Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media

This study presents a data analytics framework that aims to analyze topics and sentiments associated with COVID-19 vaccine misinformation in social media. A total of 40,359 tweets related to COVID-19 vaccination were collected between January 2021 and March 2021. Misinformation was detected using multiple predictive machine learning models. Latent Dirichlet Allocation (LDA) topic model was used to identify dominant topics in COVID-19 vaccine misinformation. Sentiment orientation of misinformation was analyzed using a lexicon-based approach. An independent-samples t-test was performed to compare the number of replies, retweets, and likes of misinformation with different sentiment orientations. Based on the data sample, the results show that COVID-19 vaccine misinformation included 21 major topics. Across all misinformation topics, the average number of replies, retweets, and likes of tweets with negative sentiment was 2.26, 2.68, and 3.29 times higher, respectively, than those with positive sentiment.

The Development of Energy Storage in China: Policy Evolution and Public Attitude

With the challenges posed by the intermittent nature of renewable energy, energy storage technology is the key to effectively utilize renewable energy. China’s energy storage industry has experienced rapid growth in recent years. In order to reveal how China develops the energy storage industry, this study explores the promotion of energy storage from the perspective of policy support and public acceptance. Accordingly, by tracing the evolution of the energy storage policies during 2010–2020 comprehensively, a better understanding of the policy intention and implementation can be obtained. Meanwhile, this paper collects the information of Weibo users and posts related to energy storage by web crawler technology. The status of public attention and sentiment orientation toward energy storage are investigated with a text mining method. The main results are as follows. 1) The evolution of energy storage is characterized by three stages: the foundation stage, the nurturing stage, and the commercialization stage. 2) Most people have a positive attitude towards energy storage and recognize the potential of the energy storage industry, and it is discovered that the public attitudes towards energy storage exist cognitive bias. 3) More policies concerning market mechanism, R&D, and subsidies should be introduced to enhance the effect of energy storage policies and increase public recognition. These findings help to understand the energy storage policy and provide better strategies for policymaking.

Sentiment Weighted Word Embedding for Big Text Data

Sentiment analysis is the practice of eliciting a sentiment orientation of people's opinions (i.e. positive, negative and neutral) toward the specific entity. Word embedding technique like Word2vec is an effective approach to encode text data into real-valued semantic feature vectors. However, it fails to preserve sentiment information that results in performance deterioration for sentiment analysis. Additionally, big sized textual data consisting of large vocabulary and its associated feature vectors demands huge memory and computing power. To overcome these challenges, this research proposed a MapReduce based Sentiment weighted Word2Vec (MSW2V), which learns the sentiment and semantic feature vectors using sentiment dictionary and big textual data in a distributed MapReduce environment, where memory and computing power of multiple computing nodes are integrated to accomplish the huge resource demand. Experimental results demonstrate the outperforming performance of the MSW2V compared to the existing distributed and non-distributed approaches.

Weibo users’ emotion and sentiment orientation in Traditional Chinese Medicine (TCM) during the COVID-19 pandemic

Objective: This study aimed to explore Chinese people’s attitudes to the official application of TCM in COVID-19 treatment. Methods: We collected data referring to TCM on Weibo from 0:00 on January 24th, 2020, to 23:59:59 on March 31st, 2020 (Beijing time). Besides, this paper utilized DLUT- Emotion ontology to analyze the sentiment orientation and emotions of selected data and then conducted a text analysis. Results: According to DLUT-Emotion ontology, we examined three sentiment orientations of 215,565 valid Weibo posts. Among them,25,025 posts were judged as positive emotions, accounting for approximately 12%; 22,362 were regarded as negative emotions, accounting for about 10%; and 168,178 were judged as neutral emotions, accounting for approximately 78%. Results indicate that the words judged as ‘Good’ have the highest frequency, and words marked as ‘Happy’ have increased over time. The word frequency of ‘Fear’ and ‘Sadness’ showed a significant downward trend. Conclusion: Weibo users have a relatively positive attitude to the TCM in the COVID-19 treatment in general. Results of text analysis show that data with negative emotions is essentially an expression of public opinions to supporting TCM or not. Texts of ‘Fear’ and ‘Sadness’ do not reflect users’ negative attitudes to TCM.

Tourist Sentiment Mining Based on Deep Learning

Mining the sentiment of the user on the internet via the context plays a significant role in uncovering the human emotion and in determining the exactness of the underlying emotion in the context. An increasingly enormous number of user-generated content (UGC) in social media and online travel platforms lead to development of data-driven sentiment analysis (SA), and most extant SA in the domain of tourism is conducted using document-based SA (DBSA). However, DBSA cannot be used to examine what specific aspects need to be improved or disclose the unknown dimensions that affect the overall sentiment like aspect-based SA (ABSA). ABSA requires accurate identification of the aspects and sentiment orientation in the UGC. In this book chapter, we illustrate the contribution of data mining based on deep learning in sentiment and emotion detection.

The Influence of Sentiment Orientation in Open Innovation Communities: Empirical Evidence from a Business Analytics Community

This study aims to investigate the influence of presentation and sentiment orientation of user-generated ideas and reviews on idea adoption in open innovation communities (OICs). Drawing on the social influence theory, this study develops a research model that divides idea components into informational and normative determinants. The sentiment orientation of the idea title, description, and associated reviews is determined using a lexicon-based sentiment analysis approach. The research model is empirically tested using logistic regression analysis based on a dataset from the Microsoft community for business analytics products. The results reveal that the sentiment orientation of idea title has a negative influence on idea adoption, whilst the sentiment orientation of description has no influence on idea adoption. The sentiment orientation of the associated reviews has a positive influence on idea adoption, and this influence is moderated by the number of reviews. In addition, both idea title length and description length have a positive influence on idea adoption. These results offer several theoretical and practical implications and should therefore contribute to a better understanding of how user-generated ideas can be leveraged to drive innovation development and sustainability in OICs.

Context-Based News Headlines Analysis: A Comparative Study of Machine Learning and Deep Learning Algorithms

Online news blogs and websites are becoming influential to any society as they accumulate the world in one place. Aside from that, online news blogs and websites have efficient strategies in grabbing readers’ attention by the headlines, that being so to recognize the sentiment orientation or polarity of the news headlines for avoiding misinterpretation against any fact. In this study, we have examined 3383 news headlines created by five different global newspapers. In the interest of distinguishing the sentiment polarity (or sentiment orientation) of news headlines, we have trained our model by seven machine learning and two deep learning algorithms. Finally, their performance was compared. Among them, Bernoulli naïve Bayes and Convolutional Neural Network (CNN) achieved higher accuracy than other machine learning and deep learning algorithms, respectively. Such a study will help the audience in determining their impression against or for any leader or governance; and will provide assistance to recognize the most indifferent newspaper or news blogs.

Exploring BERT for Aspect Extraction in Portuguese Language

Sentiment Analysis is the computer science field that comprises techniques that aim to automatically extract opinions from texts. Usually, these techniques assign a Sentiment Orientation to the whole document (Document Level Sentiment Analysis). But a document can express sentiment about several aspects of an entity. Methods that extract those aspects, paired with the sentiment about them, operate in the Aspect Level. Aspect-Based Sentiment Analysis approaches can be split into two stages: Aspect Extraction and Aspect Sentiment Classification. The literature presents works mainly focused on reviews about hotels, smartphones, or restaurants. In this work, we present an approach for Aspect Extraction based on Multilingual (Google's) and Portuguese (BERTimbau) BERT pre-trained models. Our experiments show that Aspect Extraction based on BERT pre-trained for Portuguese achieved Balanced Accuracy of up to 93% on a corpus of reviews about the accommodation sector.

Sentiment analysis of comments in social media

Social media platforms are witnessing a significant growth in both size and purpose. One specific aspect of social media platforms is sentiment analysis, by which insights into the emotions and feelings of a person can be inferred from their posted text. Research related to sentiment analysis is acquiring substantial interest as it is a promising filed that can improve user experience and provide countless personalized services. Twitter is one of the most popular social media platforms, it has users from different regions with a variety of cultures and languages. It can thus provide valuable information for a diverse and large amount of data to be used to improve decision making. In this paper, the sentiment orientation of the textual features and emoji-based components is studied targeting “Tweets” and comments posted in Arabic on Twitter, during the 2018 world cup event. This study also measures the significance of analyzing texts including or excluding emojis. The data is obtained from thousands of extracted tweets, to find the results of sentiment analysis for texts and emojis separately. Results show that emojis support the sentiment orientation of the texts and that texts or emojis cannot separately provide reliable information as they complement each other to give the intended meaning.

Anticipating Corporate Financial Performance from CEO Letters Utilizing Sentiment Analysis

With the emergence and tremendous growth of text mining, a computer-assisted approach for capturing sentiment viewpoints from textual data is gradually becoming a promising field, particularly when researchers are increasingly facing the problem of filtering bunches of useless information without capturing the essence in the big data era. This study aims at observing and classifying the sentiment orientation in CEO letters, digging the main corporate social responsibility (CSR) themes, and examining the effectiveness of CEO letters’ sentiment on forecasting financial performance. A specific sentiment dictionary has been proposed to identify and classify the sentiment orientation in CEO letters by utilizing the appraisal theory. Additionally, the qualitative data analysis software NVivo is applied to explore the CSR topics. Furthermore, a modified Altman’s Z-score model and machine-learning approach are employed to predict financial performance. The results of preliminary evaluations validate that approximately 62.14% of the texts represent positive polarity even when companies are not in a promising economic situation. The CSR themes mainly focus on business ethical responsibility, particularly ethical activities. Among various machine-learning approaches, the logistic regression approach is appropriate for predicting financial performance with the state-of-the-art accuracy of 70.46 %. The encouraging results indicate that the sentiment information inCEO letters is a vital factor for anticipating financial performance. This work not only offers a new analytic framework for associating linguistic theory with computer science and economic models but will also improve stakeholders’ decision-making.