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A polarity analysis framework for Twitter messages

Author

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  • Lima, Ana Carolina E.S.
  • de Castro, Leandro Nunes
  • Corchado, Juan M.

Abstract

Social media, such as Twitter and Facebook, allow the creation, sharing and exchange of information among people, companies and brands. This information can be used for several purposes, such as to understand consumers and their preferences. In this direction, the sentiment analysis can be used as a feedback mechanism. This analysis corresponds to classifying a text according to the sentiment that the writer intended to transmit. A basic sentiment classifier determines the sentiment polarity (negative, neutral or positive) of a given text at the document, sentence, or feature/aspect level. Advanced types may consider other elements like the emotional state (e.g. angry, sad, happy), affective states (e.g. pleasure and pain), motivational states (e.g. hunger and curiosity), temperaments, among others. In general, there are two main approaches to attribute sentiment to tweets: based on knowledge; or based on machine learning algorithms. In the latter case, the learning algorithm requires a pre-classified 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 a polarity analysis framework for Twitter messages, which combines both approaches and an automatic contextual module. To assess the performance of the proposed framework, four text datasets from the literature are used. Five different types of classifiers were considered: Naïve Bayes (NB); Support Vector Machines (SVM); Decision Trees (J48); and Nearest Neighbors (KNN). The results show that the proposal is a suitable framework to automate the whole polarity analysis process, providing high accuracy levels and low false positive rates.

Suggested Citation

  • Lima, Ana Carolina E.S. & de Castro, Leandro Nunes & Corchado, Juan M., 2015. "A polarity analysis framework for Twitter messages," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 756-767.
    • Handle: RePEc:eee:apmaco:v:270:y:2015:i:c:p:756-767
    DOI: 10.1016/j.amc.2015.08.059
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    References listed on IDEAS

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    1. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    2. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    3. Prabowo, Rudy & Thelwall, Mike, 2009. "Sentiment analysis: A combined approach," Journal of Informetrics, Elsevier, vol. 3(2), pages 143-157.
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    Cited by:

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    3. Yu, Jing-Rung & Chiou, W. Paul & Hung, Cing-Hung & Dong, Wen-Kuei & Chang, Yi-Hsuan, 2022. "Dynamic rebalancing portfolio models with analyses of investor sentiment," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 1-13.

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