, 0.9301 and 0.7731, respectively. The accuracy of the EC model is higher than state-of-the-art models. Ablation studies and case studies are used to verify the model structure. The optimal number of graph convolutional network (GCN) layers is also verified. [ABSTRACT FROM AUTHOR]

NLP, or natural language processing, is a subfield of AI that aims to equip computers with the ability to understand and analyze human language. Sentiment analysis is a widely used application of NLP, particularly for examining attitudes expressed in online conversations. Nevertheless, many social media comments are written in languages that are not native to the authors, making sentiment analysis more difficult, especially for languages with limited resources, such as Tamil. To tackle this issue, a code-mixed and sentiment-annotated corpus in Tamil and English was created. This article will explain how the corpus was established, including the process of data collection and the assignment of polarities. The article will also explore the agreement between annotators and the results of sentiment analysis performed on the corpus. This work signifies various performance metrics such as precision, recall, support, and F1-score for the transformer-based model such as BERT, RoBerta, and XLM-RoBerta. Among the various models, XLM-Robert shows slightly significant positive results on the code-mixed corpus when compared to the state of art models. [ABSTRACT FROM AUTHOR]

The analysis of sentiments and mining of opinions have become more and more important in years because of the development of social media technologies. The methods that utilize natural language processing and lexicon‐based sentiment analysis techniques to analyze people's opinions in texts require the proper extraction of sentiment words to ensure accuracy. The current issue is tackled with a novel perspective in this paper by introducing a hybrid sentiment analysis technique. This technique brings together Convolutional Neural Network (CNN) and Hidden Markov Models (HMMs), to accurately categorize text data and pinpoint feelings. The proposed method involves 1D convolutional‐layer CNN to extract hidden features from comments and applying HMMs on a feature‐sentence matrix, allowing for the utilization of word sequences in extracting opinions. The method effectively captures diverse text patterns by extracting a range of features from texts using CNN. Text patterns are learned using text HMM by calculating the probabilities between sequences of feature vectors and clustering feature vectors. The paper's experimental evaluation employs benchmark datasets such as CR, MR, Subj, and SST2, demonstrating that the proposed method surpasses existing sentiment analysis techniques and traditional HMMs. One of its strengths is to analyze a range of text patterns and identify crucial features that recognize the emotion of different pieces of a sentence. Additionally, the research findings highlight the improved performance of sentiment analysis tasks through the strategic use of zero padding in conjunction with the masking technique. [ABSTRACT FROM AUTHOR]

In natural language processing, the general approach to sentiment analysis involves a pre-training and fine-tuning paradigm using pre-trained language models combined with classifier models. Recently, numerous studies have applied prompts not only to downstream generation but also to classification tasks as well. However, to fully utilize the advantages of prompts and incorporate the context-dependent meaning of class representations into the prompts, it is necessary for the prompts to be learned similarly to the sentiment representations of each class. To achieve this, We introduce a novel method to learn soft prompts during fine-tuning. In this method, the class prompts are initialized with sentiment-related embeddings and are trained by a denoising task, which replaces them with masked tokens, just like the conventional masked language model (MLM) approach. Furthermore, a novel attention pattern is designed to tune attention between class prompts effectively. As a result, we demonstrate that our approach outperforms state-of-the-art models through experiments on four common datasets, achieving superior performance on sentiment analysis. • A soft prompt-based classification method, SPACE, is proposed for sentiment analysis. • SPACE utilizes a modified attention pattern to consider only the attentions between prompt and the input text. • A MLM task for prompts is suggested to generate more balanced and robust representations. • Experiments on 4 datasets show superior performance in sentiment analysis. [ABSTRACT FROM AUTHOR]

With the vast amount of social media posts available online, topic modeling and sentiment analysis have become central methods to better understand and analyze online behavior and opinion. However, semantic and sentiment analysis have rarely been combined for joint topic-sentiment modeling which yields semantic topics associated with sentiments. Recent breakthroughs in natural language processing have also not been leveraged for joint topic-sentiment modeling so far. Inspired by these advancements, this paper presents a novel framework for joint topic-sentiment modeling of short texts based on pre-trained language models and a clustering approach. The method leverages techniques from dimensionality reduction and clustering for which multiple algorithms were considered. All configurations were experimentally compared against existing joint topic-sentiment models and an independent sequential baseline. Our framework produced clusters with semantic topic quality scores of up to 0.23 while the best score among the previous approaches was 0.12 . The sentiment classification accuracy increased from 0.35 to 0.72 and the uniformity of sentiments within the clusters reached up to 0.9 in contrast to the baseline of 0.56 . The presented approach can benefit various research areas such as disaster management where sentiments associated with topics can provide practical useful information. [ABSTRACT FROM AUTHOR]

Interaction via social media involves frequent code‐mixed text, spelling errors and noisy elements, which creates a bottleneck in the performance of natural language processing applications. This proposed work is the first approach for code‐mixed Hindi‐English social media text that comprises language identification, detection and correction of non‐word (Out of Vocabulary) errors as well as real‐word errors occurring simultaneously. Each identified language (Devanagari Hindi, Roman Hindi, and English) has its own complexities and challenges. Errors are detected individually for each language and a suggestive list of the erroneous words is created. After this, a fuzzy graph between different words of the suggestive lists is generated using various semantic relations in Hindi WordNet. Word embeddings and Fuzzy graph‐based centrality measures are used to find the correct word. Several experiments are performed on different social media datasets taken from Instagram, Twitter, YouTube comments, Blogs, and WhatsApp. The experimental results demonstrate that the proposed system corrects out‐of‐vocabulary words as well as real‐word errors with a maximum recall of 0.90 and 0.67, respectively for Dev_Hindi and 0.87 and 0.66, respectively for Rom_Hindi. The proposed method is also applied for state‐of‐art sentiment analysis approaches where the F1‐score has been visibly improved. [ABSTRACT FROM AUTHOR]

In this study, we compared the social media net sentiment of one policy with two names. Specifically, we analyzed Obamacare and the Affordable Care Act (ACA) to understand how social media users engaged with each term on social media from March 2010 to March 2017. The net sentiment was measured with a sample of over 50 million micro-blogs, and the analysis was done using a combination of digital instruments and human validation. We found a significant difference between the social media engagement and sentiment of both terms, with the ACA performing significantly better than Obamacare, despite Obamacare's higher conversation volume. With the ACA having an average of 26% less negative sentiment than Obamacare, the findings of this study emphasize the need to be careful when attaching nicknames to public policy. The findings also have implications for policymakers and politicians. [ABSTRACT FROM AUTHOR]

Myasthenia gravis (MG) is a rare, autoimmune, antibody-mediated, neuromuscular disease. This study analyzed digital conversations about MG to explore unprovoked perspectives. Advanced search, data extraction, and artificial intelligence-powered algorithms were used to harvest, mine, and structure public domain digital conversations about MG from US Internet Protocol addresses (August 2021 to August 2022). Thematic analyses examined topics, mindsets, and sentiments/key drivers via natural language processing and text analytics. Findings were described by sex/gender and treatment experience with steroids or intravenous immunoglobulin (IVIg). The 13,234 conversations were extracted from message boards (51%), social media networks (22%), topical sites (21%), and blogs (6%). Sex/gender was confirmed as female in 5703 and male in 2781 conversations, and treatment experience was with steroids in 3255 and IVIg in 2106 conversations. Topics focused on diagnosis (29%), living with MG (28%), symptoms (24%), and treatment (19%). Within 3176 conversations about symptoms, eye problems (21%), facial muscle problems (18%), and fatigue (18%) were most commonly described. Negative sentiments about MG were expressed in 59% of conversations, with only 2% considered positive. Negative conversations were dominated by themes of impact on life (29%), misdiagnosis problems (27%), treatment issues (24%), and symptom severity (20%). Impact on life was a key driver of negativity in conversations by both men (27%) and women (34%), and treatment issues was a dominant theme in conversations by steroid-treated (29%) and IVIg-treated (31%) patients. Of 1382 conversations discussing treatment barriers, 36% focused on side effects, 33% on lack of efficacy, 21% on misdiagnosis, and 10% on cost/insurance. Side effects formed the main barrier in conversations by both steroid-treated and IVIg-treated patients. Capturing the patient voice via digital conversations reveals a high degree of concern related to burden of disease, misdiagnosis, and common MG treatments among those with MG, pointing to a need for treatment options that can improve quality of life. [ABSTRACT FROM AUTHOR]

Text-graph representation learning is a critical and important area of research with extensive applications in natural language processing (NLP). Recently, graph learning models based on graph neural networks (GNNs) have been effectively utilized for encoding text-graph representation for various tasks due to their ability to handle complex structures and capture global information. However, existing text-graph representation learning models are heavily based on the manual design of model architectures and fine-tuning hyperparameters, which is time-consuming and relies on expert knowledge. To address this challenge, we propose an automatic text-graph representation learning (AutoTGRL) framework for transductive and inductive learning-based downstream tasks on text graphs. Specifically, the AutoTGRL framework first builds a general text-graph representation learning model (TGRL model) for text-graph transductive and inductive learning. Then, to enable the automatic design of TGRL models, we propose an automated TGRL model search module. In the automated TGRL model search module, we propose an effective and customized search space called text-graph representation learning (TGRL) search space, which consists of three subspaces, including large-scale embedding strategy space, text-graph representation strategy space, and GNN structure and hyperparameter space, to build TGRL models. We propose to use a search algorithm to search for the best combinations to construct TGRL models to fulfill different downstream tasks from the TGRL search space. To demonstrate the effectiveness AutoTGRL framework, we apply it to text classification, aspect-based sentiment analysis (ABSA), and entity and relation extraction tasks. The extensive experiments demonstrate the superiority of AutoTGRL to design the optimal TGRL models, which outperform the state-of-the-art models over multiple datasets. [ABSTRACT FROM AUTHOR]

Emotion is closely associated with language, but we know very little about how children express emotion in their own writing. We used a large‐scale, cross‐sectional, and data‐driven approach to investigate emotional expression via writing in children of different ages, and whether it varies for boys and girls. We first used a lexicon‐based bag‐of‐words approach to identify emotional content in a large corpus of stories (N>100,000) written by 7‐ to 13‐year‐old children. Generalized Additive Models were then used to model changes in sentiment across age and gender. Two other machine learning approaches (BERT and TextBlob) validated and extended these analyses, converging on the finding that positive sentiments in children's writing decrease with age. These findings echo reports from previous studies showing a decrease in mood and an increased use of negative emotion words with age. We also found that stories by girls contained more positive sentiments than stories by boys. Our study shows the utility of large‐scale data‐driven approaches to reveal the content and nature of children's writing. Future experimental work should build on these observations to understand the likely complex relationships between written language and emotion, and how these change over development. [ABSTRACT FROM AUTHOR]

Text classification involves organizing textual information into predefined classes, a task which is particularly useful in domains like sentiment analysis, spam detection, and content labeling. In India, where a massive amount of information is generated daily through newspapers and social media, Hindi is one of the most widely used and spoken languages. However, there is limited research on Hindi text classification and, particularly, regarding Hindi news classification. This paper presents a research study to classify Hindi news articles published in Hindi-language newspapers in India by using and comparing various Machine Learning (ML) and Deep Learning (DL) algorithms. To prepare the textual news data for classification, pre-processing and feature engineering techniques, such as count vectorizer, Tf-Idf vectorizer and Doc2Vec, were used and applied to convert texts into vectors. This pre-processing step on the textual data was very challenging due to the presence of multimodal words, conjunctions, punctuation, and special characters in Hindi texts. The study considered Hindi news headlines from predetermined categories (Science, Sports, Entertainment and Business) and, among the different ML and DL models tested and evaluated, Linear Regression with Doc2Vec vectorizer and SGD classifier with Tf-Idf vectorizer produced best accuracies of 97.04% and 96.59%, respectively. The best performing DL model was found to be the Bi-LSTM with an accuracy of approximately 97% on the testing data. [ABSTRACT FROM AUTHOR]

COVID‐19 pandemic has impacted many nations, causing physical as well as mental health concerns globally. In most countries, governments enforced strict lockdowns and social distancing, thus affecting people's daily lives. People usually tweet their views on online platforms that is unstructured text with implicit meaning. With the evolution of artificial intelligence in the natural language processing domain, the prediction of sentiments accurately has become a challenge. To contribute as a solution to this, a hybrid approach is proposed for sentiment prediction with the use of an evolutionary‐based approach, transfer‐based learning and machine learning. The proposed approach uses bidirectional encoder representations from transformers (BERT) with genetic algorithm (GA) and support vector machine (SVM), namely, hybrid evolutionary intelligent model (GA‐BERT‐SVM). These approaches aid in extracting important features considering semantics and context present in the text. To avoid the limitations of the backpropagation approach, such as trapping in local minima and overfitting the data, the initial parameters (weights and biases) of the dense layers has been optimized using GA. Additionally, the pretrained BERT layers are utilized without any modification, following a standard transfer learning approach. The BERT embeddings are concatenated with the SVM for training and classification. GridSearchCV and GeneticSearchCV is used for obtaining optimal parameters of SVM. A multi‐classification problem is tackled using a benchmark COVID‐19 dataset, which comprises of Twitter data and is categorized into COVIDSENTI‐A, COVIDSENTI‐B, COVIDSENTI‐C and a combined dataset called COVIDSENTI. Experimental evaluation demonstrates promising results of the proposed model in terms of accuracy, F1‐score, precision and recall, surpassing state‐of‐the‐art approaches. [ABSTRACT FROM AUTHOR]

Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning Recommender Systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. Users posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. Innovative has been made in this work to propose a new e-learning recommendation system based on individualization and FSA. A new framework is proposed based on collaborative filtering models (CFMs) integrating with fine-grained sentiment analysis (FSA) for hybrid recommendation (CFISAR) for effective recommendations. CFMs attempt to capture the learner's latent factors based on their selections of interest to build the learner profile. FSA models are introduced to deliver e-content with the highest ranked ratings related to the learner's area and interests based on the extracted learner model. Moreover, a new approach is proposed to update the system continuously and not keep it bound to certain items by adding new books, where the initial rating of these new books is predicted based on FSA models. CFISAR is explored utilizing six CFMs to generate the prediction matrix and derive the learner model, resulting in a low MSE of 0.699 for Asymmetric SVD. The system used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context, and leveraging the goodness of deep learning, which predicted an accuracy of 0.9264% for the Peephole algorithm, that performed better than other models. [ABSTRACT FROM AUTHOR]

Sentiment analysis aims to study, analyse and identify the sentiment polarity contained in subjective documents. In the realm of natural language processing (NLP), the study of sentiment analysis and its subtask research is a hot topic, which has very important significance. The existing sentiment analysis methods based on sentiment lexicon and machine learning take into account contextual semantic information, but these methods still lack the ability to utilize context information, so they cannot effectively encode context information. Inspired by the concept of density matrix in quantum mechanics, we propose a sentiment analysis method, named Complex-valued Quantum-enhanced Long Short-term Memory Neural Network (CQLSTM). It leverages complex-valued embedding to incorporate more semantic information and utilizes the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network for feature extraction. Specifically, a complex-valued neural network based on density matrix is used to capture interactions between words (i.e., the correlation between words). Additionally, the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network, which is inspired by the quantum measurement theory and quantum long short-term memory neural network, is developed to learn interactions between sentences (i.e., contextual semantic information). This approach effectively encodes semantic dependencies, which reflects the dispersion of words in the embedded space of sentences and comprehensively captures interactive information and long-term dependencies among the emotional features between words. Comparative experiments were performed on four sentiment analysis datasets using five traditional models, showcasing the effectiveness of the CQLSTM model. [ABSTRACT FROM AUTHOR]

Purpose: Given the limited availability of topic-specific resources, many people turn to anonymous social media platforms such as Reddit to seek information and connect to others with similar experiences and needs. Mining of such data can therefore identify unmet needs within the community and allow speech-language pathologists to incorporate clients' real-life insights into clinical practices. Method: A mixed-method analysis was performed on 3,648 traumatic brain injury (TBI) subreddit posts created between 2013 and 2021. Sentiment analysis was used to determine the sentiment expressed in each post; topic modeling and qualitative content analysis were used to uncover the main topics discussed across posts. Subgroup analyses were conducted based on injury severity, chronicity, and whether the post was authored by a person with TBI or a close other. Results: There was no significant difference between the number of posts with positive sentiment and the number of posts with negative sentiment. Comparisons between subgroups showed significantly higher positive sentiment in posts by or about people with moderate-to-severe TBI (compared to mild TBI) and who were more than 1 month postinjury (compared to less than 1 month). Posts by close others had significantly higher positive sentiment than posts by people with TBI. Topic modeling identified three meta-themes: Recovery, Symptoms, and Medical Care. Qualitative content analysis further revealed that returning to productivity and life as well as sharing recovery tips were the primary focus under the Recovery theme. Symptom-related posts often discussed symptom management and validation of experiences. The Medical Care theme encompassed concerns regarding diagnosis, medication, and treatment. Conclusions: Concerns and needs shift over time following TBI, and they extend beyond health and functioning to participation in meaningful daily activities. The findings can inform the development of tailored educational resources and rehabilitative approaches, facilitating recovery and community building for individuals with TBI. [ABSTRACT FROM AUTHOR]

These days, the world has become a non-denominational place due to cyberspace. Digital services such as search portals and social platforms have become intricate with the regular course of daily life. As a massive number of clients' thoughts are available on the internet, sentiment analysis has become one of the most prolific investigation arenas in natural language processing. Sarcasm detection is a vital phase in sentiment analysis because of the intrinsically vague nature of sarcasm. To subsidize as a resolution to the current ever-increasing arena of attention on the ever-increasing volume of digital data, this work proposes a novel voting-based ensemble approach for sarcasm detection utilizing deep learning techniques. This paper uses Bidirectional Encoder Representations from Transformers to build contextual word embeddings and feeds those to the network, which is an ensemble of four deep learning models. Convolutional Neural Network, Bidirectional Long Short-Term Memory, and parallel and sequential combinations of both are among the models. For classification, the acquired features from each model are experimented with four machine learning classifiers, such as Support Vector Machine, Least Squares Support Vector Machine, Multinomial Naive Bayes, and Random Forest, along with Sigmoid activation function. Out of these five, the optimal classifier is selected for each model. Finally, a majority voting is performed on the outputs of all four models to differentiate between sarcastic and non-sarcastic texts. The proposed approach has been assessed using two benchmark datasets containing English-language texts, namely, news headlines and the self-annotated reddit corpus. Experiments demonstrated that the proposed methodology produces an accuracy of 94.89% on the news headlines repository, which is a gain of 2.99%, and an F1-score of 80.49% on the self-annotated reddit corpus, which is an improvement of 0.52% over the previous state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is a study area focused on obtaining contextual polarity from the text. Currently, deep learning has obtained outstanding results in this task. However, much annotated data are necessary to train these algorithms, and obtaining this data is expensive and difficult. In the context of low-resource scenarios, this problem is even more significant because there are little available data. Transfer learning (TL) can be used to minimize this problem because it is possible to develop some architectures using fewer data. Language models are a way of applying TL in natural language processing (NLP), and they have achieved competitive results. Nevertheless, some models need many hours of training using many computational resources, and in some contexts, people and organizations do not have the resources to do this. In this paper, we explore the models BERT (Pretraining of Deep Bidirectional Transformers for Language Understanding), MultiFiT (Efficient Multilingual Language Model Fine-tuning), ALBERT (A Lite BERT for Self-supervised Learning of Language Representations), and RoBERTa (A Robustly Optimized BERT Pretraining Approach). In all of our experiments, these models obtain better results than CNN (convolutional neural network) and LSTM (Long Short Term Memory) models. To MultiFiT and RoBERTa models, we propose a pretrained language model (PTLM) using Twitter data. Using this approach, we obtained competitive results compared with the models trained in formal language datasets. The main goal is to show the impacts of TL and language models comparing results with other techniques and showing the computational costs of using these approaches. [ABSTRACT FROM AUTHOR]

During the past years, we have seen a steady increase in the number of social networks worldwide. Among them, Twitter has consolidated its position as one of the most influential social platforms, with Brazilian Portuguese speakers holding the fifth position in the number of users. Due to the informal linguistic style of tweets, the discovery of information in such an environment poses a challenge to Natural Language Processing (NLP) tasks such as sentiment analysis. In this work, we state sentiment analysis as a binary (positive and negative) and multiclass (positive, negative, and neutral) classification task at the Portuguese-written tweet level. Following a feature extraction approach, embeddings are initially gathered for a tweet and then given as input to learning a classifier. This study was designed to evaluate the effectiveness of different word representations, from the original pre-trained language model to continued pre-training strategies, to improve the predictive performance of sentiment classification, using three different classifier algorithms and eight Portuguese tweets datasets. Because of the lack of a language model specific to Brazilian Portuguese tweets, we have expanded our evaluation to consider six different embeddings: fastText, GloVe, Word2Vec, BERT-multilingual (mBERT), BERTweet, and BERTimbau. The experiments showed that embeddings trained from scratch solely using the target Portuguese language, BERTimbau, outperform the static representations, fastText, GloVe, and Word2Vec, and the Transformer-based models BERT multilingual and BERTweet. In addition, we show that extracting the contextualized embedding without any adjustment to the pre-trained language model is the best approach for most datasets. [ABSTRACT FROM AUTHOR]

Understanding customer emotions and preferences is paramount for success in the dynamic product design landscape. This paper presents a study to develop a prediction pipeline to detect the aspect and perform sentiment analysis on review data. The pre-trained Bidirectional Encoder Representation from Transformers (BERT) model and the Text-to-Text Transfer Transformer (T5) are deployed to predict customer emotions. These models were trained on synthetically generated and manually labeled datasets to detect the specific features from review data, then sentiment analysis was performed to classify the data into positive, negative, and neutral reviews concerning their aspects. This research focused on eco-friendly products to analyze the customer emotions in this category. The BERT and T5 models were finely tuned for the aspect detection job and achieved 92% and 91% accuracy, respectively. The best-performing model will be selected, calculating the evaluation metrics precision, recall, F1-score, and computational efficiency. In these calculations, the BERT model outperforms T5 and is chosen as a classifier for the prediction pipeline to predict the aspect. By detecting aspects and sentiments of input data using the pre-trained BERT model, our study demonstrates its capability to comprehend and analyze customer reviews effectively. These findings can empower product designers and research developers with data-driven insights to shape exceptional products that resonate with customer expectations. [ABSTRACT FROM AUTHOR]

Data mining and natural language processing researchers have been working on sentiment analysis for the past decade. Using deep neural networks (DNNs) for sentiment analysis has recently shown promising results. A technique of studying people's attitudes through emotional sentiment analysis of data generated from various sources such as Twitter, social media reviews, etc. and classifying emotions based on the given data is related to text data generation. Therefore, the proposed study proposes a well-known deep learning technique for facet-based emotional mood classification using text data that can handle a large amount of content. Text data pre-processing uses stemming, segmentation, tokenization, case folding, and removal of stop words, nulls, and special characters. After data pre-processing, three word embedding approaches such as Assimilated N-gram Approach (ANA), Boosted Term Frequency Inverse Document Frequency (BT-IDF) and Enhanced Two-Way Encoder Representation from Transformers (E-BERT) are used to extract relevant features. The extracted features from the three different approaches are concatenated using the Feature Fusion Approach (FFA). The optimal features are selected using the Intensified Hunger Games Search Optimization (I-HGSO) algorithm. Finally, aspect-based sentiment analysis is performed using the Senti-BILSTM (Deep Aspect-EMO SentiNet) autoencoder based on the Hybrid Emotional Aspect Capsule autoencoder. The experiment was built on the yelp reviews dataset, IDMB movie review dataset, Amazon reviews dataset and the Twitter sentiment dataset. A statistical evaluation and comparison of the experimental results are conducted with respect to the accuracy, precision, specificity, the f1-score, recall, and sensitivity. There is a 99.26% accuracy value in the Yelp reviews dataset, a 99.46% accuracy value in the IMDB movie reviews dataset, a 99.26% accuracy value in the Amazon reviews dataset and a 99.93% accuracy value in the Twitter sentiment dataset. [ABSTRACT FROM AUTHOR]

TikTok—the world's most downloaded app since 2020, has become a place for more than silly dancing and lip-syncing. TikTok users are increasingly turning to TikTok for news content. Meanwhile, news publishers are embracing TikTok to reach a younger audience. We aim to examine the content strategy adopted by the most-followed news publishers on TikTok and how effective their TikTok strategy is in spurring audience engagement in terms of liking, commenting, and sharing. This study retrieved 101,292 TikTok news videos as of November 22, 2022. With the help of computer vision, natural language processing, and sentiment analysis, we found that TikTok news videos containing negative sentiment and more second-person view shots are associated with significantly higher audience engagement. In addition, this study demonstrated that the TikTok video features and engagement levels differ between the news publishers and other TikTok creators. Moderator analysis shows that both the effect of negative sentiment on engagement and the effect of the second-person view on engagement are moderated by the TikTok account type. The impact of negative sentiment and second-person view on engagement behaviors becomes smaller or even insignificant for news publisher TikTok videos. Theoretical and practical implications are discussed in this study. [ABSTRACT FROM AUTHOR]

In recent years, many real-time applications extensively utilize text classification problems. It is vital to make use of text classification methods by developing new text mining and applications of natural language processing (NLP). Among the most well-known uses of text classification are intent detection, language recognition, topic labelling, and sentiment analysis. An in-depth examination of deep learning techniques is necessary because of the exponential rise in the quantity of complicated documents. Any deep learning algorithm's capacity to comprehend the nonlinear relationships between complicated models within data is what determines its success. Therefore, A researcher's formidable task is to develop appropriate techniques, structures, and models to classify texts. This study suggests a blended deep-learning framework depending on the attention mechanism and is well-analyzed for text categorization. The suggested hybrid approach entails a Convolutional Bidirectional Gated Recurrent Unit (Bi-GRU) featuring an attention mechanism and output (CBGA) model. In this model, the attention mechanism is put after the Bi-GRU and then the construction output SoftMax layer. The model was implemented and operated on more than one dataset. According to the comparative analysis, the suggested CBGA model performs better than the other one that was chosen using the conventional approach. We obtained a high classification accuracy on some of the datasets, and these results are the optimum that has been achieved so far. [ABSTRACT FROM AUTHOR]

Span-level method achieves competitive results in Aspect Sentiment Triplet Extraction (ASTE) by enumerating all possible spans. However, previous span-level methods fail to exploit syntactic information to identify the correspondence between aspect terms and opinion terms, which makes the extracted triplets inaccurate. In this paper, we propose a syntactic and semantic dual-enhanced bidirectional network (SSBN) for ASTE task. By constructing word dependencies as a graph and embedding them into features to capture syntactic information more effectively in bidirectional network. Furthermore, we design a pruning strategy that uses part-of-speech information to alleviate the problem of identifying potential aspects and opinions from a large number of spans. We conduct extensive experiments on four benchmark datasets, and the experimental results demonstrate the effectiveness of the SSBN model. [ABSTRACT FROM AUTHOR]

Negation is a linguistic phenomenon that can cause sentences to have their meanings reversed. It frequently inverts affirmative sentences into negative ones, affecting the polarity; therefore, the sentiment of the text also changes accordingly. Negation can be expressed differently, making it somewhat challenging to detect. As a result, detecting negation is critical for Sentiment Analysis (SA) system development and improvement and will increase classifier accuracy, but it also poses a significant conceptual and technical challenge. This paper aims to survey and gather the most recent research related to detecting negation in SA. Many researchers have worked and performed methods, including algorithmic, machine, and deep learning approaches such as Decision Tree (DT), Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naive Bayesian (NB), Logistic Regression (LR), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Bidirectional Long ShortTerm Memory (BiLSTM), and other hybrid methods such as rule-based and machine learning, lexicon and machine learning, machine learning, and deep learning. It addresses and tries to identify the gaps in the current studies, laying the foundation for future studies in this field. [ABSTRACT FROM AUTHOR]

The demand for explainable sentiment analysis has intensified, emphasizing the need for models that are both accurate and interpretable. This research introduces the Multi-Aspect Framework for Explainable Sentiment Analysis (MAFESA), a groundbreaking model that seamlessly integrates aspect extraction, sentiment prediction, and explainability. By harnessing the power of Latent Dirichlet Allocation (LDA) for aspect extraction and leveraging hierarchical neural networks for sentiment prediction, MAFESA achieves remarkable performance metrics. Notably, our framework outperforms state-of-the-art baseline models across benchmark datasets such as IMDB Movie Reviews, Amazon Product Reviews, and Twitter Sentiment Analysis. The inclusion of an explainability module, built around techniques like LIME, offers unparalleled insights into the model's decision-making, ensuring predictions are transparent and trustworthy. Our performance evaluations, underscored by a thorough ablation study, cross-validation, and rigorous statistical tests, attest to MAFESA's robustness, generalizability, and superiority. A detailed qualitative analysis further showcases the model's adeptness at discerning aspect-level nuances and delivering clear explanations for sentiment predictions. This research not only sets a new benchmark in explainable sentiment analysis but also provides a holistic framework that balances prediction precision with interpretability. • A unified model integrating aspect extraction, sentiment prediction, and explainability. • Leveraged Latent Dirichlet Allocation for granular understanding of sentiments in text. • Utilized hierarchical neural networks for improved sentiment prediction at both document and aspect levels. • Incorporated LIME-based explainability, ensuring transparent and trustworthy sentiment predictions. [ABSTRACT FROM AUTHOR]

In recent years, models such as the transformer have demonstrated impressive capabilities in the realm of natural language processing. However, these models are known for their complexity and the substantial training they require. Furthermore, the self-attention mechanism within the transformer, designed to capture semantic relationships among words in sequences, faces challenges when dealing with short sequences. This limitation hinders its effectiveness in five-polarity Arabic sentiment analysis (SA) tasks. The switch-transformer model has surfaced as a potential substitute. Nevertheless, when employing one-task learning for their training, these models frequently face challenges in presenting exceptional performances and encounter issues when producing resilient latent feature representations, particularly in the context of small-size datasets. This challenge is particularly prominent in the case of the Arabic dialect, which is recognized as a low-resource language. In response to these constraints, this research introduces a novel method for the sentiment analysis of Arabic text. This approach leverages multi-task learning (MTL) in combination with the switch-transformer shared encoder to enhance model adaptability and refine sentence representations. By integrating a mixture of experts (MoE) technique that breaks down the problem into smaller, more manageable sub-problems, the model becomes skilled in managing extended sequences and intricate input–output relationships, thereby benefiting both five-point and three-polarity Arabic sentiment analysis tasks. The proposed model effectively identifies sentiment in Arabic dialect sentences. The empirical results underscore its exceptional performance, with accuracy rates reaching 84.02% for the HARD dataset, 67.89% for the BRAD dataset, and 83.91% for the LABR dataset, as demonstrated by the evaluations conducted on these datasets. [ABSTRACT FROM AUTHOR]

The use of natural language processing, to identify, extract and organize sentiment from user generated texts in social networks, blogs or product review of text is known as sentiment analysis or opinion mining. Hausa language belongs to one of the major wellspoken languages in Africa and one of the three major Nigerian languages. Now investigating into such a language will have significant influence on social, economic business political and even educational services and settings. Some of these Hausa texts are abbreviated and some in acronym format which is a challenge to researchers as such comments are in an unstructured format and needs normalization to get further understanding of that text and also there is scarcity of sentiment analysis on Hausa abbreviation and acronym. Abbreviation is a shorten form of a word while acronym is an abbreviation formed from the initial letters of other words and pronounced as a word. This research aims to develop an improved Hausa Sentiment Dataset for the enhancement of sentiment analysis with abbreviation and acronyms. This is achieved by adapting to the approach for Hausa Sentiment Analysis based on Multinomial Naïve Bayes (MNB) and Logistic Regression algorithms using the count vectorizer, along with python libraries for NLP. This research affirmed that the improved dataset with abbreviation and acronym outperforms the plain Hausa dataset by 4% in accuracy using Multinomial Naïve Bayes. The result shows that in addition to normal preprocessing techniques of the social media stream, understanding, interpreting and resolving ambiguity in the usage of abbreviations and acronyms lead to improved accuracy of algorithms with evidence in the experimental result. [ABSTRACT FROM AUTHOR]

Nowadays, users prefer to share their opinions, suggestions, and complaints about the products and/or services they buy more on e-commerce sites, or they can decide to buy based on the comments written by other users. Sentiment analysis is very useful in determining their attitudes towards them. This study aims to perform ensemble learning-based sentiment analysis. For this purpose, firstly, data pre-processing was implemented on Amazon meal review data, the text was preprocessed, and the data was brought into a format suitable for learning models with feature engineering. Then the text has been classified according to whether it contained positive, negative, or neutral sentiments using Random Forest (RF), CatBoost, and XGBoost ensemble learning models. The obtained results have been evaluated in terms of training and test accuracy, precision, recall, and F1-score metrics. The highest test accuracy of 90.22% has been achieved in the classification using RF and CountVectorizer technique. In addition, the models have been tested on a new dataset created with web scraping. The results obtained provide insight into the company's products and services. [ABSTRACT FROM AUTHOR]

Parliamentary and legislative debate transcripts provide an informative insight into elected politicians' opinions, positions, and policy preferences. They are interesting for political and social sciences as well as linguistics and natural language processing (NLP) research. While exiting research studied individual parliaments, we apply advanced NLP methods to a joint and comparative analysis of six national parliaments (Bulgarian, Czech, French, Slovene, Spanish, and United Kingdom) between 2017 and 2020. We analyze emotions and sentiment in the transcripts from the ParlaMint dataset collection, and assess if the age, gender, and political orientation of speakers can be detected from their speeches. The results show some commonalities and many surprising differences among the analyzed countries. [ABSTRACT FROM AUTHOR]

The applications of artificial intelligence (AI) and natural language processing (NLP) have significantly empowered the safety and operational efficiency within the aviation sector for safer and more efficient operations. Airlines derive informed decisions to enhance operational efficiency and strategic planning through extensive contextual analysis of customer reviews and feedback from social media, such as Twitter and Facebook. However, this form of analytical endeavor is labor-intensive and time-consuming. Extensive studies have investigated NLP algorithms for sentiment analysis based on textual customer feedback, thereby underscoring the necessity for an in-depth investigation of transformer architecture-based NLP models. In this study, we conducted an exploration of the large language model BERT and three of its derivatives using an airline sentiment tweet dataset for downstream tasks. We further honed this fine-tuning by adjusting the hyperparameters, thus improving the model's consistency and precision of outcomes. With RoBERTa distinctly emerging as the most precise and overall effective model in both the binary (96.97%) and tri-class (86.89%) sentiment classification tasks and persisting in outperforming others in the balanced dataset for tri-class sentiment classification, our results validate the BERT models' application in analyzing airline industry customer sentiment. In addition, this study identifies the scope for improvement in future studies, such as investigating more systematic and balanced datasets, applying other large language models, and using novel fine-tuning approaches. Our study serves as a pivotal benchmark for future exploration in customer sentiment analysis, with implications that extend from the airline industry to broader transportation sectors, where customer feedback plays a crucial role. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) technology within the hotel industry marks a transformative shift in the way guests experience and engage with hospitality services. Virtual reality, with its immersive and interactive capabilities, enables hotels to provide a novel and engaging environment for guests. From virtual tours of hotel rooms and amenities to immersive experiences showcasing local attractions and cultural highlights, VR has the potential to revolutionize the pre-booking and on-site guest experience. This paper focused on the user experiences within hotel rooms enhanced with virtual reality (VR) technology. Leveraging content analysis, sentiment analysis, and advanced classification models, we aim to unravel the intricacies of user sentiments and preferences in this evolving domain. The content analysis reveals a spectrum of user opinions, ranging from enthusiastic endorsements of immersive VR content to nuanced critiques of room ambiance and interactivity. Subsequently, a sentiment analysis model accurately categorizes these sentiments, showcasing its effectiveness in capturing the diverse user expressions. Our classification analysis demonstrates the robustness of the sentiment analysis model, with high accuracy, precision, recall, and F1-score metrics. Comparatively, we introduce a proposed BERT model, harnessing advanced natural language processing techniques, and observe its performance against traditional sentiment analysis and an AutoEncoder Model. The results indicate that the BERT model matches the performance of traditional sentiment analysis, outperforming the AutoEncoder Model. This underscores the effectiveness of leveraging state-of-the-art language models in understanding and classifying user sentiments. [ABSTRACT FROM AUTHOR]

This study explores the connection between the fractal dimensions of time series representing sentiments regarding economic news and the fractal dimensions of correlation networks among companies listed in the Borsa Istanbul star section. While there have been many studies on the correlation between different time series, the investigation into the impact of fractal dimensions on correlation networks' dynamics has been somewhat restricted. This study investigates the correlation networks among companies listed in the Borsa Istanbul Stars segment, employing distance and topological filters. The network fractional dimensions are evaluated using the box counting and information dimension techniques. A convolutional neural network is employed to perform analysis of sentiments regarding on 2020 Turkish economic news. The network is trained on user comments and specifically built to identify fluctuations in news editorials. The Zemberek natural language processing framework is beneficial for data preprocessing. Identical analytical methods are employed to quantify the fractal dimensions of each sentiment time series. Experiments are performed on these measurements using various sliding window widths to ascertain both independence and causality. The findings indicate a substantial correlation between market behavior and the feelings expressed in economic news. [ABSTRACT FROM AUTHOR]

Introduction: Noninvasive prenatal testing (NIPT) can be used to detect fetal chromosomal abnormalities early in pregnancy. As eligibility criteria broaden and screening targets expand, gauging public acceptability of NIPT becomes increasingly important. Leveraging social media as a rich source of public discourse, the purpose of this study was to understand public opinions and attitudes toward NIPT on the social media platform Reddit. Methods: We applied content and natural language processing techniques (i.e., sentiment analysis) to textual data collected from 4 Reddit communities focusing on the NIPT content posted from September 2012 to September 2022 (367 posts and 7,822 comments in total). Results: Content analysis findings indicated that social media users consider NIPT to be worthwhile. Reasons NIPT was perceived to be not worthwhile related to unwanted anxiety, and the fact that NIPT results would not change anything about their approach to pregnancy were also expressed. The sentiment analysis identified more positive than negative emotions; the mean sentiment scores ranged from 0.48 to 1.22, depending on the specific Lexicon used. Specific emotions (i.e., trust, fear) were also identified. Conclusion: Our novel approach to understanding public perception and attitudes toward NIPT yielded results that are consistent with conventional patient-oriented research methods. These findings may not only contribute to ongoing improvements in prenatal patient care, research, and policy but also indicate that sentiment analysis applied to social media data can serve as a suitable means to assess public acceptability of NIPT, particularly as public dialogue on this topic increases over time. [ABSTRACT FROM AUTHOR]

Traditional sentiment analysis focuses on text-level sentiment mining, transforming sentiment mining into classification or regression problems, resulting in a sentiment analysis low accuracy rate. Sentiment analysis refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study sentimental states. Therefore, more scholars have begun to focus on speech recognition and facial expression recognition research, and extracting and analysing people's sentiment tendencies can improve sentiment recognition accuracy. Traditional single-modal sentiment analysis can no longer meet people's needs. Therefore, this paper proposes a multimodal sentiment analysis method based on the multimodal sentiment analysis method that can obtain more sentimental information sources and help people make better decisions. The experimental results in this paper show that the highest recognition rates of CNN-SVM, RNN-SVM, and CRNN-SVM were 76.8%, 71.2%, and 93.5%, respectively. It can be seen that CRNN-SVM has the highest sentiment tendency recognition rate in deep learning, so it is suitable to apply CRNN-SVM to sentiment tendency analysis system design in this paper. The average accuracy rate of the system designed in this paper was 91%, and the stability was also very strong, which shows that the system designed in this paper is meaningful. The main contribution of this paper is based on the limitations of single-mode emotion analysis. It proposes a multimode emotion analysis method and introduces a convolutional neural network to help people obtain more emotional information sources to meet their needs. [ABSTRACT FROM AUTHOR]