Light Gradient Boosting Machine for General Sentiment Classification on Short Texts: A Comparative Evaluation

Abstract

Recently, the focus on sentiment analysis has been domain dependent even though the expressions used by the public are unsophisticatedly familiar regardless of the topics or domains. Online social media (OSNs) has been a daily venue for informal conversational contents from various domains ranging from sports and cooking to politics and human rights. Generating specific resources for every domain independently requires high cost and extensive efforts. In response, we propose to build a general multi-class sentiment classifier using our Domain-Free Sentiment Multimedia Dataset (DFSMD). Based on the proven capabilities of Light Gradient Boosting Machine (LGBM) in dealing with high dimensional and imbalance data, we have trained an LGBM model to recognize one of three sentiments of tweets: positive, negative, or neutral. We have conducted extensive comparisons and evaluations for six other standard sentiment classification algorithms and different sets of features including OSNs-specific ones. Our results have shown that LGBM model is the winner among the other six algorithms. It has been also shown that our dataset contains distinguishing characteristics in the three classes. Moreover, hashtag words are shown to be significantly important in capturing the sentiments of tweets. In addition, our findings have revealed the effectiveness of our approach in adapting general-domain sentiment to domain-specific sentiment analysis.