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Twitter Sentiment Analysis System

by Shaunak Joshi, Deepali Deshpande
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 47
Year of Publication: 2018
Authors: Shaunak Joshi, Deepali Deshpande
10.5120/ijca2018917319

Shaunak Joshi, Deepali Deshpande . Twitter Sentiment Analysis System. International Journal of Computer Applications. 180, 47 ( Jun 2018), 35-39. DOI=10.5120/ijca2018917319

@article{ 10.5120/ijca2018917319,
author = { Shaunak Joshi, Deepali Deshpande },
title = { Twitter Sentiment Analysis System },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2018 },
volume = { 180 },
number = { 47 },
month = { Jun },
year = { 2018 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number47/29557-2018917319/ },
doi = { 10.5120/ijca2018917319 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:03:52.803233+05:30
%A Shaunak Joshi
%A Deepali Deshpande
%T Twitter Sentiment Analysis System
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 47
%P 35-39
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Social media is increasingly used by humans to express their feelings and opinions in the form of short text messages. Detecting sentiments in text has a wide range of applications including identifying anxiety or depression of individuals and measuring well-being or mood of a community. Sentiments can be expressed in many ways that can be seen such as facial expression and gestures, speech and by written text. Sentiment Analysis in text documents is essentially a content – based classification problem involving concepts from the domains of Natural Language Processing as well as Machine Learning. In this paper, sentiment recognition based on textual data and the techniques used in sentiment analysis are discussed.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning Python Social Media Sentiment Analysis

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