Analyzing User Tweets around foursquare checkins

Project idea

Recently there has been a massive increase in the usage of location sharing social networks. Social networks such as FourSquare have brought a new way of social interaction where in an user checks in to a physical location (Food, College & University, Nightlife Spots etc). FourSquare allows the user checkins to be published as tweets. We plan to analyze the tweeting behaviour of the user after their foursquare checkin.

Team

• Rajarshi Das

• Ranjitha Gurunath Kulkarni

Data

We have data for tweets over a week for around 300,000 users over the world. We expect that there will be significant number of foursquare checkins in the tweets. As a starting point we will start our analysis on this data and once we have a proof of concept we will start gathering more data. The present data has been generously shared to us by Hazim Almuhimedi, a Phd student of Institute of Software Research at CMU.

Tasks

• For a user, we plan to analyze tweets (within a small interval) after their foursquare check-in, to see if the user talks about things related to the places in which he/she has checked in.
• Analyzing all the tweets that follow foursquare check-in to a particular place (or category), to see what percentage of the users do tweet about that place.
• Find out the topics that users mostly talk about when they are at a particular place.
• Once we have all the tweets about a particular place, analyze the overall sentiment about that place. (For example, a particular restaurant is liked by most people or not).

Note : We will be able to do that if we find out that a significant number of people actually tweet about a place they are in after checking into that place.

Evaluation

• Quantitative: Build a small annotated test dataset to evaluate the accuracy of our prediction.
• Qualitative : For sentiment analysis on restaurant tweets, we will see if the overall sentiment correlates with the ratings on other famous social networks like Yelp.

Key Technical Challenges

• We might not have sufficient amount of data if we narrow to a single location (for example a particular restaurant)
• Given the limited amount of data, we are not sure if we can do topic modelling accurately (since tweets are inherently short)