Research on the Characteristics and Usefulness of User Reviews of Online Mental Health Consultation Services: A Content Analysis

Online consultation based on Internet technology is gradually becoming the main way to seek health information and professional assistance. Online user reviews, such as content reviews and star ratings, are an important basis for reflecting users’ views on the effectiveness of health services. Here, we used user reviews related to online psychological consultation services for content feature mining and usefulness analyses. We used a professional online psychological counseling service platform in China to collect user reviews that were liked by users as a data sample for a content analysis. An LDA topic model, dictionary-based sentiment analysis, and the NRC Word-Emotion Association Lexicon were used to extract the topic, sentiment, and context features of the content of 4254 useful reviews, and the influence of these features on the usefulness of the reviews was verified by a multiple linear regression analysis. Our results show that the content of online reviews by psychological counseling users presented a positive emotional attitude as a whole and expressed more views on the process, effects, and future expectations of counseling than on other topics. There was a significant correlation between the topic, sentiment, and context features of a user review and its usefulness: reviews giving high scores and containing topics such as “ease emotions” and “consulting expectations” received more user likes. However, the usefulness of a review was significantly reduced if it was in existence for too long. This research provides valuable suggestions for understanding the needs and emotional attitudes of users with mental health problems in terms of online psychological consultation; identifying the factors that affect the number of likes a review receives can help platform users write better consultation evaluations and thereby provide greater usefulness. In addition, the use of online reviews generated by users for content analysis effectively supplements the current research on online psychological counseling in terms of data and methods.

Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling Approach

Background The COVID-19 pandemic is severely affecting people worldwide. Currently, an important approach to understand this phenomenon and its impact on the lives of people consists of monitoring social networks and news on the internet. Objective The purpose of this study is to present a methodology to capture the main subjects and themes under discussion in news media and social media and to apply this methodology to analyze the impact of the COVID-19 pandemic in Brazil. Methods This work proposes a methodology based on topic modeling, namely entity recognition, and sentiment analysis of texts to compare Twitter posts and news, followed by visualization of the evolution and impact of the COVID-19 pandemic. We focused our analysis on Brazil, an important epicenter of the pandemic; therefore, we faced the challenge of addressing Brazilian Portuguese texts. Results In this work, we collected and analyzed 18,413 articles from news media and 1,597,934 tweets posted by 1,299,084 users in Brazil. The results show that the proposed methodology improved the topic sentiment analysis over time, enabling better monitoring of internet media. Additionally, with this tool, we extracted some interesting insights about the evolution of the COVID-19 pandemic in Brazil. For instance, we found that Twitter presented similar topic coverage to news media; the main entities were similar, but they differed in theme distribution and entity diversity. Moreover, some aspects represented negative sentiment toward political themes in both media, and a high incidence of mentions of a specific drug denoted high political polarization during the pandemic. Conclusions This study identified the main themes under discussion in both news and social media and how their sentiments evolved over time. It is possible to understand the major concerns of the public during the pandemic, and all the obtained information is thus useful for decision-making by authorities.

An Analysis of Online Classes Tweets Using Gephi: Inputs for Online Learning

The conduct of online classes has emerged as one of the major changes in the educational landscape at the onset of COVID-19. Its implementation has been met by varying reactions that have become evident in social media, particularly on Twitter. This paper analyzed #onlineclasses tweets of Filipino users using network analysis through Gephi and NodeXL software. The resulting network has 2,278 users and 998 interactions with many groups of small interactions among users, and low clustering coefficient and modularity values. The users in the top 8 communities in the network talk about the challenges brought about by online classes and the opportunities that online networks offer. Hence, the network of #OnlineClasses tweets can be described as a community cluster. Smaller groups of users who engaged in aspects of online classes emerge in the network, signifying that Filipinos have differing points of view about the topic. Sentiment sharing through social networks provides an avenue for sharing challenges and building communities that help address challenges for online learning in the pandemic.

IMPLEMENTATION OF BACKPROPAGATION NEURAL NETWORK IN SENTIMENT ANALYSIS ON TWITTER TO PUBLIC FIGURES

Sentiment analysis is a process for identifying or analyzing people's opinions on a topic. Sentiment analysis analyzes each word in a sentence to find out the opinions or sentiments expressed in the sentence. The opinions expressed can be in the form of positive or negative opinions. Twitter is one of the most popular social media in Indonesia. Twitter users always discuss various kinds of topics every day. One of the things discussed on Twitter and which has become a trending topic several times is about public figures. This study discusses the analysis of positive or negative sentiments towards public figures based on tweet data carried out by text processing. The results of text processing are classified using a backpropagation neural network. Tests were carried out using 69 test data, resulting in an accuracy of 62.3%, with 43 correct classification results.

Comparing News and Tweets about COVID-19 in Brazil (Preprint)

BACKGROUND COVID-19 pandemic is severely affecting people all over the world. Nowadays, an important approach to understand such a phenomenon and its impacts on the lives of people consists of monitoring social networks and news on Internet. OBJECTIVE COVID-19 pandemic is severely affecting people all over the world. Nowadays, an important approach to understand such a phenomenon and its impacts on the lives of people consists of monitoring social networks and news on Internet. METHODS This work proposes a methodology based on topic modeling, named entity recognition and sentiment analysis of the text to compare Twitter posts and news, followed by envision of COVID evolution and impacts. We have focused on an analysis in Brazil, one important epicenter of the pandemic in the world, so we have faced the challenge to deal with Brazilian Portuguese texts. RESULTS This work collected and analysed 18,413 articles from news media, and 1,597,934 tweets posted by 1,299,084 users in Brazil. Results show that the proposed methodology improved the topic-sentiment analysis over time, so a better monitoring of Internet media is allowed. Besides, with this tool, we extracted some interesting insights about COVID evolution in Brazil. For instance, we found out that Twitter presents similar topic coverage from news media, the main entities are similar, but they differ in theme distribution and entity diversity. Besides, some aspects represent a negative sentiment of political theme from both media, and a high incidence of mentions to a specific drug denotes a high political polarization of the pandemic. CONCLUSIONS This work collected and analysed 18,413 articles from news media, and 1,597,934 tweets posted by 1,299,084 users in Brazil. Results show that the proposed methodology improved the topic-sentiment analysis over time, so a better monitoring of Internet media is allowed. Besides, with this tool, we extracted some interesting insights about COVID evolution in Brazil. For instance, we found out that Twitter presents similar topic coverage from news media, the main entities are similar, but they differ in theme distribution and entity diversity. Besides, some aspects represent a negative sentiment of political theme from both media, and a high incidence of mentions to a specific drug denotes a high political polarization of the pandemic.

Discussing God: The Effect of (Ir)Religious Identities on Topic-Sentiment Polarization in Online Debates

Amidst growing societal tensions, social media platforms become hubs of heated intergroup exchanges. According to social identity theory, group membership and the value we assign to it drive the expression of intergroup bias. Within the blooming scholarship on social and political polarization online, little attention has been paid to interreligious deliberations, despite the well-established relationship between religion and intergroup grievances. The present studies are designed to address the void in the scholarship of social identity and online religion by examining how religious identities, or the lack thereof, affect intergroup biases in the form of identity-specific topic preferences and topic-sentiment polarization. Drawing from social identity theory, five hypotheses were tested. The data for the study, a product of a natural experiment, are YouTube commentary sections featuring videos on two cases of interreligious debates between (1) Christian and Muslim or (2) Christian and atheist speakers. Using topic-sentiment analysis, a multistage method of topic modeling with latent semantic analysis and sentiment analysis, 24,179 comments, for the Christian–Muslim debates, and 52,607 comments, for the Christian–atheist debates, were analyzed. The results demonstrate normative content and identity-specific instances of topic-sentiment polarization. In terms of content, Christian–Muslim and Christian–atheist discussions are nearly completely preoccupied with theological or intellectual concepts. While interreligious polarization is robust in both debates, it appears more normative among Christians–Muslims and deeper among Christians–atheists, possibly indicating the higher stakes in the battle for moral authority. Interreligious debates on YouTube serve to uplift and defend the in-group and to delegitimize the outgroup in a broader battle for moral authority. Regardless of group affiliation, these debaters were concerned with ‘big picture’ questions of meaning and how best to address them. Stereotyping and cultural altercations appear mostly as a reaction to challenged identity characteristics, suggesting that issue-based social differences and cultural incompatibilities, often emphasized in self-report research, may be evoked as rationalizations of interreligious prejudice. Last, the successful application of topic-sentiment analysis lends support for the more systematic utilization of this method.

Topic Sentiment Analysis in Online Learning Community from College Students

PurposeOpinion mining and sentiment analysis in Online Learning Community can truly reflect the students’ learning situation, which provides the necessary theoretical basis for following revision of teaching plans. To improve the accuracy of topic-sentiment analysis, a novel model for topic sentiment analysis is proposed that outperforms other state-of-art models.Methodology/approachWe aim at highlighting the identification and visualization of topic sentiment based on learning topic mining and sentiment clustering at various granularity-levels. The proposed method comprised data preprocessing, topic detection, sentiment analysis, and visualization.FindingsThe proposed model can effectively perceive students’ sentiment tendencies on different topics, which provides powerful practical reference for improving the quality of information services in teaching practice.Research limitationsThe model obtains the topic-terminology hybrid matrix and the document-topic hybrid matrix by selecting the real user’s comment information on the basis of LDA topic detection approach, without considering the intensity of students’ sentiments and their evolutionary trends.Practical implicationsThe implication and association rules to visualize the negative sentiment in comments or reviews enable teachers and administrators to access a certain plaint, which can be utilized as a reference for enhancing the accuracy of learning content recommendation, and evaluating the quality of their services.Originality/valueThe topic-sentiment analysis model can clarify the hierarchical dependencies between different topics, which lay the foundation for improving the accuracy of teaching content recommendation and optimizing the knowledge coherence of related courses.