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Twitter Sentiment Analysis using Rapid Miner Tool

by Shilpa Singh Hanswal, Astha Pareek, Amita Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 16
Year of Publication: 2019
Authors: Shilpa Singh Hanswal, Astha Pareek, Amita Sharma
10.5120/ijca2019919604

Shilpa Singh Hanswal, Astha Pareek, Amita Sharma . Twitter Sentiment Analysis using Rapid Miner Tool. International Journal of Computer Applications. 177, 16 ( Nov 2019), 44-50. DOI=10.5120/ijca2019919604

@article{ 10.5120/ijca2019919604,
author = { Shilpa Singh Hanswal, Astha Pareek, Amita Sharma },
title = { Twitter Sentiment Analysis using Rapid Miner Tool },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2019 },
volume = { 177 },
number = { 16 },
month = { Nov },
year = { 2019 },
issn = { 0975-8887 },
pages = { 44-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number16/30986-2019919604/ },
doi = { 10.5120/ijca2019919604 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:46:05.749647+05:30
%A Shilpa Singh Hanswal
%A Astha Pareek
%A Amita Sharma
%T Twitter Sentiment Analysis using Rapid Miner Tool
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 16
%P 44-50
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Since last few years social networking and Micro-blogging sites have become a largest platform for sharing users’ personal feelings, marketing or social liking. Especially in product based company where success of a company depends on the opinion of different customers. These opinions can be use to analyze the user’s sentiments, feelings and assessment of product. In this paper tweets about government schemes has been fetched from twitter with the help of scraper written in python language. Tweets are divided into two data sets, one is of 50 tweets length and another data set is of 200 tweets length. An experiment has been performed in Rapid Miner tool to find accuracy of sentiments polarity using Naive Bayes and k-NN techniques, also comparison between these techniques is observed to find out the best performing one.

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Index Terms

Computer Science
Information Sciences

Keywords

Sentiment analysis Naïve Bayes k-NN Rapid Miner Python Twitter polarity.

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