Abstract
Research done on Arabic sentiment analysis is considered very limited almost in its early steps compared to other languages like English whether at document-level or sentence-level. In this paper, we test the effect of preprocessing (normalization, stemming, and stop words removal) on the performance of an Arabic sentiment analysis system using Arabic tweets from twitter. The sentiment (positive or negative) of the crawled tweets is analyzed to interpret the attitude of the public with regards to topic of interest. Using Twitter as the main source of data reflects the importance of the system for the Middle East region, which mostly speaks Arabic.- Anthology ID:
- 2012.amta-caas14.7
- Volume:
- Fourth Workshop on Computational Approaches to Arabic-Script-based Languages
- Month:
- November 1
- Year:
- 2012
- Address:
- San Diego, California, USA
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 47–56
- Language:
- URL:
- https://aclanthology.org/2012.amta-caas14.7
- DOI:
- Cite (ACL):
- Amira Shoukry and Ahmed Rafea. 2012. Preprocessing Egyptian Dialect Tweets for Sentiment Mining. In Fourth Workshop on Computational Approaches to Arabic-Script-based Languages, pages 47–56, San Diego, California, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Preprocessing Egyptian Dialect Tweets for Sentiment Mining (Shoukry & Rafea, AMTA 2012)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2012.amta-caas14.7.pdf