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Reseach Article

Effective Sentiment Analysis of Social Media Datasets using Naive Bayesian Classification

by Dhiraj Gurkhe, Niraj Pal, Rishit Bhatia
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 99 - Number 13
Year of Publication: 2014
Authors: Dhiraj Gurkhe, Niraj Pal, Rishit Bhatia
10.5120/17430-8274

Dhiraj Gurkhe, Niraj Pal, Rishit Bhatia . Effective Sentiment Analysis of Social Media Datasets using Naive Bayesian Classification. International Journal of Computer Applications. 99, 13 ( August 2014), 1-4. DOI=10.5120/17430-8274

@article{ 10.5120/17430-8274,
author = { Dhiraj Gurkhe, Niraj Pal, Rishit Bhatia },
title = { Effective Sentiment Analysis of Social Media Datasets using Naive Bayesian Classification },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 99 },
number = { 13 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume99/number13/17430-8274/ },
doi = { 10.5120/17430-8274 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:28:05.065367+05:30
%A Dhiraj Gurkhe
%A Niraj Pal
%A Rishit Bhatia
%T Effective Sentiment Analysis of Social Media Datasets using Naive Bayesian Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 99
%N 13
%P 1-4
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Effective Sentiment Analysis Of Social Media Datasets Using Naive Bayesian Classification involves extraction of subjective information from textual data. A normal human can easily understand the sentiment of a document written in natural language based on its knowledge of understanding the polarity of words (unigram, bigram and n-grams) and in some cases the general semantics used to describe the subject. The project aims to make the machine extract the polarity (positive, negative or neutral) of social media dataset with respect to the queried keyword. This project introduces an approach for automatically classifying the sentiment of social media data by using the following procedure: First the training data is fed to the Sentiment Analysis Engine for learning by using machine learning algorithm. After the learning is complete with qualified accuracy, the machine starts accepting individual social data with respect to keyword that it analyses and interprets, and then classifies it as positive, negative or neutral with respect to the query term.

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Index Terms

Computer Science
Information Sciences

Keywords

Natural Language Processing Machine Learning Supervised Learning Text Analysis