Background: Electronic cigarettes (e-cigarettes) have been a newsworthy topic in China. E-cigarettes are receiving greater consumer attention due to the rise of the Chinese e-cigarettes industry. In the past decade, e-cigarettes have been widely debated across the media, particularly their identity and their health effects. Objective: this study aims to (1) find the key topics in e-cigarette news and (2) provide suggestions for future media strategies to improve health communication. Method: We collected Chinese e-cigarettes news from 1 November 2015 to 31 October 2020, in the Huike (WiseSearch) database, using “e-cigarettes” (Chinese: “电子烟”) as the keyword. We used the Jieba package in python to perform the data cleaning process and the Dirichlet allocation (LDA) topic modeling method to generate major themes of the health communication through news content. Main finding: through an analysis of 1584 news articles on e-cigarettes, this paper finds 26 topics covered with 4 themes as regulations and control (n = 475, 30%), minor protection (n = 436, 27.5%), industry activities (n = 404, 25.5%), and health effects (n = 269, 17%). The peak and decline of the number of news articles are affected by time and related regulations. Conclusion: the main themes of Chinese news content on e-cigarettes are regulations and control, and minor protection. Newspapers should shoulder the responsibilities and play an important role in health communication with balanced coverage.

Conducting emotion analysis and generating users' feedback from social media platforms may help understand their emotional responses to video products, such as a documentary on the lockdown of Wuhan during COVID-19. The results of emotion analysis could be used to make further user recommendations for marketing purposes. In our study, we try to understand how users respond to a documentary through YouTube comments. We chose “The lockdown: One month in Wuhan” YouTube documentary, and applied emotion analysis as well as a machine learning approach to the comments. We first cleaned the data and then introduced an emotion analysis based on the statistical characteristics and lexicon combination. After that, we applied the Latent Dirichlet Allocation (LDA) topic modeling approach to further generate main topics with keywords from the comments and visualized the distribution by visualizing the topics. The result shows trust (22.8%), joy (15.4%), and anticipation (17.6%) are the most prominent emotions dominating the comments. The major three themes, which account for 70% of all comments, are discussing stories about fighting against the virus, medical workers being heroes, and medical workers being respected. Further discussion has been conducted on the changing of different sentiments over time for the ongoing health crisis. This study proves that emotion analysis and LDA topic modeling could be used to generate explanations of users' opinions and feelings about video products, which could support user recommendations in marketing.

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Data mining plays an important role in getting insights from news text. This study collected 695,051 English-language news reports on terrorism and counter-terrorism from March 2017 to March 2018 in BBC news, and conducted a text analysis with LDA topic modeling. 20 topics and 5 themes were classified and it was disclosed that major themes include that: (1) BBC focused on constructing local discourse structure, (2) Comparing the news reports at home and abroad, and (3) Historical origins, the development in ancient and modern times and a trial of strength between different countries.