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.
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Wang, B., Wang, R., Liu, S., Chai, Y., Xing, S. (2020). Aspect-Level Sentiment Analysis of Online Product Reviews Based on Multi-features. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Communications in Computer and Information Science, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-3412-6_16
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DOI: https://doi.org/10.1007/978-981-15-3412-6_16
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