INTERPRETIVE STRUCTURE MODELLING(ISM) FOR FEATURE DEPENDENCY IN SENTIMENT ANALYSIS

被引：0

作者：

Gupta, Hina ^{[1
]}

Hasteer, Nitasha ^{[1
]}

Majumdar, Rana ^{[1
]}

机构：

[1] Amity Univ, Amity Sch Engn, Noida, India

来源：

PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE AND ENGINEERING (CONFLUENCE 2017) | 2017年

关键词：

Sentiment Analysis; Feature relevance; ISM; MICMAC;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：86 / 91

页数：6

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