Detecting contextual word polarity using aspect based sentiment analysis and logistic regression

Publication Type:

Conference Paper

Authors:

Rafeek, Roshna; R. Remya

Source:

2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), IEEE, Chennai, India (2017)

URL:

https://ieeexplore.ieee.org/abstract/document/8089134

Keywords:

aspect based sentiment analysis, Conferences, context dependent opinion words, Context modeling, contextual word polarity, Data mining, logistic regression model, Logistics, LR model, negative class, Pattern classification, polarity changes, positive class, Regression analysis, Sentiment analysis, sentiment word classification, syntactic rules, Syntactics

Abstract:

Sentiment analysis (SA) is a process done computationally for detecting opinion as well as determining their polarity. Context dependent opinion words remains as a challenge for SA since their polarity changes according to the context in which they are used. This work proposes a new approach for solving this problem using (aspect, opinion) pairs and logistic regression (LR) model. Syntactic rules are used for obtaining (aspect, opinion) pairs. LR model is employed as the classifier for classifying the sentiment words into positive class or negative class. In this work we have considered product review dataset and the evaluation results obtained showed an improved classification accuracy.

Cite this Research Publication

R. Rafeek and R. Remya, “Detecting contextual word polarity using aspect based sentiment analysis and logistic regression”, in 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Chennai, India, 2017.

 
          
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