AN EXPERIMENTAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MINING
J. Kumar, and S. Abirami. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 4 (1):
15 - 21(February 2015)
Abstract
The feature selection or extraction is the most important task in Opinion mining and Sentimental Analysis
(OSMA) for calculating the polarity score. These scores are used to determine the positive, negative, and
neutral polarity about the product, user reviews, user comments, and etc., in social media for the purpose
of decision making and Business Intelligence to individuals or organizations. In this paper, we have
performed an experimental study for different feature extraction or selection techniques available for
opinion mining task. This experimental study is carried out in four stages. First, the data collection process
has been done from readily available sources. Second, the pre-processing techniques are applied
automatically using the tools to extract the terms, POS (Parts-of-Speech). Third, different feature selection
or extraction techniques are applied over the content. Finally, the empirical study is carried out for
analyzing the sentiment polarity with different features.
%0 Journal Article
%1 kumarexperimental
%A Kumar, J. Ashok
%A Abirami, S.
%D 2015
%J International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI)
%K Analysis Classification Extraction Feature Mining Opinion Polarity Sentiment
%N 1
%P 15 - 21
%T AN EXPERIMENTAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MINING
%U https://airccse.org/journal/ijscai/papers/4115ijscai02.pdf
%V 4
%X The feature selection or extraction is the most important task in Opinion mining and Sentimental Analysis
(OSMA) for calculating the polarity score. These scores are used to determine the positive, negative, and
neutral polarity about the product, user reviews, user comments, and etc., in social media for the purpose
of decision making and Business Intelligence to individuals or organizations. In this paper, we have
performed an experimental study for different feature extraction or selection techniques available for
opinion mining task. This experimental study is carried out in four stages. First, the data collection process
has been done from readily available sources. Second, the pre-processing techniques are applied
automatically using the tools to extract the terms, POS (Parts-of-Speech). Third, different feature selection
or extraction techniques are applied over the content. Finally, the empirical study is carried out for
analyzing the sentiment polarity with different features.
@article{kumarexperimental,
abstract = {The feature selection or extraction is the most important task in Opinion mining and Sentimental Analysis
(OSMA) for calculating the polarity score. These scores are used to determine the positive, negative, and
neutral polarity about the product, user reviews, user comments, and etc., in social media for the purpose
of decision making and Business Intelligence to individuals or organizations. In this paper, we have
performed an experimental study for different feature extraction or selection techniques available for
opinion mining task. This experimental study is carried out in four stages. First, the data collection process
has been done from readily available sources. Second, the pre-processing techniques are applied
automatically using the tools to extract the terms, POS (Parts-of-Speech). Third, different feature selection
or extraction techniques are applied over the content. Finally, the empirical study is carried out for
analyzing the sentiment polarity with different features.},
added-at = {2022-01-12T07:25:23.000+0100},
author = {Kumar, J. Ashok and Abirami, S.},
biburl = {https://www.bibsonomy.org/bibtex/2413cb4c3475c4450829d200cc0e6273b/leninsha},
interhash = {56f2af01887eb04aa425ac6d2a2361b6},
intrahash = {413cb4c3475c4450829d200cc0e6273b},
journal = {International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI)},
keywords = {Analysis Classification Extraction Feature Mining Opinion Polarity Sentiment},
month = {February},
number = 1,
pages = {15 - 21},
timestamp = {2022-01-12T07:25:23.000+0100},
title = {AN EXPERIMENTAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MINING},
url = {https://airccse.org/journal/ijscai/papers/4115ijscai02.pdf},
volume = 4,
year = 2015
}