EMA at SemEval-2018 Task 1: Emotion Mining for Arabic
| Autor: | Obeida El Jundi, Alaa Khaddaj, Gilbert Badaro, Alaa Maarouf, Hazem Hajj, Raslan Kain, Wassim El-Hajj |
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| Rok vydání: | 2018 |
| Předmět: |
Normalization (statistics)
business.industry Arabic Computer science Sentiment analysis 02 engineering and technology computer.software_genre language.human_language SemEval Task (project management) 020204 information systems 0202 electrical engineering electronic engineering information engineering Feature (machine learning) language 020201 artificial intelligence & image processing Artificial intelligence business computer Word (computer architecture) Natural language processing |
| Zdroj: | SemEval@NAACL-HLT |
| DOI: | 10.18653/v1/s18-1036 |
| Popis: | While significant progress has been achieved for Opinion Mining in Arabic (OMA), very limited efforts have been put towards the task of Emotion mining in Arabic. In fact, businesses are interested in learning a fine-grained representation of how users are feeling towards their products or services. In this work, we describe the methods used by the team Emotion Mining in Arabic (EMA), as part of the SemEval-2018 Task 1 for Affect Mining for Arabic tweets. EMA participated in all 5 subtasks. For the five tasks, several preprocessing steps were evaluated and eventually the best system included diacritics removal, elongation adjustment, replacement of emojis by the corresponding Arabic word, character normalization and light stemming. Moreover, several features were evaluated along with different classification and regression techniques. For the 5 subtasks, word embeddings feature turned out to perform best along with Ensemble technique. EMA achieved the 1st place in subtask 5, and 3rd place in subtasks 1 and 3. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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