Search

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]

Stance detection is a crucial task in natural language processing and social computing, focusing on classifying expressed attitudes towards specific targets based on the input text. Conventional methods predominantly view stance detection as a task of target-oriented, sentence-level text classification. On popular social media platforms like Twitter, users often express their opinions through hashtags in addition to textual content within tweets. However, current methods primarily treat hashtags as data retrieval labels, neglecting to effectively utilize the semantic information they carry. In this paper, we propose a large language model knowledge-enhanced stance detection framework (LKESD) for stance detection. LKESD contains three main components: an instruction-prompted background knowledge acquisition module (IPBKA) that retrieves background knowledge of hashtags by providing handcrafted prompts to large language models (LLMs); a graph convolutional feature-enhancement module (GCFEM) is designed to extract the semantic representations of words that frequently co-occur with hashtags in the dataset by leveraging textual associations; an a knowledge fusion network (KFN) is proposed to selectively integrate graph representations and LLM features using a prompt-tuning framework. Extensive experimental results on three benchmark datasets demonstrate that our LKESD method outperforms 2.7% on all setups over compared methods, validating its effectiveness in stance detection tasks. [ABSTRACT FROM AUTHOR]

This paper presents an analysis on information disorder in social media platforms. The study employed methods such as Natural Language Processing, Topic Modeling, and Knowledge Graph building to gain new insights into the phenomenon of fake news and its impact on critical thinking and knowledge management. The analysis focused on four research questions: 1) the distribution of misinformation, disinformation, and malinformation across different platforms; 2) recurring themes in fake news and their visibility; 3) the role of artificial intelligence as an authoritative and/or spreader agent; and 4) strategies for combating information disorder. The role of AI was highlighted, both as a tool for fact-checking and building truthiness identification bots, and as a potential amplifier of false narratives. Strategies proposed for combating information disorder include improving digital literacy skills and promoting critical thinking among social media users. [ABSTRACT FROM AUTHOR]

Social media platforms have surpassed cultural and linguistic boundaries, thus enabling online communication worldwide. However, the expanded use of various languages has intensified the challenge of online detection of hate speech content. Despite the release of multiple Natural Language Processing (NLP) solutions implementing cutting-edge machine learning techniques, the scarcity of data, especially labeled data, remains a considerable obstacle, which further requires the use of semisupervised approaches along with Generative Artificial Intelligence (Generative AI) techniques. This paper introduces an innovative approach, a multilingual semisupervised model combining Generative Adversarial Networks (GANs) and Pretrained Language Models (PLMs), more precisely mBERT and XLM-RoBERTa. Our approach proves its effectiveness in the detection of hate speech and offensive language in Indo-European languages (in English, German, and Hindi) when employing only 20% annotated data from the HASOC2019 dataset, thereby presenting significantly high performances in each of multilingual, zero-shot crosslingual, and monolingual training scenarios. Our study provides a robust mBERT-based semisupervised GAN model (SS-GAN-mBERT) that outperformed the XLM-RoBERTa-based model (SS-GAN-XLM) and reached an average F1 score boost of 9.23% and an accuracy increase of 5.75% over the baseline semisupervised mBERT model. [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]

The pervasive reach of social media like the X platform, formerly known as Twitter, offers unique opportunities for real-time analysis of cyberattack developments. By parsing and classifying tweets related to cyberattacks, we can glean valuable insights into their type, location, impact, and potential mitigation strategies. However, with millions of daily tweets, manual analysis is inefficient and time-consuming. This paper proposes an interactive and automated dashboard powered by natural language processing to effectively address this challenge. First, we created the CybAttT dataset, which contains 36,071 manually labeled English cyberattack tweets. We experimented with different classification algorithms. Following that, the best model was deployed and integrated into the streaming pipeline for real-time classification. This dynamic dashboard makes use of four different visualization formats: a geographical map, a data table, informative tiles, and a bar chart. Users can readily access crucial information about attacks, including location, timing, and perpetrators, enabling a swift response and mitigation efforts. Our experimental results demonstrated the dashboard's promising visualization capabilities, highlighting its potential as a valuable tool for organizations and individuals seeking an intuitive and comprehensive overview of cyberattack events. [ABSTRACT FROM AUTHOR]

Text-guided image editing has been highlighted in the fields of computer vision and natural language processing in recent years. The approach takes an image and text prompt as input and aims to edit the image in accordance with the text prompt while preserving text-unrelated regions. The results of text-guided image editing differ depending on the way the text prompt is represented, even if it has the same meaning. It is up to the user to decide which result best matches the intended use of the edited image. This paper assumes a situation in which edited images are posted to social media and proposes a novel text-guided image editing method to help the edited images gain attention from a greater audience. In the proposed method, we apply the pre-trained text-guided image editing method and obtain multiple edited images from the multiple text prompts generated from a large language model. The proposed method leverages the novel model that predicts post scores representing engagement rates and selects one image that will gain the most attention from the audience on social media among these edited images. Subject experiments on a dataset of real Instagram posts demonstrate that the edited images of the proposed method accurately reflect the content of the text prompts and provide a positive impression to the audience on social media compared to those of previous text-guided image editing methods. [ABSTRACT FROM AUTHOR]

The advent of online platforms and services has revolutionized communication, enabling users to share opinions and ideas seamlessly. However, this convenience has also brought about a surge in offensive and harmful language across various communication mediums. In response, social platforms have turned to automated methods to identify offensive content. A critical research question emerges when investigating the role of specific text spans within comments in conveying offensive characteristics. This paper conducted a comprehensive investigation into detecting offensive text spans in Romanian language comments using Transformer encoders and Large Language Models (LLMs). We introduced an extensive dataset of 4800 Romanian comments annotated with offensive text spans. Moreover, we explored the impact of varying model sizes, architectures, and training data volumes on the performance of offensive text span detection, providing valuable insights for determining the optimal configuration. The results argue for the effectiveness of BERT pre-trained models for this span-detection task, showcasing their superior performance. We further investigated the impact of different sample-retrieval strategies for few-shot learning using LLMs based on vector text representations. The analysis highlights important insights and trade-offs in leveraging LLMs for offensive-language-detection tasks. [ABSTRACT FROM AUTHOR]

People have become part of the digital era with the advent of the Web. They actively create, share, a variety of content on the web. Unlike earlier days, people widely use different social platforms to talk about their interests, hobbies, reviews on movies, and purchased items in natural language. Processing such natural languages with mixed language tasks is challenging. A sizable proportion communicates in regional language but using code-mixed and script like Roman and Devnagari for English and Marathi language. These texts are generally informal, causal, short length, non-standard spelling alteration etc are prime challenges in language processing. Language identification in mixed text is challenging, since the Romanized string of several languages is comparable. Mixed text is essential to transform into native script (Devnagari) for further processing like Information Retrieval, machine translation, Question Answering etc. Due to the lack of orthography of Latin script in Marathi, language modelling, and identification of mixed text is a challenging issue. Many NLP (Natural Language Processing) applications ranging from machine translation and information retrieval uses Machine transliteration as input mechanism for non-roman script. In this paper, different techniques and various approaches presented by the researchers for code-mixed language, Indian regional languages processing are discussed. The tasks like language identification, transliteration Named Entity Recognition are reviewed with respect to Statistical, Rule and Neural based approaches. [ABSTRACT FROM AUTHOR]

In recent years, Aspect-based Sentiment Analysis (ABSA) has been a crucial yet challenging task in recognizing textual emotions from text. ABSA has numerous application across various fields, such as social media, commodity review, and movie comment, making it an attractive area of research. Many researchers are working to develop more powerful sentiment analysis models. Currently, most existing ABSA models use the generic pre-trained language models (PLMs) based fine-tuning paradigm, which only utilizes the encoder parameters while discarding the decoder parameters of PLMs. However, this approach fails to leverage the prior knowledge revealed in PLMs effectively. To address these issues, we investigate the potential of the initial pre-training scheme of PLMs to conduct ABSA and thus propose a novel approach in this paper, namely Target Word Transferred ABSA (WordTransABSA). In WordTransABSA, we propose "Word Transferred LM", a novel sequence-level optimization strategy that transferred target words in sentence into pivot tokens to stimulate better PLM semantic understanding capability. Given a sentence with aspect terms as input, WordTransABSA generates contextually appropriate semantics and predicts the affective tokens on the corresponding positions of the aspect terms. The final sentiment polarity of each aspect term is determined through several sentiment identification strategies that we selected. WordTransABSA takes full advantage of the versatile linguistic knowledge of Pre-trained Language Model, resulting in competitive accuracy compared with recent baselines. The WordTransABSA demonstrates its superiority and effectiveness through extensive experiments in both data-sufficient (full-data supervised learning) and data-insufficient (few-shot learning) scenarios. We have made our code publicly available on GitHub: https://github.com/albert-jin/WordTransABSA. • We present WordTransABSA, introducing a "Word Transferred LM" strategy for ABSA. • Various strategies are explored for searching sentiment-related pivot tokens. • Data-sufficient/scarce experiments validate the effectiveness of WordTransABSA. • Stimulating PLM pre-training paradigm is proved to be a better solution for ABSA. [ABSTRACT FROM AUTHOR]