Sentiment analysis on yelp reviews

Sentiment analysis, also known as opinion mining, is used to systematically identify, quantify, and study affective states expressed by someone towards a topic or phenomenon. In this project, sentiment analysis will be applied to determine the text polarity of reviews on various businesses left by Y...

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Main Author: Wong, Hong Yong
Other Authors: Sun Aixin
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/148120
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1481202021-04-23T15:05:24Z Sentiment analysis on yelp reviews Wong, Hong Yong Sun Aixin School of Computer Science and Engineering AXSun@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Document and text processing Sentiment analysis, also known as opinion mining, is used to systematically identify, quantify, and study affective states expressed by someone towards a topic or phenomenon. In this project, sentiment analysis will be applied to determine the text polarity of reviews on various businesses left by Yelp users. Various supervised machine learning classifiers namely Naïve Bayes, Random Forest, Neural Networks, Support Vector Machine and Logistic Regression have been used to label reviews by performing text classification. These traditional classifiers will be pitted up against deep learning models and compared. They will be tasked with classifying Yelp reviews into five different ratings, and also three different labels according to the rating given by the user: - positive, neutral and negative. Negations have the ability to change the polarity of a given text and must be taken into account when performing sentiment analysis. The goal of this project is to analyze and observe the correlation between the use of negations in a Yelp review and the rating the user provided. The attention score of each token in the review text will be derived from two different deep learning models and compared. Bachelor of Engineering (Computer Science) 2021-04-23T15:05:24Z 2021-04-23T15:05:24Z 2021 Final Year Project (FYP) Wong, H. Y. (2021). Sentiment analysis on yelp reviews. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148120 https://hdl.handle.net/10356/148120 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Document and text processing
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Document and text processing
Wong, Hong Yong
Sentiment analysis on yelp reviews
description Sentiment analysis, also known as opinion mining, is used to systematically identify, quantify, and study affective states expressed by someone towards a topic or phenomenon. In this project, sentiment analysis will be applied to determine the text polarity of reviews on various businesses left by Yelp users. Various supervised machine learning classifiers namely Naïve Bayes, Random Forest, Neural Networks, Support Vector Machine and Logistic Regression have been used to label reviews by performing text classification. These traditional classifiers will be pitted up against deep learning models and compared. They will be tasked with classifying Yelp reviews into five different ratings, and also three different labels according to the rating given by the user: - positive, neutral and negative. Negations have the ability to change the polarity of a given text and must be taken into account when performing sentiment analysis. The goal of this project is to analyze and observe the correlation between the use of negations in a Yelp review and the rating the user provided. The attention score of each token in the review text will be derived from two different deep learning models and compared.
author2 Sun Aixin
author_facet Sun Aixin
Wong, Hong Yong
format Final Year Project
author Wong, Hong Yong
author_sort Wong, Hong Yong
title Sentiment analysis on yelp reviews
title_short Sentiment analysis on yelp reviews
title_full Sentiment analysis on yelp reviews
title_fullStr Sentiment analysis on yelp reviews
title_full_unstemmed Sentiment analysis on yelp reviews
title_sort sentiment analysis on yelp reviews
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148120
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