Opinion based intelligent recommender system

The study of sentiment analysis on social media posts can be used to analyse human emotions towards certain brands, topics, or products. Collaborative Filtering (CF) is a technique used to create personalized recommendations based on the preferences of other similar users. In this paper, we proposed...

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Main Author: Yuen, Jing Wen
Other Authors: Li Fang
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
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Online Access:https://hdl.handle.net/10356/138096
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1380962020-04-24T02:57:23Z Opinion based intelligent recommender system Yuen, Jing Wen Li Fang School of Computer Science and Engineering Agency for Science, Technology and Research (A*STAR) Wang Zhaoxia ASFLi@ntu.edu.sg; zhxwang720101@hotmail.com Engineering::Computer science and engineering The study of sentiment analysis on social media posts can be used to analyse human emotions towards certain brands, topics, or products. Collaborative Filtering (CF) is a technique used to create personalized recommendations based on the preferences of other similar users. In this paper, we proposed a system design to incorporate sentiment analysis and CF to come up with a recommender system based on texts on social media. Sentiment analysis was performed using various forms of two machine learning models — Multinomial Naïve Bayes and Long short-term memory (LSTM) to learn users’ sentiments towards different products. User-user CF was then applied to estimate the ratings of unseen products for users based on other users with similar tastes. Products with top predicted scores were then be recommended to users. Our finding had shown that the proposed Multinomial Naïve Bayes model using Term Frequency – Inverse Document Frequency (TF-IDF) was most effective in classifying human sentiments towards a product. Our user-user CF method allowed more relevant product recommendations to users since it was derived based on users who had similar tastes like them. This project can be extrapolated to real-life applications, such as e- commerce recommender systems, by recommending products to users based on their social media content. Bachelor of Engineering (Computer Engineering) 2020-04-24T02:57:22Z 2020-04-24T02:57:22Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138096 en SCSE 19-0124 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Yuen, Jing Wen
Opinion based intelligent recommender system
description The study of sentiment analysis on social media posts can be used to analyse human emotions towards certain brands, topics, or products. Collaborative Filtering (CF) is a technique used to create personalized recommendations based on the preferences of other similar users. In this paper, we proposed a system design to incorporate sentiment analysis and CF to come up with a recommender system based on texts on social media. Sentiment analysis was performed using various forms of two machine learning models — Multinomial Naïve Bayes and Long short-term memory (LSTM) to learn users’ sentiments towards different products. User-user CF was then applied to estimate the ratings of unseen products for users based on other users with similar tastes. Products with top predicted scores were then be recommended to users. Our finding had shown that the proposed Multinomial Naïve Bayes model using Term Frequency – Inverse Document Frequency (TF-IDF) was most effective in classifying human sentiments towards a product. Our user-user CF method allowed more relevant product recommendations to users since it was derived based on users who had similar tastes like them. This project can be extrapolated to real-life applications, such as e- commerce recommender systems, by recommending products to users based on their social media content.
author2 Li Fang
author_facet Li Fang
Yuen, Jing Wen
format Final Year Project
author Yuen, Jing Wen
author_sort Yuen, Jing Wen
title Opinion based intelligent recommender system
title_short Opinion based intelligent recommender system
title_full Opinion based intelligent recommender system
title_fullStr Opinion based intelligent recommender system
title_full_unstemmed Opinion based intelligent recommender system
title_sort opinion based intelligent recommender system
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/138096
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