Name
Abstract | ||
|---|---|---|
Sentiment analysis on Twitter data has attracted much attention recently. 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. In this paper, we develop a novel method to harvest sentiment knowledge in the MapReduce framework. Our algorithm exploits the hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification procedure of diverse 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5220/0005850401400147 | PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND TECHNOLOGIES, VOL 1 (WEBIST) |
Keywords | Field | DocType |
Big Data, Bloom Filters, Classification, MapReduce, Hadoop, Sentiment Analysis, Twitter | Data science,Data mining,World Wide Web,Sentiment analysis,Computer science,Big data | Conference |
Citations | PageRank | References |
1 | 0.36 | 12 |
Authors | ||
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 |