Exploration of social media for sentiment analysis using deep learning

Analysis of COVID-19-Related User Content on the Baseball Bulletin Board in 2020 through Text Mining

The world engaged in online sport watching during COVID-19. Fortunately, in Taiwan, the pandemic was stably controlled in 2020, allowing for the continuation of the Chinese Professional Baseball League (CPBL); this attracted international attention and encouraged relevant discussions on social media in Taiwan. In the present study, through text mining, we analyzed user content (e.g., the concepts of sports service quality and social identity) on the Professional Technology Temple (PTT) baseball board-the largest online bulletin board system in Taiwan. A predictive model was constructed to assess PTT users' COVID-19-related comments in 2020. A total of 422 articles and 21,167 comments were retrieved. PTT users interacted more frequently during the closed-door period, particularly during the beginning of the CPBL in April. Effective pandemic prevention, which garnered global attention to the league, generated a sense of national identity among the users, which was strengthened with the development of peripheral products, such as English broadcasting and live broadcasting on Twitch. We used machine learning to develop a chatbot for predicting the attributes of users' comments; this chatbot may improve CPBL teams' understanding of public opinion trends. Our findings may help stakeholders develop tailored programs for online spectators of sports during pandemic situations.

Improved multi-lingual sentiment analysis and recognition using deep learning

Speech emotion recognition (SER) is still a fresh in natural language processing domain since the accuracy is beyond targeted. Mainly due to real-time applications such as human–robot interaction, human behaviour evaluation and virtual reality rely heavily on SER. Moreover, cross-lingual SER plays a significant role in practical applications, especially when users of different cultural and linguistic backgrounds interact with the system. However, the existing conventional approaches of SER cannot be employed for real-world applications because it uses the same corpus for training and testing, which cannot be used for multi-lingual environments to detect or classify real emotions. In such a situation, the performance of SER is degraded. Therefore, the proposed work develops cross-lingual emotion recognition through Urdu, Italian, English and German. The features are extracted through the most employed audio feature known as MFCCs (Mel Frequency Cepstral Coefficients). Experimental results exhibited that the proposed deep learning model comes out with promising results on the URDU data set with 91.25% accuracy using random forest (RF) and XGBoost classifier.

Synergy Masks of Domain Attribute Model DaBERT: Emotional Tracking on Time-Varying Virtual Space Communication

Emotional tracking on time-varying virtual space communication aims to identify sentiments and opinions expressed in a piece of user-generated content. However, the existing research mainly focuses on the user's single post, despite the fact that social network data are sequential. In this article, we propose a sentiment analysis model based on time series prediction in order to understand and master the chronological evolution of the user's point of view. Specifically, with the help of a domain-knowledge-enhanced pre-trained encoder, the model embeds tokens for each moment in the text sequence. We then propose an attention-based temporal prediction model to extract rich timing information from historical posting records, which improves the prediction of the user's current state and personalizes the analysis of user's sentiment changes in social networks. The experiments show that the proposed model improves on four kinds of sentiment tasks and significantly outperforms the strong baseline.

Organizational Adoption of Sentiment Analytics in Social Media Networks

Enterprise adoption and application of sentiment analytics (SA) has recently attracted significant interest from both academia and industry, as it offers exciting opportunities to generate competitive intelligence on consumer attitudes and opinions. Yet, there is limited understanding of the factors underlying successful and widespread adoption of SA in enterprises. This study presents a systematic literature review (SLR) to analyze and summarize previous research on corporate adoption of SA in social media. The SLR examines the results of 83 studies and focuses on tasks, techniques, application domains, and factors that influence enterprise adoption of SA. The findings provide insights into (i) key factors influencing SA adoption, (ii) research trends and paradigms across disciplines, and (iii) potential areas for future research on enterprise adoption of SA. These findings recommend actionable future research agendas for scholars and inform practitioners' understanding of the decision-making processes involved in enterprise adoption of SA in social media.

An investigation into the deep learning approach in sentimental analysis using graph-based theories

Sentiment analysis is a branch of natural language analytics that aims to correlate what is expressed which comes normally within unstructured format with what is believed and learnt. Several attempts have tried to address this gap (i.e., Naive Bayes, RNN, LSTM, word embedding, etc.), even though the deep learning models achieved high performance, their generative process remains a "black-box" and not fully disclosed due to the high dimensional feature and the non-deterministic weights assignment. Meanwhile, graphs are becoming more popular when modeling complex systems while being traceable and understood. Here, we reveal that a good trade-off transparency and efficiency could be achieved with a Deep Neural Network by exploring the Credit Assignment Paths theory. To this end, we propose a novel algorithm which alleviates the features' extraction mechanism and attributes an importance level of selected neurons by applying a deterministic edge/node embeddings with attention scores on the input unit and backward path respectively. We experiment on the Twitter Health News dataset were the model has been extended to approach different approximations (tweet/aspect and tweets' source levels, frequency, polarity/subjectivity), it was also transparent and traceable. Moreover, results of comparing with four recent models on same data corpus for tweets analysis showed a rapid convergence with an overall accuracy of ≈83% and 94% of correctly identified true positive sentiments. Therefore, weights can be ideally assigned to specific active features by following the proposed method. As opposite to other compared works, the inferred features are conditioned through the users' preferences (i.e., frequency degree) and via the activation's derivatives (i.e., reject feature if not scored). Future direction will address the inductive aspect of graph embeddings to include dynamic graph structures and expand the model resiliency by considering other datasets like SemEval task7, covid-19 tweets, etc.

Sentiment Classification of News Text Data Using Intelligent Model

Text sentiment classification is a fundamental sub-area in natural language processing. The sentiment classification algorithm is highly domain-dependent. For example, the phrase “traffic jam” expresses negative sentiment in the sentence “I was stuck in a traffic jam on the elevated for 2 h.” But in the domain of transportation, the phrase “traffic jam” in the sentence “Bread and water are essential terms in traffic jams” is without any sentiment. The most common method is to use the domain-specific data samples to classify the text in this domain. However, text sentiment analysis based on machine learning relies on sufficient labeled training data. Aiming at the problem of sentiment classification of news text data with insufficient label news data and the domain adaptation of text sentiment classifiers, an intelligent model, i.e., transfer learning discriminative dictionary learning algorithm (TLDDL) is proposed for cross-domain text sentiment classification. Based on the framework of dictionary learning, the samples from the different domains are projected into a subspace, and a domain-invariant dictionary is built to connect two different domains. To improve the discriminative performance of the proposed algorithm, the discrimination information preserved term and principal component analysis (PCA) term are combined into the objective function. The experiments are performed on three public text datasets. The experimental results show that the proposed algorithm improves the sentiment classification performance of texts in the target domain.

A Hybrid CNN-LSTM: A Deep Learning Approach for Consumer Sentiment Analysis Using Qualitative User-Generated Contents

With the fastest growth of information and communication technology (ICT), the availability of web content on social media platforms is increasing day by day. Sentiment analysis from online reviews drawing researchers’ attention from various organizations such as academics, government, and private industries. Sentiment analysis has been a hot research topic in Machine Learning (ML) and Natural Language Processing (NLP). Currently, Deep Learning (DL) techniques are implemented in sentiment analysis to get excellent results. This study proposed a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for sentiment analysis. Our proposed model is being applied with dropout, max pooling, and batch normalization to get results. Experimental analysis carried out on Airlinequality and Twitter airline sentiment datasets. We employed the Keras word embedding approach, which converts texts into vectors of numeric values, where similar words have small vector distances between them. We calculated various parameters, such as accuracy, precision, recall, and F1-measure, to measure the model’s performance. These parameters for the proposed model are better than the classical ML models in sentiment analysis. Our results analysis demonstrates that the proposed model outperforms with 91.3% accuracy in sentiment analysis.