Large Scale Sentiment Analysis on Twitter with Spark.

Abstract

Sentiment analysis on Twitter data has attracted much at-tention recently. One of the system’s key features, is the immediacy in communication with other users in an easy, user-friendly and fast way. Consequently, people tend to express their feelings freely, which makes Twitter an ideal source for accumulating a vast amount of opinions towards a wide diversity of topics. This amount of information offers huge potential and can be harnessed to receive the sentiment tendency towards these topics. However, since none can in-vest an infinite amount of time to read through these tweets, an automated decision making approach is necessary. Nev-ertheless, most existing solutions are limited in centralized environments only. Thus, they can only process at most a few thousand tweets. Such a sample, is not representa-tive to define the sentiment polarity towards a topic due to the massive number of tweets published daily. In this pa-per, we go one step further and develop a novel method for sentiment learning in the Spark framework. Our algorithm exploits the hashtags and emoticons inside a tweet, as senti-ment labels, and proceeds to a classification procedure of di-verse sentiment types in a parallel and distributed manner. Moreover, we utilize Bloom filters to compact the storage size of intermediate data and boost the performance of our algorithm. Through an extensive experimental evaluation, we prove that our solution is efficient, robust and scalable and confirm the quality of our sentiment identification.