A Survey and Comparative Study of Tweet Sentiment Analysis via Semi-Supervised Learning

Author(s)

Da Silva, Nadia Felix F. ; Coletta, Luiz F. S. ; Hruschka, Eduardo R.

• Document type: Journal article
• Source: ACM COMPUTING SURVEYS; v. 49, n. 1 JUL 2016.
• Web of Science Citations: 15

Abstract

Twitter is a microblogging platform in which users can post status messages, called ``tweets,{''} to their friends. It has provided an enormous dataset of the so-called sentiments, whose classification can take place through supervised learning. To build supervised learning models, classification algorithms require a set of representative labeled data. However, labeled data are usually difficult and expensive to obtain, which motivates the interest in semi-supervised learning. This type of learning uses unlabeled data to complement the information provided by the labeled data in the training process; therefore, it is particularly useful in applications including tweet sentiment analysis, where a huge quantity of unlabeled data is accessible. Semi-supervised learning for tweet sentiment analysis, although appealing, is relatively new. We provide a comprehensive survey of semi-supervised approaches applied to tweet classification. Such approaches consist of graph-based, wrapper-based, and topic-based methods. A comparative study of algorithms based on self-training, co-training, topic modeling, and distant supervision highlights their biases and sheds light on aspects that the practitioner should consider in real-world applications. (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: 10/20830-0 - Evolutionary Algorithms for Aggregating Ensembles of Classifiers and Clusterers
• Grantee: Luiz Fernando Sommaggio Coletta
• Support Opportunities: Scholarships in Brazil - Doctorate