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dc.contributor.advisorArif, Hossain
dc.contributor.authorKafi, Abdullahil
dc.contributor.authorAlam, Md. Shaikh Ashikul
dc.contributor.authorHossain, Sayeed Bin
dc.contributor.authorAwal, Siam Bin
dc.date.accessioned2018-12-20T05:57:49Z
dc.date.available2018-12-20T05:57:49Z
dc.date.copyright2018
dc.date.issued2018
dc.identifier.issnID 14301040
dc.identifier.otherID 14301019
dc.identifier.otherID 14301017
dc.identifier.otherID 14301061
dc.identifier.urihttp://hdl.handle.net/10361/11034
dc.descriptionThis thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 50-51).
dc.description.abstractThis 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.en_US
dc.description.statementofresponsibilityAbdullahil Kafi
dc.description.statementofresponsibilityMd. Shaikh Ashikul Alam
dc.description.statementofresponsibilitySayeed Bin Hossain
dc.description.statementofresponsibilitySiam Bin Awal
dc.format.extent51 pages
dc.language.isoenen_US
dc.rightsBRAC 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.
dc.subjectMobile phone ratingen_US
dc.subjectSentiment analysisen_US
dc.subject.lcshCell phones.
dc.subject.lcshMachine learning.
dc.titleFeature based mobile phone rating using sentiment analysis and machine learning approachesen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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