Spatial-Temporal Political Event Analysis Based On Social Web

This thesis presents an approach towards bilingual sentiment analysis of short messages data retrieved from twitter. Social networks evolved to become one of the most advanced and widespread communication medium of the modern era. They also serve as multi-cultural and multi-lingual information centers. Information analysis of these social networks can help in designing better government and commercial policies on local, national and international platforms. A number of behavioral and demographic oriented analytical studies reported that use data from social networks; however, most of these studies are focused towards English language. Despite being spoken by almost 350 million population (6% of world’s population), Hindi and its sister languages (Urdu) lack extensive work on such sentiment analysis. This proposed research focused on sentiment analysis of bi-lingual dataset, composed of English and Roman-Urdu tweet messages, on subject General Elections 2013 in Pakistan. A bilingual sentiment lexicon (BSL) is semi-automatically created for assigning sentiment strengths to the short messages (tweets). In order to provide maximum lexicon coverage for sentiment analysis, other linguistic resources such as WordNet have been involved. Proposed lexicon is used to measure the popularity of four major political parties on twitter. The proposed system yield promising results with 76 % accuracy in tweet’s sentiment strength classification.