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With the fast growth of online content and social media platforms, the amount of user- generated data available in the form of product evaluations, social media postings, and online debates has rapidly expanded. It has become essential for organizations to comprehend client attitudes, make wise decisions, and improve their goods and services by analysing the sentiment indicated in this data. This paper aims to develop a sentiment analysis system for product reviews using URLs of particular item as data sources. It utilizes web scraping techniques to collect textual information from web pages. Natural Language Processing (NLP) techniques are applied for data pre-processing, including tokenization and stop word removal. Machine learning models like Support Vector Classifier (SVC), Random Forest, KNN, Logistic Regression, Naive Bayes and Deep learning models like LSTM and GRU are used for sentiment analysis. Performance measures such as recall, accuracy, and precision are employed to evaluate the models. High scores were consistently achieved by the LSTM and GRU models across all measures, demonstrating their potency in successfully capturing sequential relationships. With strong F-1 ratings of 91% and 90%, respectively, Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) regularly beat other models, demonstrating their outstanding precision-recall balance. Conversely, K-Nearest Neighbor (KNN) presents a significant trade-off, offering a blazing-fast training time of 0.01 seconds but demanding an extensive 102.34 seconds for prediction, resulting in a lower F-1 score of 47%. Support Vector Machine (SVM) and Logistic Regression achieve competitive F-1 scores of 85% and 84%, respectively, with relatively slow training and prediction times, striking a balance between performance and efficiency. Naive Bayes and Random Forest, while respectable in accuracy, fall slightly behind with F-1 scores of 71% and 80%, respectively, highlighting the significance of taking into account both performance and computing efficiency when choosing the best approach. Through this research, valuable insights are gained into the effectiveness and accuracy of different algorithms in sentiment analysis. The findings contribute to the understanding of sentiment analysis techniques and offer guidance for selecting the most suitable algorithm for URL-based sentiment analysis applications. [ABSTRACT FROM AUTHOR]

Suicidal ideation detection from social media is an evolving research with great challenges. Many of the people who have the tendency to suicide share their thoughts and opinions through social media platforms. As part of many researches it is observed that the publicly available posts from social media contain valuable criteria to effectively detect individuals with suicidal thoughts. The most difficult part to prevent suicide is to detect and understand the complex risk factors and warning signs that may lead to suicide. This can be achieved by identifying the sudden changes in a user's behavior automatically. Natural language processing techniques can be used to collect behavioral and textual features from social media interactions and these features can be passed to a specially designed framework to detect anomalies in human interactions that are indicators of suicidal intentions. We can achieve fast detection of suicidal ideation using deep learning and/or machine learning based classification approaches. For such a purpose, we can employ the combination of LSTM and CNN models to detect such emotions from posts of the users. In order to improve the accuracy, some approaches like using more data for training, using attention model to improve the efficiency of existing models etc. could be done. This paper proposes a LSTM-Attention-CNN combined model to analyze social media submissions to detect any underlying suicidal intentions. During evaluations, the proposed model demonstrated an accuracy of 90.3% and an F1-score of 92.6%, which is greater than the baseline models. [ABSTRACT FROM AUTHOR]

Regret is a universal emotion that arises from sadness, disappointment, or remorse about something that has occurred or that one has done or failed to do in the past. It typically involves wishing that a different decision had been made or that a different action had been taken. Although regret has been studied in various contexts such as psychology, neuroscience, and philosophy, its expression and analysis on social media using natural language processing techniques is a relatively recent research topic that warrants further investigation. In this paper, we present a study of regret and its expression on social media platforms. Specifically, we present a novel dataset of Reddit texts that have been classified into three classes: Regret by Action, Regret by Inaction, and No Regret. We then use this dataset to investigate the language used to express regret on Reddit and to identify the domains of text that are most commonly associated with regret. Our findings show that Reddit users are most likely to express regret for past actions, particularly in the domain of relationships. We also found that deep learning models using GloVe embedding outperformed other models in all experiments, indicating the effectiveness of GloVe for representing the meaning and context of words in the domain of regret. Overall, our study provides valuable insights into the nature and prevalence of regret on social media, as well as the potential of deep learning and word embeddings for analyzing and understanding emotional language in online text. These findings have implications for the development of natural language processing algorithms and the design of social media platforms that support emotional expression and communication. • Regret is an emotion that has a significant impact on behavior and decision-making. • Regret is often triggered by actions or inactions in the past. • Reddit's users often regret their actions about relationships in the past. • Effectiveness of GloVe in representing the meaning and context of words. [ABSTRACT FROM AUTHOR]

Abstract Recently, the vast use of social media and the high availability of internet access have produced a considerably different textual data from the formal and standard data on the Web. This includes various Arabic dialectal languages, which are the native spoken languages of Arabic speakers. The presence of textual Arabic dialectal languages on the Web has brought many new opportunities as well as challenges for machine learning and Arabic language processing. The identification of this type of informal data has its crucial effect on several applications such as sentiment analysis and machine translation. However, the standard NLP tools developed for traditional data fall short due to nature of dialectal textual data. Deep learning tools have proven to be very effective in processing social Media dialectal text. In this paper, we consider a variety of deep learning models for the automatic classification of Arabic dialectal text. We use a free large manually-annotated dataset known as Arabic Online Commentary (AOC), which includes several Dialectal Arabic (DA) along with the Modern Standard Arabic (MSA), [3]. We consider the most frequent dialects in the dataset. Namely, the Egyptian (EGP), Levantine (LEV), and Gulf –including Iraqi - (GLF). Four different deep neural network models have been implemented to examine the Arabic dialectal classification problem for each pair of the 3 dialects (binary classification experiments) as well as one ternary-classification experiment including all dialects together. The results show a varying but promising performance of the models for each pair of dialects. Furthermore, a closer examination on the manually-annotated AOC dataset has been carried out and hence, we conclude that there is a serious demand for a thorough refinement and review of the AOC annotated sentences as it is an important benchmark dataset in the field. [ABSTRACT FROM AUTHOR]

• Three datasets are merged to build a larger, more diversified spam dataset. • Data balancing is done using SmoteTomek and NearMiss data resampling techniques. • Proposed Hard and Soft voting ensemble models for performance improvement. • Several DL approaches are experimented with GloVe and FastText word embeddings. • Proposed FCB-SA hybrid DL approach outperformed other DL approaches. Cybercriminals use social media platforms to disseminate spam, misleading facts, fake news, and malicious links. Blocking such deceptive social media spam is essential. However, extracting relevant features from social networks is challenging due to privacy and time constraints. Traditional frequency-based word representation techniques are time-consuming and inefficient in producing contextual word vectors. Word embeddings and deep learning models have recently shown good results in text classification. Also, most existing approaches assumed balanced class distribution, which is false for most real-world datasets. In this paper, an attempt is made to advance the performance of the social spam detection system by leveraging dataset balancing, advanced word embedding techniques, machine learning, and deep learning approaches with the self-attention mechanism. In the proposed framework, the datasets are balanced using NearMiss and SmoteTomek techniques to feed several machine-learning models. Later, the baseline ML models and proposed voting-based ensemble models are evaluated on imbalanced and balanced datasets. For the proposed deep learning-based hybrid approaches, embeddings are generated using GloVe and FastText word embeddings on the balanced combined dataset and passed into the deep neural network comprised of Conv1D and Bi-directional recurrent neural network layers with the self-attention mechanism for improved context understanding and effective results. This study examines hybrid approaches for detecting social spam using imbalanced social network data and picks the optimum combination. Besides, Machine learning ensembles, word embeddings, deep learning with hyper-parameter optimization, and a self-attention method are compared thoroughly. Experiments and comparisons with other techniques show that the proposed hybrid framework with deep learning-based approaches achieves better performance. [ABSTRACT FROM AUTHOR]

As the prevalence of social media on the Internet, opinion mining has become an essential approach to analyzing so many data. Various applications appear in a wide range of industrial domains. Meanwhile, opinions have diverse expressions which bring along research challenges. Both of the practical demands and research challenges make opinion mining an active research area in recent years. In this paper, we present a review of Natural Language Processing (NLP) techniques for opinion mining. First, we introduce general NLP techniques which are required for text preprocessing. Second, we investigate the approaches of opinion mining for different levels and situations. Then we introduce comparative opinion mining and deep learning approaches for opinion mining. Opinion summarization and advanced topics are introduced later. Finally, we discuss some challenges and open problems related to opinion mining. [ABSTRACT FROM AUTHOR]

Undoubtedly, social media, such as Facebook and Twitter, constitute a major part of our everyday life due to the incredible possibilities they offer to their users. However, Twitter and generally online social networks (OSNs) are increasingly used by automated accounts, widely known as bots, due to their immense popularity across a wide range of user categories. Their main purpose is the dissemination of fake news, the promotion of specific ideas and products, the manipulation of the stock market and even the diffusion of sexually explicit material. Therefore, the early detection of bots in social media is quite essential. In this paper, two methods are introduced targeting this that are mainly based on Natural Language Processing (NLP) to distinguish legitimate users from bots. In the first method, a feature extraction approach is proposed for identifying accounts posting automated messages. After applying feature selection techniques and dealing with imbalanced datasets, the subset of features selected is fed in machine learning algorithms. In the second method, a deep learning architecture is proposed to identify whether tweets have been posted by real users or generated by bots. To the best of the authors' knowledge, there is no prior work on using an attention mechanism for identifying bots. The introduced approaches have been evaluated over a series of experiments using two large real Twitter datasets and demonstrate valuable advantages over other existing techniques targeting the identification of malicious users in social media. [ABSTRACT FROM AUTHOR]