A Machine Learning-Based Lexicon Approach for Sentiment Analysis

被引：6

作者：

Sahu, Tirath Prasad ^{[1
]}

Khandekar, Sarang ^{[2
]}

机构：

[1] NIT Raipur, Dept Informat Technol, Raipur, Madhya Pradesh, India

[2] NIT Raipur, Raipur, Madhya Pradesh, India

来源：

INTERNATIONAL JOURNAL OF TECHNOLOGY AND HUMAN INTERACTION | 2020年 / 16卷 / 02期

关键词：

Lexicon Resource; Machine Learning; Online Reviews; Performance Measures; Sentiment Analysis;

D O I：

10.4018/IJTHI.2020040102

中图分类号：

G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];

学科分类号：

1205 ; 120501 ;

摘要：

Sentiment analysis can be a very useful aspect for the extraction of useful information from text documents. The main idea for sentiment analysis is how people think for a particular online review, i.e. product reviews, movie reviews, etc. Sentiment analysis is the process where these reviews are classified as positive or negative. The web is enriched with huge amount of reviews which can be analyzed to make it meaningful. This article presents the use of lexicon resources for sentiment analysis of different publicly available reviews. First, the polarity shift of reviews is handled by negations. Intensifiers, punctuation and acronyms are also taken into consideration during the processing phase. Second, words are extracted which have some opinion; these words are then used for computing score. Third, machine learning algorithms are applied and the experimental results show that the proposed model is effective in identifying the sentiments of reviews and opinions.

引用

页码：8 / 22

页数：15

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