CFP last date
01 April 2024
Reseach Article

Sentiment Analysis of Twitter Feeds using Machine Learning, Effect of Feature Hash Bit Size

by Silas Kwabla Gah, Nana Kwame Gyamfi, Ferdinard Katsriku
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 6 - Number 9
Year of Publication: 2017
Authors: Silas Kwabla Gah, Nana Kwame Gyamfi, Ferdinard Katsriku
10.5120/cae2017652544

Silas Kwabla Gah, Nana Kwame Gyamfi, Ferdinard Katsriku . Sentiment Analysis of Twitter Feeds using Machine Learning, Effect of Feature Hash Bit Size. Communications on Applied Electronics. 6, 9 ( Apr 2017), 16-21. DOI=10.5120/cae2017652544

@article{ 10.5120/cae2017652544,
author = { Silas Kwabla Gah, Nana Kwame Gyamfi, Ferdinard Katsriku },
title = { Sentiment Analysis of Twitter Feeds using Machine Learning, Effect of Feature Hash Bit Size },
journal = { Communications on Applied Electronics },
issue_date = { Apr 2017 },
volume = { 6 },
number = { 9 },
month = { Apr },
year = { 2017 },
issn = { 2394-4714 },
pages = { 16-21 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume6/number9/717-2017652544/ },
doi = { 10.5120/cae2017652544 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:56:26.955058+05:30
%A Silas Kwabla Gah
%A Nana Kwame Gyamfi
%A Ferdinard Katsriku
%T Sentiment Analysis of Twitter Feeds using Machine Learning, Effect of Feature Hash Bit Size
%J Communications on Applied Electronics
%@ 2394-4714
%V 6
%N 9
%P 16-21
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment Analysis is a way of considering and grouping of opinions or views expressed in a text. In this age when social media technologies are generating vast amounts of data in the form of tweets, Facebook comments, blog posts, and Instagram comments, sentiment analysis of these user-generated data provides very useful feedback. Since it is undisputable facts that twitter sentiment analysis has become an effective way in determining public sentiment about a certain topic product or issue. Thus, a lot of research have been ongoing in recent years to build efficient models for sentiment classification accuracy and precision. In this work, we analyse twitter data using support vector machine algorithm to classify tweets into positive, negative and neutral sentiments. This research try to find the relationship between feature hash bit size and the accuracy and precision of the model that is generated. We measure the effect of varying the feature has bit size on the accuracy and precision of the model. The research showed that as the feature hash bit size increases at a certain point the accuracy and precision value started decreasing with increase in the feature hash bit size.

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Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis; Machine Learning; Support Vector Machine; Feature Hashing