SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model
| Autor: | S Milton Rajendram, Angel Deborah S, T. T. Mirnalinee |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Smoothness (probability theory) business.industry Computer science Sentiment analysis Pattern recognition 02 engineering and technology Machine learning computer.software_genre SemEval Task (project management) symbols.namesake Kernel (linear algebra) 020901 industrial engineering & automation Kriging Kernel (statistics) 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Artificial intelligence business Gaussian process computer |
| Zdroj: | SemEval@ACL |
| DOI: | 10.18653/v1/s17-2139 |
| Popis: | The system developed by the SSN_MLRG1 team for Semeval-2017 task 5 on fine-grained sentiment analysis uses Multiple Kernel Gaussian Process for identifying the optimistic and pessimistic sentiments associated with companies and stocks. Since the comments made at different times about the same companies and stocks may display different emotions, their properties such as smoothness and periodicity may vary. Our experiments show that while single kernel Gaussian Process can learn certain properties well, Multiple Kernel Gaussian Process are effective in learning the presence of different properties simultaneously. |
| Databáze: | OpenAIRE |
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