Abstract
As e-commerce is becoming more and more popular, sentiment analysis of online reviews has become one of the most important studies in text mining. The main task of sentiment analysis is to analyze the user’s attitude towards different product features. Product feature extraction refers to extracting the product features of user evaluation from reviews, which is the first step to achieve further sentiment analysis tasks. The existing product feature extraction methods do not address flexibility and randomness of online reviews. Moreover, these methods have defects such as low accuracy and recall rate. In this study, we propose a product feature extraction method based on topic model and synonym recognition. Firstly, we set a threshold that TF-IDF value of a product feature noun must reach to filter meaningless words in reviews, and select the threshold by grid search. Secondly, considering the occurrence rule of different product features in reviews, we propose a novel product similarity calculation, which also performs weighted fusion based on information entropy with a variety of general similarity calculation methods. Finally, compared with traditional methods, the experimental results show that the product feature extraction method proposed in this paper can effectively improve \( F1 \) and recall score of product feature extraction.
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Feng, J., Yang, W., Gong, C., Li, X., Bo, R. (2019). Product Feature Extraction via Topic Model and Synonym Recognition Approach. In: Jin, H., Lin, X., Cheng, X., Shi, X., Xiao, N., Huang, Y. (eds) Big Data. BigData 2019. Communications in Computer and Information Science, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-1899-7_6
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DOI: https://doi.org/10.1007/978-981-15-1899-7_6
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