%A Li Hui,Chai Yaqing %T Analyzing Sentiment Polarity of Comments Based on Attributes %0 Journal Article %D 2017 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2017.0338 %P 1-11 %V 1 %N 10 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4427.shtml} %8 2017-10-25 %X

[Objective] This article tries to quantitatively study the sentiment polarity of online comments base on the targets’ attributes. [Methods] First, we analyzed the comments by their objects, attributes and contents. Then, we extracted the attribute words and the corresponding comment sets. Third, we introduced the attribute factors and calculated their values with the modified TFIDF formula. Finally, we developed a quantitative analysis algorithm based on the attribute features with Python. [Results] Compared to the traditional machine learning classification algorithms (e.g., NB and SVM), our method improved the accuracy of sentiment classification, when the attribute factor was set to equal weight. [Limitations] The comments selection method and the coefficients parameters of the proposed algorithm need to be improved. [Conclusions] Our method could effectively improve the accuracy of the sentiment classification.