Sentiment Classification of News Article Dissertation Submitted in partial fulfillment of requirements for the degree of Master of Technology in Computer Engineering By Kiran S. Doddi MIS No: 121222013 Under the guidance of Dr. Mrs. Y. V. Haribhakta Department of Computer Engineering and Information Technology College of Engineering Pune Shivaji Nagar, Pune – 411005 2013-14 i GoodBook DEPARTMENT OF COMPUTER ENGINEERING AND INFORMATION TECHNOLOGY, COLLEGE OF ENGINEERING, PUNE CERTIFICATE This is to certify that dissertation titled “Sentiment Classification of News Article” has been successfully By Kiran Shriniwas Doddi (121222013) And is approved for the degree of Master of Technology in Computer Engineering Dr. Mrs. Y. V. Haribhakta Dr. J. V. Aghav Project Guide, Head of Department, Department of Computer Engineering Department of Computer Engineering And Information Technology, And Information Technology, College of Engineering, Pune, College of Engineering, Pune, Shivaji Nagar, Pune-411005 Shivaji Nagar, Pune-411005 ii GoodBook Acknowledgments I express my sincere gratitude towards my guide Dr. Mrs. Y. V. Haribhakta for her constant help, encouragement and inspiration till date the project work. Without her invaluable guidance, this work would never have been a Reached to this level. I would also like to thank all the faculty members and staff of Computer and IT department for providing us ample facility and flexibility and for making my journey of post-graduation successful. Last, but not the least, I would like to thank my classmates for their valuable suggestions and helpful discussions. I am thankful to them for their unconditional support and help throughout the year. Doddi Kiran Shriniwas College of Engineering, Pune-5 iii GoodBook Abstract The advent use of new online social media such as articles, blogs, message boards, news channels, and in general Web content has dramatically changed the way people look at various things around them. Today, it’s a daily practice for many people to read news online. People's perspective tends to undergo a change as per the news content they read. The majority of the content that we read today is on the negative aspects of various things e.g. corruption, rapes, thefts etc. Reading such news is spreading negativity amongst the people. Positive news seems to have gone into a hiding. The positivity surrounding the good news has been drastically reduced by the number of bad news. This has made a great practical use of Sentiment Analysis and there has been more innovation in this area in recent era. Sentiment analysis refers to a broad range of fields of text mining, natural language processing and computational linguistics. It traditionally emphasizes on classification of text document into positive and negative categories. Sentiment analysis of any text document has emerged as the most useful application in the area of sentiment analysis. The objective of this project is to provide a platform for serving good news and create a positive environment. This is achieved by finding the sentiments of the news articles and filtering out the negative articles which carry negative sentiments. This would enable us to focus only on the good news which will help spread positivity around society and would allow people to think positively. To achieve our objective, we have built our own algorithm for classification of News articles. This includes data aggregator tool and processing engine at the server side as a Sentiment classifier and a platform for user where positive news being served to read and this is a GoodBook. Index Terms- Document classification, SentiWordNet, Sentiment Analysis, Support vector machine (SVM) iv GoodBook Contents Abstract ........................................................................................................................... iv Contents ........................................................................................................................... v List of Figures ................................................................................................................. vii List of Tables ................................................................................................................. viii 1. Introduction............................................................................................................... 1 1.1 Text Mining........................................................................................................... 2 1.2 Text Categorization................................................................................................ 3 1.3 Sentiment Analysis ................................................................................................ 4 2. Literature Survey ...................................................................................................... 6 2.1 Classification methods and Feature extraction ......................................................... 7 2.2 Model building: ..................................................................................................... 8 2.3 Approaches used in Sentiment Analysis.................................................................. 8 2.3.1 Stop words removal and Stemming ................................................................ 8 2.3.2 SentiWordNet ............................................................................................... 9 2.3.3 POS tagger ...................................................................................................10 2.3.4 Feature Extraction& document classification .................................................11 2.3.5 Support Vector Machine (SVM) ...................................................................13 2.4 Data set ................................................................................................................16 3. Proposed System.......................................................................................................17 3.1 Problem System....................................................................................................17 3.2 Objective ..............................................................................................................17 3.3 Proposed Solution .................................................................................................18 3.3.1 System Block Diagram .................................................................................18 3.3.2 Methodology................................................................................................18 3.4 System Requirement Specification ........................................................................19 4. Design and Implementation......................................................................................20 4.1 News Aggregator ..................................................................................................20 4.2 Processing Engine.................................................................................................22 4.3 Social sharing flow ...............................................................................................26 5. Experiment & Results...............................................................................................30 v GoodBook 6. Conclusion and Future scope....................................................................................31 6.1 Conclusion ...........................................................................................................35 6.2 Future Scope ........................................................................................................35 Appendix A ......................................................................................................................36 References........................................................................................................................38 vi GoodBook List of Figures Figure 1: General Text mining process ..................................................................................... 2 Figure 2: General Text categorization process .......................................................................... 3 Figure 3: General architecture of the general sentiment analysis system..................................... 5 Figure 4: Separating hyperplanes ........................................................................................... 13 Figure 5: Maximal separating hyper planes............................................................................. 14 Figure 6: System Block Diagram............................................................................................ 18 Figure 7: RSS Feed reader ..................................................................................................... 20 Figure 8: Graph of Result of SVM algorithm with RBFKernel ................................................ 33 Figure 9: Graph of Result of SVM algorithm with PolyKernel ................................................ 34 vii GoodBook List of Tables Table 4.1: RSS Feeds List .......................................................................................................21 Table 5.2: Confusion matrix for Classification Model ............................................................. 30 Table 5.3: Result for adverb and verb scaling factor ................................................................ 31 Table 5.4.1: Result of SVM algorithm with RBFKernel .........................................................