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Interpretive structure modelling(ISM) for feature dependency in sentiment analysis

Authors: Hina Gupta; Nitasha Hasteer; Rana Majumdar;

Interpretive structure modelling(ISM) for feature dependency in sentiment analysis

Abstract

Under the domain of text mining, Sentiment Analysis is a field that is in progress these days. Sentiment analysis is the calculative analysis of views, sentiments, opinions and positivity or negativity of a text. This paper identifies the factors that are responsible for the different sentiments of a person regarding a particular entity. In this work, the objective is to categorize the factors that influence the system of sentiment analysis due to varied sentiments of an individual. The methodology of Interpretative Structure Modeling has been employed for identifying the driving power and the dependent power of the various elements influencing sentiment analysis.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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