A New Opinion Mining Method based on Fuzzy Classifier and Particle Swarm Optimization (PSO) Algorithm

被引:4
作者
Bordbar, Samira [1 ]
Shamsinejad, Pirooz [2 ]
机构
[1] Islamic Azad Univ, Shiraz Branch, Dept Comp & Elect Engn, Shiraz, Iran
[2] Shiraz Univ Technol, Dept Comp Engn & Informat Technol, Shiraz, Iran
关键词
Opinion mining; sentiment analysis; Particle Swarm Optimization Algorithm; fuzzy classification algorithm;
D O I
10.2478/cait-2018-0026
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Opinion Mining or Sentiment Analysis is the task of extracting people final opinion about something through their unstructured sentiments. The Opinion Mining process is as follows: first, product features which are most important to a user are extracted from his/her comments. Then, sentiments will be emotionally classified using their emotional implications. In this paper we propose an opinion classification method based on Fuzzy Logic. Up to now, a few methods have taken advantage of fuzzy logic in opinion classification and all of them have imported fuzzy rules into system as background knowledge. But the main challenge here is finding the fuzzy rules. Our contribution is to automatically extract fuzzy rules and their parameters from training data. Here we have used the Particle Swarm Optimization (PSO) algorithm to extract fuzzy rules from training data. Also, for better results we have devised a mutation-based PSO. All proposed methods have been implemented and tested on relevant data. Results confirm that our method can reach better accuracy than current state of the art methods in this domain.
引用
收藏
页码:36 / 50
页数:15
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