We Know Where You Are Tweeting From: Assigning a Type of Place to Tweets Using Natural Language Processing and Random Forests

摘要

Identifying the type of the place a user is tweeting from is important for many business and social applications, e.g., user profiles can help local businesses identify current and potential clients and their interests. We used Random Forest to identify six location categories. They are active life, eating out, hotels, nightlife, shopping, and shows. We evaluated 16 features for use in classification. The features are generated from the textual contents in the tweet, the metadata associated with the tweet, and the geographical area the user is tweeting from. We trained our classifier by analyzing 43,149 reviews from Yelp and by examining two twitter datasets. The first is an original dataset consisting of 6,359 tweets and the second is a stratified one containing 2,400 tweets uniformly distributed between the six categories. We evaluated our approach by creating a gold standard. Using 60% of our tweets for training and 40% for testing, our approach classified 74% of tweets in the original dataset, and 77% of tweets in the stratified dataset, correctly with the right location category. The results could be beneficial for research and business.