Integrating feature and instance selection techniques in opinion mining

Zi Hung You, Ya Han Hu, Chih Fong Tsai, Yen Ming Kuo

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Opinion mining focuses on extracting polarity information from texts. For textual term representation, different feature selection methods, e.g. term frequency (TF) or term frequency- inverse document frequency (TF-IDF), can yield diverse numbers of text features. In text classification, however, a selected training set may contain noisy documents (or outliers), which can degrade the classification performance. To solve this problem, instance selection can be adopted to filter out unrepresentative training documents. Therefore, this article investigates the opinion mining performance associated with feature and instance selection steps simultaneously. Two combination processes based on performing feature selection and instance selection in different orders, were compared. Specifically, two feature selection methods, namely TF and TF-IDF, and two instance selection methods, namely DROP3 and IB3, were employed for comparison. The experimental results by using three Twitter datasets to develop sentiment classifiers showed that TF-IDF followed by DROP3 performs the best.

Original languageEnglish
Pages (from-to)168-182
Number of pages15
JournalInternational Journal of Data Warehousing and Mining
Issue number3
StatePublished - 1 Jul 2020


  • Feature Selection
  • Instance Selection
  • Opinion Mining
  • Text Classification


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