دانلود رایگان مقاله چارچوبی برای زمان واقعی تجزیه و تحلیل داده های توییتر

Abstract

Twitter is a popular social network which allows millions of users to share their opinions on what happens all over the world. In this work we present a system for real-time Twitter data analysis in order to follow popular events from the user’s perspective. The method we propose extends and improves the Soft Frequent Pattern Mining (SFPM) algorithm by overcoming its limitations in dealing with dynamic, real-time, detection scenarios. In particular, in order to obtain timely results, the stream of tweets is organized in dynamic windows whose size depends both on the volume of tweets and time. Since we aim to highlight the user’s point of view, the set of keywords used to query Twitter is progressively refined to include new relevant terms which reflect the emergence of new subtopics or new trends in the main topic. The real-time detection system has been evaluated during the 2014 FIFA World Cup and experimental results show the effectiveness of our solution.

5. Conclusion

In this work we presented a framework for real-time analysis of Twitter data in order to detect relevant topics discussed by the users. The analysis of the state of the art suggested us to start from an existing technique, i.e. SFPM, which seemed to provide promising results in offline detection scenarios. Then we designed some improvements to SFPM which allowed to use it for real-time detection of social events. We run tests on a dataset collected during the FIFA World Cup 2014 and aimed at evaluating the effectiveness of our solution compared with a basic SFPM approach and two real-time systems.