Tweet credibility analysis evaluation by improving sentiment dictionary

• Published in: IEEE transactions on evolutionary computation pp. 2354 - 2361
• Main Authors: Kawabe, Takashi, Namihira, Yoshimi, Suzuki, Kouta, Nara, Munehiro, Sakurai, Yoshitaka, Tsuruta, Setsuo, Knauf, Rainer
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2015
• Subjects: Accuracy; Dictionaries; evolutional learn by tweet; imbalanced data analysis; semantic orientation dictionary; Semantics; Sentiment analysis; Speech; topic classification; tweet credibility; Twitter; Web pages
• ISSN: 1089-778X
• DOI: 10.1109/CEC.2015.7257176

To detect false information or rumors spread on Twitter on and after the Great East Japan Earthquake, a tweet credibility assessing method was proposed, based on the topic and opinion classification. The credibility is assessed by calculating the ratio of the same opinions to all opinions about a topic identified by topic models generated using Latent Dirichlet Allocation. To identify an opinion (positive or negative) about a tweet, sentiment analysis is performed using a semantic orientation dictionary. However, it is a kind of imbalanced data analysis to identify usually very few false tweets and the accuracy is a problem. The accuracy of the originally proposed method was susceptible since the sentiment opinion of most tweets was identified negative by the baseline (namely Takamura's) semantic orientation dictionary. To cope with this problem, a method for extracting sentiment orientations of words and phrases is also proposed to improve the evaluation for analyzing the credibility of tweet information. This method 1) evolutionally learns from a large amount of social data on Twitter, 2) focuses on adjective predicates, and 3) considers co-occurrences with negation expressions or multiple adjectives, between subjects and predicates, etc. The effects are proven by experiments using a large number of real tweets, in which we could detect rumor tweet much more accurately. In opposition to the baseline semantic dictionary, our method leads to succeed in imbalanced data analysis.