| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
[2] Univ Fed Sao Carlos UFSCAR, Dept Comp Sci, Sao Carlos, SP - Brazil
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | DECISION SUPPORT SYSTEMS; v. 66, p. 170-179, OCT 2014. |
| Web of Science Citations: | 103 |
| Abstract | |
Twitter is a microblogging site in which users can post updates (tweets) to friends (followers). It has become an immense dataset of the so-called sentiments. In this paper, we introduce an approach that automatically classifies the sentiment of tweets by using classifier ensembles and lexicons. Tweets are classified as either positive or negative concerning a query term. This approach is useful for consumers who can use sentiment analysis to search for products, for companies that aim at monitoring the public sentiment of their brands, and for many other applications. Indeed, sentiment classification in microblogging services (e.g., Twitter) through classifier ensembles and lexicons has not been well explored in the literature. Our experiments on a variety of public tweet sentiment datasets show that classifier ensembles formed by Multinomial Naive Bayes, SVM, Random Forest, and Logistic Regression can improve classification accuracy. (C) 2014 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 13/07787-6 - Models and algorithm for never-ending learning |
| Grantee: | Estevam Rafael Hruschka Júnior |
| Support Opportunities: | Regular Research Grants |