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Academic Journal of Computing & Information Science, 2019, 2(1); doi: 10.25236/AJCIS.010024.

Online Comment Text Analysis with Improved Feature Weight

Author(s)

Chaoju Hu*, Xiaojie Yang

Corresponding Author:
Chaoju Hu
Affiliation(s)

Department of computer, North China Electric Power University, Baoding 071000, China
*Corresponding author e-mail: y2296665134@163.com

Abstract

In the product reviews of online shopping platforms, the star rating and text comments given by the same user often appear. These data are processed by the unreasonable scoring system to give the merchants a false rating and mislead consumers. In order to improve the scoring system, an improved feature weighting method combining text review content and star rating is proposed. Firstly, the weighting rules were defined. Secondly, according to the part-of-speech evaluation function in the short text, the feature word selection was combined with the star rating and the text comment content. The CBOW model was used to train the word vector. Finally, the text classification method SVM was used to obtain the final result. This model was applied to two text datasets and compared with the traditional TF-IDF feature selection. The results show that the algorithm can effectively improve the accuracy and F1 value of text classification.

Keywords

Online user review, SVM, short text classification, sentiment analysis

Cite This Paper

Chaoju Hu, Xiaojie Yang, Online Comment Text Analysis with Improved Feature Weight. Academic Journal of Computing & Information Science (2019) Vol. 2: 110-119. https://doi.org/10.25236/AJCIS.010024.

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