Twitter Sentiment Mining: A Multi Domain Analysis
MetadataShow full item record
Microblogging such as Twitter provides a rich source of information about products, personalities, and trends, etc. We proposed a simple methodology for analyzing sentiment of users in Twitter. First, we automatically collected Twitter corpus in positive and negative tweets. Second, we built a simple sentiment classifier by utilizing the Naive Bayes model to determine the positive and negative sentiment of a tweet. Third, we tested the classifier against a collection of users’ opinions from five interesting domains of Twitter, i.e., news, finance, job, movies, and sport. The experimental results show that it is feasible to use Twitter corpus alone to classify new tweet for a certain domain applications.
Copyright © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Showing items related by title, author, creator and subject.
Xia, Jianhong (Cecilia); Zhiwen, S. (2016)The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter ...
Abu-Salih, B.; Wongthongtham, Pornpit; Kit, C. (2018)© 2018, Emerald Publishing Limited. Purpose: This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a ...
Abu-Salih, B.; Wongthongtham, Pornpit; Kit, C. (2018)Purpose: This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a significant step towards addressing their ...