See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/319519309 SELCHI : Travel Profiling Technical Report · November 2013 DOI: 10.13140/RG.2.2.35156.91529 CITATIONS READS 0 89 6 authors, including: Nisansa de Silva Danaja Maldeniya University of Moratuwa University of Michigan 62 PUBLICATIONS 348 CITATIONS 16 PUBLICATIONS 50 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: SeMap - FYP View project Sensing Puns is its Own Reword: Automatic Detection of Paronomasia View project All content following this page was uploaded by Nisansa de Silva on 06 September 2017. The user has requested enhancement of the downloaded file. Final Year Project Report SELCHI Travel Profiling Department of Computer Science and Engineering University of Moratuwa Group Members Chiran Chathuranga (090067P) Eranga Mapa (090321P) Lasitha Wattaladeniya (090545F) Samith Dassanayake (090076R) Project Supervisors Mr. Nisansa de Silva Mr. Danaja Maldeniya Abstract In present days, with the evolution social media, people has become addicted to it social media. Due to that large amount textual information related to human activities are available in social network repositories. If these data can be used in a proper way, the outcome would be very powerful. In this project we are planning to use those data to structure and update travel domain data. The basic problems are to identify the trending places within time periods and trending geo locations. A system where it suggests users about trending travel locations according to their preferences within given time period would be the final outcome of this project. When considering the research, we have two major research areas. Main research area is domain specific information extraction from social networks. Other area is data mining. Under domain specific information extraction from social networks, we are referring the area of Natural Language Processing. Under that we are focused on POS tagging, Sentimental Analysis and Pattern-based extraction. ii Acknowledgements First and foremost we would like to thank our internal supervisor of this project. Mr. Nisansa De Silva, who linked us with this project which is under our areas of interests. He motivated and guided us on initiating this project. Mr. Danaja Maldeniya from Codegen International, being our external supervisor, and Dr. Upali Kohomban from Codegen International helped us realizing the value of this idea and directed us on correct path. We would like to convey our gratitude to them. Finally, we would like to thank our families and friends who were behind us and motivated us to perform well. iii Table of Content Abstract .......................................................................................................................................................... ii Acknowledgements ...................................................................................................................................... iii Table of Content ........................................................................................................................................... iv List of Figures ................................................................................................................................................ ix List of Tables ................................................................................................................................................. xi 1. Introduction ........................................................................................................................................... 1 1.1 Motivation ........................................................................................................................................... 1 1.2 Idea ...................................................................................................................................................... 2 1.3 Destination profiling ............................................................................................................................ 2 2. Literature Review ....................................................................................................................................... 3 2.1 Natural Language Processing ............................................................................................................... 3 2.1.1 Parsing........................................................................................................................................... 3 2.1.1.1 Stanford parser ...................................................................................................................... 3 2.1.1.2 RelEx....................................................................................................................................... 3 2.1.2 Part of Speech Tagging (POS) ....................................................................................................... 4 2.1.3 Sentiment Analysis........................................................................................................................ 5 2.1.4 Named Entity Recognition ............................................................................................................ 6 2.1.5 Chunking ....................................................................................................................................... 7 2.1.6 Training data selection ................................................................................................................. 8 2.1.7 Language Detection ...................................................................................................................... 8 2.1.8 Stemming ...................................................................................................................................... 8 2.1.9 Natural language understanding .................................................................................................. 9 2.1.10 Anaphora resolution ................................................................................................................... 9 2.2 Ontology ............................................................................................................................................ 11 2.2.1 Introduction ................................................................................................................................ 11 2.2.1.1 Individuals ............................................................................................................................ 12 2.2.1.2 Classes .................................................................................................................................. 12 2.2.1.2 Attributes ............................................................................................................................. 12 2.2.1.2 Relationships ........................................................................................................................ 12 2.2.2 Why an Ontology? ...................................................................................................................... 12 iv 2.2.3 Protégé........................................................................................................................................ 13 2.3 Social Data Mining ............................................................................................................................. 13 2.3.1 Introduction ................................................................................................................................ 13 2.3.2 Data Mining ................................................................................................................................ 14 2.3.2.1 Supervised............................................................................................................................ 14 2.3.2.2 Unsupervised ....................................................................................................................... 14 2.3.2.3 Semi-supervised ................................................................................................................... 14 2.3.3 Mining Social Media ................................................................................................................... 15 2.3.4 Community Analysis ................................................................................................................... 17 2.3.5 Opinion Mining ........................................................................................................................... 18 2.3.5.1 Model of Opinion Mining .................................................................................................... 18 2.3.5.2 Model of Feature-Based Opinion Mining ............................................................................ 19 2.3.6. Trend Mining.............................................................................................................................. 20 2.3.6.1 Trend Analyzing. .................................................................................................................. 20 2.3.6.2 Non Symantec Trending Patterns ........................................................................................ 20 2.3.6.2 Trend Pattern Ontology. .....................................................................................................