Name
Title | ||
|---|---|---|
Online Multiscale-Data Classification Based On Multikernel Adaptive Filtering With Application To Sentiment Analysis |
Abstract | ||
|---|---|---|
We present an online method for multiscale data classification, using the multikernel adaptive filtering framework. The target application is Twitter sentiment analysis, which is a notoriously challenging task of natural language processing. This is because (i) each tweet is typically short, and (ii) domain-specific expressions tend to be used. The efficacy of the proposed multiscale online method is studied with dataset of Twitter. Simulation results show that the proposed approach achieves a higher F1 score than the other online-classification methods, and also outperforms the nonlinear support vector machine. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/EUSIPCO.2019.8902958 | 2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) |
Keywords | Field | DocType |
reproducing kernel, sentiment analysis, online learning | F1 score,Nonlinear system,Expression (mathematics),Sentiment analysis,Computer science,Support vector machine,Multikernel,Artificial intelligence,Adaptive filter,Data classification,Machine learning | Conference |
ISSN | Citations | PageRank |
2076-1465 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ran Iwamoto | 1 | 0 | 0.34 |
| Masahiro Yukawa | 2 | 272 | 30.44 |