000			04322nam a22002537a 4500
008			210112b2015 a|||f mb|| 00| 0 eng d
040			_aEG-CaNU _cEG-CaNU
041	0		_aeng _beng
082			_a610
100	0		_aEmad Ashraf Samuel _9284
245	1		_aSentimento - Opinion Mining System for Product Review / _cEmad Ashraf Samuel
260			_c2015
300			_a107 p. _bill. _c21 cm.
500			_3Supervisor: Samhaa El-Beltagy
502			_aThesis (M.A.)—Nile University, Egypt, 2015 .
504			_a"Includes bibliographical references"
505	0		_aContents: CHAPTER 1: INTRODUCTION 1 1.1. Motivation ............................................................................................2 1.2. Background ..........................................................................................3 1.3. Problem Definition..............................................................................5 1.4. Proposed Solution ...............................................................................7 1.5. Methodology ........................................................................................8 1.6. Thesis Outline ....................................................................................11 CHAPTER 2: LITERATURE REVIEW 13 2.1 About Sentiment Analysis ...............................................................14 2.2 Aspect-Based Sentiment Analysis Research .................................22 2.3 Our Approach in Sentimento ..........................................................32 CHAPTER 3: SYSTEM ARCHITECTURE 35 3.1 What is Sentimento? .........................................................................36 3.2 Sentimento’s Architecture ...............................................................37 3.3 Sentimento’s Stages ..........................................................................38 CHAPTER 4: ASPECTS EXTRACTION 45 4.1 Aspect-Based Ontology ....................................................................46 4.2 Aspects Extraction and Categorization in Sentimento ................52 CHAPTER 5: SENTIMENT CLASSIFICATION 55 5.1 Opinion Lexicon ................................................................................57 5.2 Sentiment Classification in Sentimento .........................................60 iv CHAPTER 6: EVALUATION 65 6.1 Choosing Datasets For Evaluation .................................................67 6.2 SemEval Datasets ..............................................................................69 6.3 Evaluation of Aspects Extraction ....................................................71 6.4 Evaluation of Sentiment Classification ..........................................73 6.5 Evaluation of Other Parameters......................................................76 CHAPTER 7: CONCLUSION 79 7.1 Current Status of Sentimento ..........................................................80 7.2 Future Work .......................................................................................81 APPENDIX A – HOW TO USE SENTIMENTO 85 APPENDIX B – SAMPLES FROM EVALUATION DATASET 91 REFERENCES
520	3		_aAbstract: and more and more people are writing online reviews. These reviews are very important to give the real experience of products to potential customers and to the producing corporations. The number of reviews for some products may reach hundreds or even thousands. We need an automatic system that is able to summarize these reviews in a way that shows what exactly people like and dislike about the product. In this work, we develop Sentimento as an opinion mining system for online product reviews that is able to provide an aspect-based summary with accuracy close to an expert human reviewer and within an acceptable time. Sentimento is mainly divided to two tasks: aspects extraction and categorization, and opinions extraction and classification. The main idea is using ontology for the first task and opinion lexicon for the second task. Our experimental results using annotated test data on laptop reviews are promising for both tasks.
546			_aText in English, abstracts in English.
650		4	_aInformatics-IFM _9266
655		7	_2NULIB _aDissertation, Academic _9187
690			_aInformatics-IFM _9266
942			_2ddc _cTH
999			_c8840 _d8840