A framework to analyze the emotional reactions to mass violent events on Twitter and influential factors

Fake news detection in social media based on sentiment analysis using classifier techniques

Fake news on social media, has spread for personal or societal gain. Detecting fake news is a multi-step procedure that entails analysing the content of the news to assess its trustworthiness. The article has proposed a new solution for fake news detection which incorporates sentiment as an important feature to improve the accuracy with two different data sets of ISOT and LIAR. The key feature words with content's propensity scores of the opinions are developed based on sentiment analysis using a lexicon-based scoring algorithm. Further, the study proposed a multiple imputation strategy which integrated Multiple Imputation Chain Equation (MICE) to handle multivariate missing variables in social media or news data from the collected dataset. Consequently, to extract the effective features from the text, Term Frequency and Inverse Document Frequency (TF-IDF) are introduced to determine the long-term features with the weighted matrix. The correlation of missing data variables and useful data features are classified based on Naïve Bayes, passive-aggressive and Deep Neural Network (DNN) classifiers. The findings of this research described that the overall calculation of the proposed method was obtained with an accuracy of 99.8% for the detection of fake news with the evaluation of various statements such as barely true, half true, true, mostly true and false from the dataset. Finally, the performance of the proposed method is compared with the existing methods in which the proposed method results in better efficiency.

Impact of word embedding models on text analytics in deep learning environment: a review

The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.

Tweets on a horror movie: An investigation into relationships between sentiment strength, cognitive language and tweet virality

This article studies how sentiment strength and cognitive language may influence the levels of tweet virality. A total of 11,381 tweets about a horror movie (‘Mother!’) were collected. Based on the definitions of two independent variables: sentiment strength and cognitive language use, and the dependent variable: tweet virality, the data descriptive statistics and analysis of variance (ANOVA) analysis were applied to reveal the relationships between tweet virality and sentiment and cognitive factors. The results indicate that a high tweet virality is associated with either a lower level of sentiment strength or/and a higher level of cognitive language use by a statistically significant margin. This finding is more evident for negative tweets. The study findings help improve the understanding about sentimental and cognitive factors impacting tweet virality and guide the movie industry to improve marketing movie content to achieve high virality on social media. The conclusions can also be applied to other industries, government agencies, organisations and individuals who intend to quickly disseminate specific information on social media platforms.

Optimism and pessimism analysis using deep learning on COVID-19 related twitter conversations

This paper proposes a new deep learning approach to better understand how optimistic and pessimistic feelings are conveyed in Twitter conversations about COVID-19. A pre-trained transformer embedding is used to extract the semantic features and several network architectures are compared. Model performance is evaluated on two new, publicly available Twitter corpora of crisis-related posts. The best performing pessimism and optimism detection models are based on bidirectional long- and short-term memory networks. Experimental results on four periods of the COVID-19 pandemic show how the proposed approach can model optimism and pessimism in the context of a health crisis. There is a total of 150,503 tweets and 51,319 unique users. Conversations are characterised in terms of emotional signals and shifts to unravel empathy and support mechanisms. Conversations with stronger pessimistic signals denoted little emotional shift (i.e. 62.21% of these conversations experienced almost no change in emotion). In turn, only 10.42% of the conversations laying more on the optimistic side maintained the mood. User emotional volatility is further linked with social influence.

Opinion mining for national security: techniques, domain applications, challenges and research opportunities

BACKGROUND

Opinion mining, or sentiment analysis, is a field in Natural Language Processing (NLP). It extracts people's thoughts, including assessments, attitudes, and emotions toward individuals, topics, and events. The task is technically challenging but incredibly useful. With the explosive growth of the digital platform in cyberspace, such as blogs and social networks, individuals and organisations are increasingly utilising public opinion for their decision-making. In recent years, significant research concerning mining people's sentiments based on text in cyberspace using opinion mining has been explored. Researchers have applied numerous opinions mining techniques, including machine learning and lexicon-based approach to analyse and classify people's sentiments based on a text and discuss the existing gap. Thus, it creates a research opportunity for other researchers to investigate and propose improved methods and new domain applications to fill the gap.

METHODS

In this paper, a structured literature review has been done by considering 122 articles to examine all relevant research accomplished in the field of opinion mining application and the suggested Kansei approach to solve the challenges that occur in mining sentiments based on text in cyberspace. Five different platforms database were systematically searched between 2015 and 2021: ACM (Association for Computing Machinery), IEEE (Advancing Technology for Humanity), SCIENCE DIRECT, SpringerLink, and SCOPUS.

RESULTS

This study analyses various techniques of opinion mining as well as the Kansei approach that will help to enhance techniques in mining people's sentiment and emotion in cyberspace. Most of the study addressed methods including machine learning, lexicon-based approach, hybrid approach, and Kansei approach in mining the sentiment and emotion based on text. The possible societal impacts of the current opinion mining technique, including machine learning and the Kansei approach, along with major trends and challenges, are highlighted.

CONCLUSION

Various applications of opinion mining techniques in mining people's sentiment and emotion according to the objective of the research, used method, dataset, summarized in this study. This study serves as a theoretical analysis of the opinion mining method complemented by the Kansei approach in classifying people's sentiments based on text in cyberspace. Kansei approach can measure people's impressions using artefacts based on senses including sight, feeling and cognition reported precise results for the assessment of human emotion. Therefore, this research suggests that the Kansei approach should be a complementary factor including in the development of a dictionary focusing on emotion in the national security domain. Also, this theoretical analysis will act as a reference to researchers regarding the Kansei approach as one of the techniques to improve hybrid approaches in opinion mining.

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

Sentiment analysis is an emerging trend nowadays to understand people's sentiments in multiple situations in their quotidian life. Social media data would be utilized for the entire process ie the analysis and classification processes and it consists of text data and emoticons, emojis, etc. Many experiments were conducted in the antecedent studies utilizing Binary and Ternary Classification whereas Multi-class Classification gives more precise and precise Classification. In Multi-class Classification, the data would be divided into multiple sub-classes predicated on the polarities. Machine Learning and Deep Learning Techniques would be utilized for the classification process. Utilizing Social media, sentiment levels can be monitored or analysed. This paper shows a review of the sentiment analysis on Social media data for apprehensiveness or dejection detection utilizing various artificial intelligence techniques. In the survey, it was optically canvassed that social media data which consists of texts,emoticons and emojis were utilized for the sentiment identification utilizing various artificial intelligence techniques. Multi Class Classification with Deep Learning Algorithm shows higher precision value during the sentiment analysis.

From General Language Understanding to Noisy Text Comprehension

Obtaining meaning-rich representations of social media inputs, such as Tweets (unstructured and noisy text), from general-purpose pre-trained language models has become challenging, as these inputs typically deviate from mainstream English usage. The proposed research establishes effective methods for improving the comprehension of noisy texts. For this, we propose a new generic methodology to derive a diverse set of sentence vectors combining and extracting various linguistic characteristics from latent representations of multi-layer, pre-trained language models. Further, we clearly establish how BERT, a state-of-the-art pre-trained language model, comprehends the linguistic attributes of Tweets to identify appropriate sentence representations. Five new probing tasks are developed for Tweets, which can serve as benchmark probing tasks to study noisy text comprehension. Experiments are carried out for classification accuracy by deriving the sentence vectors from GloVe-based pre-trained models and Sentence-BERT, and by using different hidden layers from the BERT model. We show that the initial and middle layers of BERT have better capability for capturing the key linguistic characteristics of noisy texts than its latter layers. With complex predictive models, we further show that the sentence vector length has lesser importance to capture linguistic information, and the proposed sentence vectors for noisy texts perform better than the existing state-of-the-art sentence vectors.