CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-10-17
Cited: 0
Clicked: 7187
G. R. Brindha, P. Swaminathan, B. Santhi. Performance analysis of new word weighting procedures for opinion mining[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(11): 1186-1198.
@article{title="Performance analysis of new word weighting procedures for opinion mining",
author="G. R. Brindha, P. Swaminathan, B. Santhi",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="11",
pages="1186-1198",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500283"
}
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%T Performance analysis of new word weighting procedures for opinion mining
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%A P. Swaminathan
%A B. Santhi
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 11
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500283
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T1 - Performance analysis of new word weighting procedures for opinion mining
A1 - G. R. Brindha
A1 - P. Swaminathan
A1 - B. Santhi
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 11
SP - 1186
EP - 1198
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1500283
Abstract: The proliferation of forums and blogs leads to challenges and opportunities for processing large amounts of information. The information shared on various topics often contains opinionated words which are qualitative in nature. These qualitative words need statistical computations to convert them into useful quantitative data. This data should be processed properly since it expresses opinions. Each of these opinion bearing words differs based on the significant meaning it conveys. To process the linguistic meaning of words into data and to enhance opinion mining analysis, we propose a novel weighting scheme, referred to as inferred word weighting (IWW). IWW is computed based on the significance of the word in the document (SWD) and the significance of the word in the expression (SWE) to enhance their performance. The proposed weighting methods give an analytic view and provide appropriate weights to the words compared to existing methods. In addition to the new weighting methods, another type of checking is done on the performance of text classification by including stop-words. Generally, stop-words are removed in text processing. When this new concept of including stop-words is applied to the proposed and existing weighting methods, two facts are observed: (1) Classification performance is enhanced; (2) The outcome difference between inclusion and exclusion of stop-words is smaller in the proposed methods, and larger in existing methods. The inferences provided by these observations are discussed. Experimental results of the benchmark data sets show the potential enhancement in terms of classification accuracy.
The paper is a good work where the authors propose a new method to weight the relevance of terms for polarity classification systems.
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