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This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

This study argues that online user comments on social media platforms provide feedback and evaluation functions. These functions can provide services for the relevant departments of organizations or institutions to formulate corresponding public opinion response strategies.

This study considers Chinese universities’ public opinion events on the Weibo platform as the research object. It collects online comments on Chinese universities’ network public opinion governance strategy texts on Weibo, constructs the sentiment index based on sentiment analysis and evaluates the effectiveness of the network public opinion governance strategy adopted by university officials.

This study found the following: First, a complete information release process can effectively improve the effect of public opinion governance strategies. Second, the effect of network public opinion governance strategies was significantly influenced by the type of public opinion event. Finally, the effect of public opinion governance strategies is closely related to the severity of punishment for the subjects involved.

The theoretical contribution of this study lies in the application of image repair theory and strategies in the field of network public opinion governance, which further broadens the scope of the application of image repair theory and strategies.

This study expands online user comment research to network public opinion governance and provides a quantitative method for evaluating the effect of governance strategies.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2022-0269

The use of comment cards is a very popular method for obtaining customer feedback in the hospitality industry. An investigation was made regarding the current status of comment card usage in hotel‐restaurants offering high quality, Sunday champagne buffets, in Las Vegas, Nevada, USA. Cards were assessed with regard to frequency and mode of availability, information content, service quality, card design, and management follow‐up. Although 81 per cent of the buffets made use of comment cards, problems were found with accessibility, comprehensiveness of information, questionnaire design principles and frequency of call‐backs.

With the rapidly improving communication technologies, a growing number of people communicate with each other in online environments. In particular, social networking services (SNSs) are one of the widely used and common places that enable active communication among users. To understand what drives successful online conversations in SNSs, this study aims to explore the roles of posts and first comments in successful online conversations.

To address the purpose, the data of news-related channels in Reddit were collected and analyzed.

The study found that successful conversations tend to have the post and first comment with high scores. Also, the first comments in successful online conversations tend to be easier than those in other conversations.

The results reveal that successful online conversations can be generated not only with empathic posts but also with touching or attractive first comments. In other words, users are likely to participate in an online conversation that starts with an empathic post and first comment. Moreover, both practical and theoretical implications are presented.

The purpose of this paper is to present a practical, low-tech and adaptable methodology for converting large amounts of comment data into actionable, qualitative analysis.

In 2013, the Horace W. Sturgis Library of Kennesaw State University conducted the LibQUAL+ survey, receiving 1,653 open-ended comments. In a home-grown approach to comment analysis, the Sturgis Library targeted specific areas of operational significance (chat, ILL, etc.) to analyze in depth. Keywords and synonyms were identified, comments containing these terms were evaluated and a document known as a mini-report summarized the analysis for each area.

Over the summer of 2013, two librarians created 12 mini-reports. The mini-report format allowed investigators to build up their understanding of the organization incrementally and proved a valuable means of summarizing qualitative analysis and sharing it with various stakeholders.

Not all comments are coded, and therefore, potentially valuable information is lost. By limiting focus to specific operational areas, the methodology does not capture themes that are diffused throughout the organization. Some areas of operational significance are difficult to isolate by keywords.

This paper contributes to the literature on pragmatic qualitative analysis. Many existing models of qualitative analysis are resource intensive, in terms of time, technology and staff training. This paper presents a variation on a methodology that is more adaptable, allowing the researchers to explore within large pools of comment data without having to code and analyze every comment.

This chapter considers the political controversy in Britain over the lifting of restrictions of freedom of movement on European Union (EU) citizens from Bulgaria and Romania in January 2014. The response of the then Conservative-Liberal Democrat Coalition Government centred on altering the rules on the payment of welfare benefits to potential new EU immigrants such that they would not be entitled to claim these benefits for 3 months after entry to the United Kingdom. This policy led to a split in the coalition, with the Liberal Democrat leadership claiming that it was a panicked move by the majority Conservative coalition partner, and moreover that it was a blatant attempt to appeal the electorate in an effort to be seen to be doing something to stop the welfare benefit system from being abused by ‘foreigners’. The backdrop to this political fracas centred on the economic contribution of East European immigrants to Britain and the claim and counterclaim over the issues jobs, welfare benefits and services such as English language support in schools. These contentious issues are examined in terms of an analysis of online comments to posted in reaction to a political interview with Vince Cable, the Liberal Democrat business secretary, who claimed that the Conservatives were attempting to placate public disquiet over immigration as a response to the rising popularity of the United Kingdom Independence Party.

The United States Securities and Exchange Commission (SEC) issued an interpretative release ostensibly mandating the disclosure of the impact that climate change may have on the registrant. One means of enforcement for this release is through the use of comment letters. Prior empirical studies have supported the argument that the SEC oversight through issuing comment letters is effective in enhancing the quality of firms’ disclosures (Asthana & Boone, 2009; Johnston & Petacchi, 2017). With a total of 27 comment letter cases (34 comments based on the topics) regarding climate change disclosure, we do not find clear evidence strongly supporting that the SEC implements its oversight process through systematic procedures and that SEC comment letters enhance the quality of firms’ climate change disclosure. Although some firms responded to the comments proactively, qualitative analysis reveals that the firm’s revisions were not sufficient to provide useful information for market participants in general. The overall finding suggests that the current oversight mechanism for climate change disclosure needs to be significantly improved to enhance the quality of firms’ climate change disclosure.

This study examines the relationship between streamers' product descriptions, customer comments and online sales and focuses on the moderating effect of streamer–viewer relationship strength.

Between June 2021 and April 2022, the structured data of 965 livestreaming and unstructured text data of 42,956,147 characters from two major live-streaming platforms were collected for the study. Text analysis and regression analysis methods were employed for data analysis.

First, the authors' analysis reveals an inverted U-shaped relationship between comment length and product sales. Notably, comment volume and comment emotion positively influence product sales. Furthermore, the semantic richness, emotion and readability of streamers' product descriptions also positively influence product sales. Secondly, the authors find that the strength of streamer–viewer relationship weakens the positive effects of comment volume and comment emotion without moderating the inverted U-shaped effect of comment length. Lastly, the strength of streamer–viewer relationship also diminishes the positive effects of emotion, semantics and readability of streamers' product descriptions on product sales.

This study is the first to concurrently examine the direct and interactive effects of user-generated content (UGC) and marketer-generated content (MGC) on consumer purchase behaviors in livestreaming e-commerce, offering a novel perspective on individual decision-making and cue utilization in the social retail context.

The purpose of this chapter is to shed light on researchers and practitioners about sentiment analysis in hospitality and tourism. The technical details described throughout the chapter with a case study to provide clarifying insights. The proposed chapter adds significantly to the body of text mining knowledge by combining a technical explanation with a relevant case study. The case study used supervised machine learning to predict overall star ratings based on 20,247 comments related to Royal Caribbean International services for determining the impact of cruise travel experiences on the evaluation company process. The results indicate that travelers evaluate their travel experiences according to the most intense negative or positive feelings they have about the company.