Integrating Labeled Latent Dirichlet Allocation into sentiment analysis of movie and general domains
Ryan Coughlin, Jean-Charles Coetsier, Rachsuda Jiamthapthaksin · 2017
Sentiment Analysis is an ongoing research, which involves design and development of various algorithms. The goal of this work is to improve the accuracy of widely used algorithms in sentiment analysis. To achieve it, the work proposes to integrate different preprocessing methods including Labeled Latent Dirichlet Allocation, removing stop words and using adjectives that have a significant impact on the document's sentiment, into three popular text classification algorithms: Support Vector Machine, Naïve Bayes and artificial neural network. By implementing them and using 5 real datasets in general and specific domains, the study evaluates the effectiveness of the proposed preprocessing method in sentiment analysis. The results show that it achieves improvement on both domains.