Feature based mobile phone rating using sentiment analysis and machine learning approaches
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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| स्वरूप: | थीसिस |
| भाषा: | English |
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2018
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| ऑनलाइन पहुंच: | http://hdl.handle.net/10361/11034 |
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10361-11034 |
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10361-110342022-01-26T10:15:58Z Feature based mobile phone rating using sentiment analysis and machine learning approaches Kafi, Abdullahil Alam, Md. Shaikh Ashikul Hossain, Sayeed Bin Awal, Siam Bin Arif, Hossain Department of Computer Science and Engineering, BRAC University Mobile phone rating Sentiment analysis Cell phones. Machine learning. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 50-51). This project proposes a model of sentiment analysis of different features of different company’s mobile sets and rating them overall. Customers before buying a phone check reviews to get a better understanding of the device and this project derives an optimum solution for this. In this model, every feature of a mobile phone is rated based on public opinion and an overall rating for every type. Amazon is one of the largest internet retailer, which makes way for most public reviews on their products and so we collected data for sentiment analysis from amazon. We pre-processed the gathered data to a supervised form and chose the most common features from train data. In our model, Naïve Bayes, Support Vector Machine, Logistic Regression, Stochastic Gradient Descent and Random Forest algorithms were used to compare performance. These classification algorithms were trained with the training data and tested with test dataset to determine the accuracy of the classifiers. Our model provides an average polarity of each features and an average polarity of the mobile phone which will give a rating of the device, thus assisting the customers to choose the best according to their desire. This project can work as an assistant for the customers to determine their device following the opinion of the other users of the device. Abdullahil Kafi Md. Shaikh Ashikul Alam Sayeed Bin Hossain Siam Bin Awal B. Computer Science and Engineering 2018-12-20T05:57:49Z 2018-12-20T05:57:49Z 2018 2018 Thesis ID 14301040 ID 14301019 ID 14301017 ID 14301061 http://hdl.handle.net/10361/11034 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. 51 pages application/pdf |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Mobile phone rating Sentiment analysis Cell phones. Machine learning. |
| spellingShingle |
Mobile phone rating Sentiment analysis Cell phones. Machine learning. Kafi, Abdullahil Alam, Md. Shaikh Ashikul Hossain, Sayeed Bin Awal, Siam Bin Feature based mobile phone rating using sentiment analysis and machine learning approaches |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Arif, Hossain |
| author_facet |
Arif, Hossain Kafi, Abdullahil Alam, Md. Shaikh Ashikul Hossain, Sayeed Bin Awal, Siam Bin |
| format |
Thesis |
| author |
Kafi, Abdullahil Alam, Md. Shaikh Ashikul Hossain, Sayeed Bin Awal, Siam Bin |
| author_sort |
Kafi, Abdullahil |
| title |
Feature based mobile phone rating using sentiment analysis and machine learning approaches |
| title_short |
Feature based mobile phone rating using sentiment analysis and machine learning approaches |
| title_full |
Feature based mobile phone rating using sentiment analysis and machine learning approaches |
| title_fullStr |
Feature based mobile phone rating using sentiment analysis and machine learning approaches |
| title_full_unstemmed |
Feature based mobile phone rating using sentiment analysis and machine learning approaches |
| title_sort |
feature based mobile phone rating using sentiment analysis and machine learning approaches |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/11034 |
| work_keys_str_mv |
AT kafiabdullahil featurebasedmobilephoneratingusingsentimentanalysisandmachinelearningapproaches AT alammdshaikhashikul featurebasedmobilephoneratingusingsentimentanalysisandmachinelearningapproaches AT hossainsayeedbin featurebasedmobilephoneratingusingsentimentanalysisandmachinelearningapproaches AT awalsiambin featurebasedmobilephoneratingusingsentimentanalysisandmachinelearningapproaches |
| _version_ |
1814308538678247424 |