<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

Department of Computer & Electrical Engineering

Department of Computer Engineering & Information Technology

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Opinion Mining or Sentiment Analysis is the task of extracting people final opinion about something through their unstructured sentiments. The Opinion Mining...

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

Pubblicato online: 30 giu 2018

Pagine: 36 - 50

Ricevuto: 25 nov 2017

Accettato: 15 feb 2018

DOI: https://doi.org/10.2478/cait-2018-0026

Parole chiave
Opinion mining, sentiment analysis, Particle Swarm Optimization Algorithm, fuzzy classification algorithm

© 2018 Samira Bordbar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Pubblicato online: 30 giu 2018

Pagine: 36 - 50

Ricevuto: 25 nov 2017

Accettato: 15 feb 2018

DOI: https://doi.org/10.2478/cait-2018-0026

Parole chiaveOpinion mining, sentiment analysis, Particle Swarm Optimization Algorithm, fuzzy classification algorithm

© 2018 Samira Bordbar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Opinion mining, sentiment analysis, Particle Swarm Optimization Algorithm, fuzzy classification algorithm