Enhance sentiment analysis in big data tourism using hybrid lexicon and active learning support vector machine

Abstract

Sentiment analysis is a review analysis process used to determine whether an opinion is neutral, negative, or positive. Sentiment analysis can be done using lexicon-based or machine learning-based approaches. Lexicon can perform sentiment analysis without training data because it is dictionary-based but performs worse than machine learning. Machine learning can perform well in completing sentiment analysis but requires training data so that the model does not experience underfitting. In the case of sentiment analysis on big data, manual labeling of training data is an inefficient job. Support vector machine (SVM) has the opportunity to be used together with the active learning (AL) method to make small training data but still have good performance. This research proposed a hybrid lexicon and AL-SVM method to complete sentiment analysis on big data tourism. This research used polarity from the valence aware dictionary and sentiment reasoner (VADER) lexicon as a reference for the query by user process from the AL-SVM to automate the sentiment analysis process on big data. The experimental results showed that using the hybrid lexicon and AL-SVM increased the sentiment analysis performance compared to the VADER lexicon, SVM, and lexicon SVM, which run separately.