Lexicon-based Sentiment Analysis Using the Particle Swarm Optimization

摘要

This work belongs to the field of sentiment analysis; in particular, to opinion and emotion classification using a lexicon-based approach. It solves several problems related to increasing the effectiveness of opinion classification. The first problem is related to lexicon labelling. Human labelling in the field of emotions is often too subjective and ambiguous, and so the possibility of replacement by automatic labelling is examined. This paper offers experimental results using a nature-inspired algorithm—particle swarm optimization—for labelling. This optimization method repeatedly labels all words in a lexicon and evaluates the effectiveness of opinion classification using the lexicon until the optimal labels for words in the lexicon are found. The second problem is that the opinion classification of texts which do not contain words from the lexicon cannot be successfully done using the lexicon-based approach. Therefore, an auxiliary approach, based on a machine learning method, is integrated into the method. This hybrid approach is able to classify more than 99% of texts and achieves better results than the original lexicon-based approach. The final hybrid model can be used for emotion analysis in human–robot interactions.