DATA ANALYSIS OF TWITTER FEEDS ON GOOGLE MAPS

Abstract: With increasing popularity in microblogging sites, we are in the era of information explosion. As of June 2011, about 200 million tweets are being generated every day. Spurred by that growth, companies and media organizations are increasingly seeking ways to mine Twitter for information about what people think and feel about their products and services. One advantage of this data, over previously used data-sets, is that the tweets are collected in a streaming fashion and therefore represent a true sample of actual tweets in terms of language use and content. Although Twitter provides a list of most popular topics people tweet about known as trending topics in real time, it is often hard to understand what these trending topics are about and it becomes difficult to classify them based on the emotions that each tweet is trying to portray. Therefore a question arises as to what happens when you combine the social networking power of Twitter with the usefulness of Google Maps? The result can contain some rather innovative and intriguing applications. With Twitter and Google Maps mashup's here, it's all about taking advantage of both the Google Maps and Twitter API to create a mashup that's unique, practical, and highly efficient. The aim of our project is to display real time globally trending tweets on google maps and perform sentiment analysis on the tweets related to the topic.This paper primarily focuses on the trend detection and sentiment analysis techniques and the applications of using a Twitter and Google maps mashup. Further, we analyze the sentiments expressed within a particular sentence, paragraph or document etc. The analysis based on sentiments can pave way for automatic trend analysis, topic recognition and opinion mining etc. Furthermore, we can fairly estimate the degree of positivity and negativity of the opinions and sentiments based on the content obtained from a particular social media.

Keywords: Trend Analysis, Sentiment Analysis, Data analysis, Geocoding, microblogs, trend detection, Twitter, Google maps.