Analysing the features of negative sentiment tweets
| Date | 01 October 2018 |
| DOI | https://doi.org/10.1108/EL-05-2017-0120 |
| Published date | 01 October 2018 |
| Pages | 782-799 |
| Author | Ling Zhang,Wei Dong,Xiangming Mu |
Analysing the features of negative
sentiment tweets
Ling Zhang
Department of Management, Wuhan University of Science and Technology,
Wuhan, Hubei, China
Wei Dong
Department of Education, Tianjin University, Tianjin, China, and
Xiangming Mu
Department of Information Studies, University of Wisconsin-Milwaukee,
Milwaukee, Wisconsin, USA
Abstract
Purpose –This paper aims to addressthe 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 sentimentanalysis of tweets in further research.
Design/methodology/approach –This study classifiesnegative tweets and analyses their features.
Findings –Through negativetweet content analysis, tweets aredivided into ten topics. Many related words
and negative wordswere found. Some indicators of negative word use couldreflect 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 obeysZipf’s law.
Research limitations/implications –This study manually analysed only a small sample of negative
tweets.
Practical implications –The research exploredhow 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
featuresof negative tweets, which is useful to detect the polarityof tweets by machine learning method.
Originality/value –The research provides an initial exploration of a negative document classification
method and classifiesthe negative tweets into ten topics. By analysing the features of negativetweets, related
words, negative words, the densityof 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,
referredto as local sentimental thesauri.
Keywords Sentiment analysis, Twitter, Features, Negative sentiment tweets, Topic classification
Paper type Research paper
Introduction
The emergence of Web 2.0 has significantly changed the way users perceive the internet.
Contrary to the first generation of websites, on which users could only passively view
content, Web 2.0 users are encouraged to participate and collaborate, forming virtual online
communities. Microblogs, such as Twitter,are one of the popular Web 2.0 applications and
The research was sponsored in part by the National Social Science Fund Project “Study on dynamic
optimisation mechanism of information diffusion in social networks”, Agreement Number 15CTQ029.
EL
36,5
782
Received30 May 2017
Revised10 November 2017
6 January2018
Accepted14 February 2018
TheElectronic Library
Vol.36 No. 5, 2018
pp. 782-799
© Emerald Publishing Limited
0264-0473
DOI 10.1108/EL-05-2017-0120
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0264-0473.htm
services. Such applications have evolved into practical means for sharing opinions on
almost all aspects of everydaylife.
The main difference between microblogsand traditional blogs is the strict constraint on
content size (Kaplan and Haenlein, 2011). For example, Twitter is a popular microblogging
service through which users sendand receive text-based posts, known as tweets, consisting
of up to 140 characters. Twitter was created in 2006 and reports approximately 200 million
active users, posting 400 million tweets per day (Ritter et al.,2011). With its rapidly
increasing popularity, Twitter is important in people’s daily life. Consequently,
microblogging websites have become rich data sources for opinion mining and sentiment
analysis (Kontopoulos etal., 2013). Twitter users can post opinions, experiences and queries
on any chosen topic, and especially emotions, through commenting on sports competitions
and entertainment programmes, sharingopinions on politics, shopping experiences, etc., all
via electronic means.
In this paper, tweet data were collected over one month and were classifiedinto different
topics with reference to negative sentiment words. The findings help the researchers to
explore several issues,such as the characteristics of negative sentiment words on eachtopic.
One contribution of this work is to build a small data collection of negative comments and
sentimental words. These words provide the first step towards constructing sentimental
thesauri based on Plutchik’scircumplex model (Plutchik, 1997). Another contribution is that
this study initially explores several negative document classification techniques and
discusses the principles and bases for classifying negative documents. The analysis
enhances the understanding of the ways in which people present negative sentiments in
social networks.
Literature review
Social media topic classification and semantic text analysis
In recent years, some studies have analysed tweet topic categorisations. For example,
Honeycutt and Herring (2009) used a grounded theoryapproach on their sample and found
12 distinct categories of tweets: about the addressee, to announce or advertise, to exhort,
information for others, informationfor self, meta-commentary, media use, opinions, other’s
experience, self-experience,to solicit information and others. Another example, Hasler et al.,
(2014), took an inductiveor grounded approach and sorted the tweet content into 21 different
topics: health condition, relationships, pregnancy, health resources, legal issues, abuse, sex,
substance use, grief/death, bullying/harassment, sexuality, money issues, parenting,
religions, dreams, employment, feeling lost/lonely, alternative lifestyle, caring for others,
homelessness and schoolwork. In addition,the UK Media Codebook, developed by Jennings
and Bevan (2010), classified social media topics into macroeconomics, civil right/minority
issues/civil liberty, health, agriculture, labour and employment, education, environment,
energy, transportation, law/crime/family issues, social welfare, community development,
planning and housing issues, finance and domestic commerce, defence, space/science/
technology/communications, foreign trade, international affairs/foreign aid, government
operations, public lands/water management/colonial and territorial issues, regional and
local government administration,weather/natural disasters, fires/accidents/other manmade
disasters, arts/history/culture/entertainment, sports/recreation, deaths/death notices and
obituaries, churches/religions, political parties, human interests, news in brief, picture
gallery, display advertising, etc. These categories are various and distinct.However, there
exist few research studiesoncategorizing negativetweets.
In addition, several scholarshave worked on building the ontology of microblogs, which
will assist in making microblog classifications more effective. They believed that Web
Analysing
negative
sentiment
tweets
783
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