An effective hybrid model for opinion mining and sentiment analysis

Kai Yang, Yi Cai, Dongping Huang, Jingnan Li, Zikai Zhou, Xue Lei

Abstract: Sentiment analysis and opinion mining is a task to analyze people's opinions or sentiments from textual data, which is very useful for the analysis of many NLP applications. The difficulty of this task is that there are a variety of sentiments inside documents, and these sentiments have variety expressions. Hence, it is hard to extract all sentiments using a dictionary that is commonly used. In this paper, we construct the domain sentiment dictionary using external textual data. Besides, many classification models can be used to classify documents according to their opinion. However, these single models have strengths and weaknesses. We propose a highly effective hybrid model combining different single models to overcome their weaknesses. The experimental results show that our hybrid model outperforms baseline single models.