Stance detection and mobile app recommendation discourse on tweets. (22nd July 2019)

• Record Type: Journal Article
• Authors: R, Muthusami; A, Bharathi
• Is Part Of: Computational intelligence. Volume 35:Number 4(2019)
• Page Start: 1042
• Page End: 1059
• Publication Date: 2019-07-22
• Subjects: app recommendation -- knowledge discovery -- stance detection -- structural topic model -- tweets discourse; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12231
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Web‐based social networking such as microblogging administrations and long‐range informal communication locales are changing the way in which individuals collaborate on the web and search for data and opinions. An essential parameter of online networking discourse is searchability. A key semiotic asset supporting this capacity is the hashtag, a type of social label that enables microbloggers to insert metadata in online networking posts. In this paper, an attempt is made to analyze stance detection and app recommendation discourse on tweets in view of hashtag techniques, which is in the territory of etymology, and to spotlight the structure of dialect at the provision level. With a revival of enthusiasm for topics identified by modeling language at the discourse level, a graphical model of conversational structure (ie, the structural topic model) has been constructed by means of utilizing three methods: displaying words connected with topics or documents highly connected with topics, calculating topic correlations, and assessing associations between metadata and topical content, its capture of latent topics, and topical structures inside documents on a benchmark dataset (ie, SemEval 2016) has been scrutinized for stance detection, and data have been crawled from Twitter, using the hashtag #App for app recommendations.