Aspect-level sentiment analysis of online product reviews based on multi-features

Abstract

Aspect-level sentiment analysis aims to identify the sentiment polarity of fine-grained opinion targets. Existing methods are usually performed on structured standard datasets. We propose a model for a specific dataset which has a complex structure. First, we utilize some matching rules to extract implicit aspects, then we use the extracted aspect words to segment the corpus into samples. Finally, we propose a set of methods to construct data-based features, and try to fuse multi-features for classifier training. Experiments show that the method integrated three features has the highest F1 score, and the sentiment analysis results are more accurate.