Name
Abstract | ||
|---|---|---|
SVM technique is one of the best techniques to classify data, but it has a slow performance in the big data arrays. This paper introduces the method to improve the speed of SVM classification in sentiment analysis by reducing the training set. The method was tested on the Stanford Twitter sentiment corpus dataset and Amazon customer reviews dataset. The results show that the execution time of the introduced method outperforms the standard SVM classification method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-99972-2_18 | Communications in Computer and Information Science |
Keywords | Field | DocType |
SVM,Big data arrays,Sentiment analysis | Training set,Sentiment analysis,Computer science,Support vector machine,Customer reviews,Execution time,Artificial intelligence,Big data,Machine learning | Conference |
Volume | ISSN | Citations |
920 | 1865-0929 | 1 |
PageRank | References | Authors |
0.36 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantinas Korovkinas | 1 | 1 | 0.36 |
| Paulius Danenas | 2 | 35 | 5.07 |
| Gintautas Garsva | 3 | 41 | 4.95 |