INFORMATION CHANGE THE WORLD

International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.13, No.2, Apr. 2021

Investigation of Different Machine Learning Algorithms to Determine Human Sentiment Using Twitter Data

Full Text (PDF, 680KB), PP.38-48


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Author(s)

Golam Mostafa, Ikhtiar Ahmed, Masum Shah Junayed

Index Terms

Sentiment Analysis;Tweet;Twitter;Sentiment;Social Media;Machine learning;Natural Language Processing

Abstract

In recent years, with the advancement of the internet, social media is a promising platform to explore what going on around the world, sharing opinions and personal development. Now, Sentiment analysis, also known as text mining is widely used in the data science sector. It is an analysis of textual data that describes subjective information available in the source and allows an organization to identify the thoughts and feelings of their brand or goods or services while monitoring conversations and reviews online. Sentiment analysis of Twitter data is a very popular research work nowadays. Twitter is that kind of social media where many users express their opinion and feelings through small tweets and different machine learning classifier algorithms can be used to analyze those tweets. In this paper, some selected machine learning classifier algorithms were applied on crawled Twitter data after applying different types of preprocessors and encoding techniques, which ended up with satisfying accuracy. Later a comparison between the achieved accuracies was showed. Experimental evaluations show that the Neural Network Classifier’ algorithm provides a remarkable accuracy of 81.33% compared with other classifiers.

Cite This Paper

Golam Mostafa, Ikhtiar Ahmed, Masum Shah Junayed, "Investigation of Different Machine Learning Algorithms to Determine Human Sentiment Using Twitter Data", International Journal of Information Technology and Computer Science(IJITCS), Vol.13, No.2, pp.38-48, 2021. DOI: 10.5815/ijitcs.2021.02.04

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