Mostakim, MoinHasib, TamannaRahin, Saima Ahmed2018-02-222018-02-2220172017ID 17141017ID 13301117http://hdl.handle.net/10361/9542Cataloged from PDF version of thesis report.Includes bibliographical references (pages 35-37).This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.Sentiment analysis has become one of the most important tools in natural language processing, since it opens many possibilities to understand people’s opinions on different topics. Aspect-based sentiment analysis aims to take this a step further and find out, what exactly someone is talking about, and if he likes or dislikes it. Real world examples of perfect areas for this topic are the millions of available customer reviews in online shops. There have been multiple approaches to tackle this problem, using machine learning, deep learning and neural networks. However, currently the number of labelled reviews for training classifiers is very small. Therefore, we undertook multiple steps to research ways of improving ABSA performance on small datasets, by comparing recurrent and feed-forward neural networks and incorporating additional input data that was generated using different readily available NLP tools.37 pagesenBRAC University thesis reports 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.Sentiment analysisSemEvalAmazon datasetDependency parsingWord vectorsOpinion miningThesis