Name
Abstract | ||
|---|---|---|
Sentiment analysis on Twitter data has attracted much attention 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 oers huge potential and can be harnessed to receive the sentiment tendency towards these topics. However, since none can invest an innite amount of time to read through these tweets, an automated decision making approach is necessary. Nevertheless, 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 representative to dene the sentiment polarity towards a topic due to the massive number of tweets published daily. In this paper, 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 sentiment labels, and proceeds to a classication procedure of diverse sentiment types in a parallel and distributed manner. Moreover, we utilize Bloom lters 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 ecient, robust and scalable and conrm the quality of our sentiment identication. |
Year | Venue | Field |
|---|---|---|
2016 | EDBT/ICDT Workshops | World Wide Web,Spark (mathematics),Information retrieval,Sentiment analysis,Computer science,Exploit,Immediacy,Scalability |
DocType | Citations | PageRank |
Conference | 5 | 0.45 |
References | Authors | |
21 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikolaos Nodarakis | 1 | 23 | 6.00 |
| Spyros Sioutas | 2 | 206 | 77.88 |
| Athanasios K. Tsakalidis | 3 | 544 | 117.52 |
| Giannis Tzimas | 4 | 111 | 28.31 |