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Emotion Detection on Twitter Data using Knowledge Base Approach

by Srinivasu Badugu, Matla Suhasini
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 10
Year of Publication: 2017
Authors: Srinivasu Badugu, Matla Suhasini
10.5120/ijca2017913366

Srinivasu Badugu, Matla Suhasini . Emotion Detection on Twitter Data using Knowledge Base Approach. International Journal of Computer Applications. 162, 10 ( Mar 2017), 28-33. DOI=10.5120/ijca2017913366

@article{ 10.5120/ijca2017913366,
author = { Srinivasu Badugu, Matla Suhasini },
title = { Emotion Detection on Twitter Data using Knowledge Base Approach },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 10 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 28-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume162/number10/27281-2017913366/ },
doi = { 10.5120/ijca2017913366 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:08:40.822303+05:30
%A Srinivasu Badugu
%A Matla Suhasini
%T Emotion Detection on Twitter Data using Knowledge Base Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 10
%P 28-33
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Emotional states of individuals, also known as moods, are central to the expression of thoughts, ideas and opinions, and in turn impact attitudes and behavior. Social media tools like twitter is increasingly used by individuals to broadcast their day-to-day happenings or to report on an external event of interest, understanding the rich ‘landscape’ of moods will help us better to interpret millions of individuals. This paper describes a Rule Based approach, which detects the emotion or mood of the tweet and classifies the twitter message under appropriate emotional category. The accuracy with the system is 85%. With the proposed system it is possible to understand the deeper levels of emotions i.e., finer grained instead of sentiment i.e., coarse grained. Sentiment says whether the tweet is positive or negative but the proposed system gives the deeper information of tweet which has adverse uses in the field of Psychology, Intelligence Bureau, Social and Economic trends.

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Index Terms

Computer Science
Information Sciences

Keywords

Mood Detection Emotion Natural Language Processing POS Tagging.

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