An effective machine learning approach for sentiment analysis of restaurant reviews

This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.

Dettagli Bibliografici
Autore principale:	Karim, Rabita
Altri autori:	Uddin, Dr. Jia
Natura:	Tesi
Lingua:	English
Pubblicazione:	BARC University 2018
Soggetti:	Machine learning Sentiment analysis Opinion analysis Restaurant reviews Gaussian Naive Bayes classifier
Accesso online:	http://hdl.handle.net/10361/9023

id	10361-9023
record_format	dspace
spelling	10361-90232022-01-26T10:21:46Z An effective machine learning approach for sentiment analysis of restaurant reviews Karim, Rabita Uddin, Dr. Jia Department of Computer Science and Engineering, BRAC University Machine learning Sentiment analysis Opinion analysis Restaurant reviews Gaussian Naive Bayes classifier This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016. Cataloged from PDF version of thesis report. Includes bibliographical references (pages 31-32). Sentiment analysis or opinion analysis is creating a vast area of research in this modern era of social media. Various blogs and Social Medias (Facebook, twitter, Instagram) are the most popular platform for the users or consumers where they frequently express their opinion about current topics, various brands, restaurants, movies, books, traveling places etc. Sentiment analysis is a very smart and effective approach to find peoples view about a particular news/ place/restaurant/movie/book/brand. It is beneficial for the both service providers or sellers and consumers. Researchers in the areas of natural language processing, data mining, machine learning, and others have tested a variety of methods of automating the sentiment analysis process. In this research work, I used restaurant reviews dataset to analysis the sentiment and for this approach Gaussian Naïve Bayes method is proposed based on coupling classification methods using arcing classifier and their performances are analyzed in terms of accuracy. Rabita Karim B. Computer Science and Engineering 2018-01-11T06:27:18Z 2018-01-11T06:27:18Z 2016 2016-08 Thesis ID 13101248 http://hdl.handle.net/10361/9023 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 32 pages application/pdf BARC University
institution	Brac University
collection	Institutional Repository
language	English
topic	Machine learning Sentiment analysis Opinion analysis Restaurant reviews Gaussian Naive Bayes classifier
spellingShingle	Machine learning Sentiment analysis Opinion analysis Restaurant reviews Gaussian Naive Bayes classifier Karim, Rabita An effective machine learning approach for sentiment analysis of restaurant reviews
description	This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.
author2	Uddin, Dr. Jia
author_facet	Uddin, Dr. Jia Karim, Rabita
format	Thesis
author	Karim, Rabita
author_sort	Karim, Rabita
title	An effective machine learning approach for sentiment analysis of restaurant reviews
title_short	An effective machine learning approach for sentiment analysis of restaurant reviews
title_full	An effective machine learning approach for sentiment analysis of restaurant reviews
title_fullStr	An effective machine learning approach for sentiment analysis of restaurant reviews
title_full_unstemmed	An effective machine learning approach for sentiment analysis of restaurant reviews
title_sort	effective machine learning approach for sentiment analysis of restaurant reviews
publisher	BARC University
publishDate	2018
url	http://hdl.handle.net/10361/9023
work_keys_str_mv	AT karimrabita aneffectivemachinelearningapproachforsentimentanalysisofrestaurantreviews AT karimrabita effectivemachinelearningapproachforsentimentanalysisofrestaurantreviews
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An effective machine learning approach for sentiment analysis of restaurant reviews

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