Automatic sentiment analysis of Twitter messages

dc.contributor.authorLima A.C.E.S.
dc.contributor.authorDe Castro L.N.
dc.description.abstractTwitter® is a microblogging service usually used as an instant communication platform. The capacity to provide information in real time has stimulated many companies to use this service to understand their consumers. In this direction, TV stations have adopted Twitter for shortening the distance between them and their viewers, and use such information as a feedback mechanism for their shows. The sentiment analysis task can be used as one such feedback mechanism. This task corresponds to classifying a text according to the sentiment that the writer intended to transmit. A classifier usually requires a pre-classifled data sample to determine the class of new data. Typically, the sample is pre-classified manually, making the process time consuming and reducing its real time applicability for big data. This paper proposes an automatic sentiment classifier for Twitter messages, and uses TV shows from Brazilian stations for benchmarking. The automatic sentiment analysis reduces human intervention and, thus, the complexity and cost of the whole process. To assess the performance of the proposed system tweets related to a Brazilian TV show were captured in a 24h interval and fed into the system. The proposed technique achieved an average accuracy of 90%. © 2012 IEEE.
dc.relation.ispartofProceedings of the 2012 4th International Conference on Computational Aspects of Social Networks, CASoN 2012
dc.rightsAcesso Restrito
dc.subject.otherlanguageBig Data
dc.subject.otherlanguageSentiment Analysis
dc.subject.otherlanguageText Mining
dc.titleAutomatic sentiment analysis of Twitter messages
dc.typeArtigo de evento
local.scopus.subjectBig datum
local.scopus.subjectCommunication platforms
local.scopus.subjectData sample
local.scopus.subjectFeedback mechanisms
local.scopus.subjectHuman intervention
local.scopus.subjectMicro-blogging services
local.scopus.subjectProcess time
local.scopus.subjectReal time
local.scopus.subjectSentiment analysis
local.scopus.subjectText mining
local.scopus.subjectTV stations
local.scopus.subjectWhole process