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Product Feature Extraction via Topic Model and Synonym Recognition Approach

  • Jun Feng
  • Wen Yang
  • Cheng Gong
  • Xiaodong Li
  • Rongrong BoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1120)

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.

Keywords

Product feature extraction LDA Synonym recognition Shopping reviews 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jun Feng
    • 1
  • Wen Yang
    • 1
  • Cheng Gong
    • 1
  • Xiaodong Li
    • 1
  • Rongrong Bo
    • 2
    Email author
  1. 1.School of Computer and InformationHohai UniversityNanjingChina
  2. 2.School of Foreign LanguagesNanjing Medical UniversityNanjingChina

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