Analysing the features of negative sentiment tweets. Issue 5 (25th September 2018)

• Record Type: Journal Article
• Authors: Zhang, Ling; Dong, Wei; Mu, Xiangming
• Is Part Of: Electronic library. Volume 36:Issue 5(2018)
• Page Start: 782
• Page End: 799
• Publication Date: 2018-09-25
• Subjects: Sentiment analysis -- Twitter -- Features -- Negative sentiment tweets -- Topic classification; Digital libraries -- Periodicals; Libraries -- Automation -- Periodicals; 025.00285
• DOI: 10.1108/EL-05-2017-0120
• Languages: English
• ISSNs: 0264-0473

Abstract

Abstract : Purpose: This paper aims to address the challenge of analysing the features of negative sentiment tweets. The method adopted in this paper elucidates the classification of social network documents and paves the way for sentiment analysis of tweets in further research. Design/methodology/approach: This study classifies negative tweets and analyses their features. Findings: Through negative tweet content analysis, tweets are divided into ten topics. Many related words and negative words were found. Some indicators of negative word use could reflect the degree to which users release negative emotions: part of speech, the density and frequency of negative words and negative word distribution. Furthermore, the distribution of negative words obeys Zipf's law. Research limitations/implications: This study manually analysed only a small sample of negative tweets. Practical implications: The research explored how many categories of negative sentiment tweets there are on Twitter. Related words are helpful to construct an ontology of tweets, which helps people with information retrieval in a fixed research area. The analysis of extracted negative words determined the features of negative tweets, which is useful to detect the polarity of tweets by machine learning method. Originality/value: The research provides an initial exploration of a negative document classification method and classifies the negative tweets into ten topics. By analysing the features of negative tweets, related words, negative words, the density of negative words, etc. are presented. This work is the first step to extend Plutchik's emotion wheel theory into social media data analysis by constructing filed specific thesauri, referred to as local sentimental thesauri.