Abstract

In this paper, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages. We evaluate the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging. We take a supervied approach to the problem, but leverage existing hashtags in the Twitter data for building training data.

Keywords

MicrobloggingLeverage (statistics)Computer scienceSocial mediaSentiment analysisData scienceArtificial intelligenceNatural language processingInformation retrievalWorld Wide Web

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Publication Info

Year
2021
Type
article
Volume
5
Issue
1
Pages
538-541
Citations
1233
Access
Closed

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Cite This

Efthymios Kouloumpis, Theresa Wilson, Johanna D. Moore (2021). Twitter Sentiment Analysis: The Good the Bad and the OMG!. Proceedings of the International AAAI Conference on Web and Social Media , 5 (1) , 538-541. https://doi.org/10.1609/icwsm.v5i1.14185

Identifiers

DOI
10.1609/icwsm.v5i1.14185