Deep Learning Based Techniques for Sentiment Analysis: A Survey

被引：14

作者：

Etaiwi, Wael ^{[1
]}

Suleiman, Dima ^{[2
]}

Awajan, Arafat ^{[3
,4
]}

机构：

[1] Princess Sumaya Univ Technol, King Talal Sch Business Technol, Amman, Jordan

[2] Univ Jordan, King Abdullah II Sch Informat Technol, Amman, Jordan

[3] Princess Sumaya Univ Technol, King Hussein Sch Comp Sci, Amman, Jordan

[4] Mutah Univ, Coll Informat Technol, Alkarak, Jordan

来源：

INFORMATICA-AN INTERNATIONAL JOURNAL OF COMPUTING AND INFORMATICS | 2021年 / 45卷 / 07期

关键词：

natural nanguage processing; sentiment analysis; deep learning;

D O I：

10.31449/inf.v45i7.3674

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The automated representation of human language using a variety of techniques is called Natural Language Processing (NLP). Improvements to NLP applications are important and can be accomplished using a variety of methods, such as graphs, deep neural networks, and word embedding. Sentiment classification, which attempts to automatically classify opinionated text as positive, negative, or neutral, is a fundamental activity of sentiment analysis. Sentiment analysis methods focused on deep learning over the past five years are analyzed in this review.

引用

页码：89 / 96

页数：8

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