Building an Arabic Sentiment Lexicon Using Semi-supervised Learning

Journal of King Saud University: Computer and Information Sciences

Mohamed Y. Dahab¹  Muazzam A. Siddiqui¹  Fawaz H.H. Mahyoub¹ 

[1] Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia;

关键词: Sentiment lexicon;    Sentiment analysis;    Arabic natural language processing;    Text mining;    Semi-supervised learning;   

DOI  :  10.1016/j.jksuci.2014.06.003

来源: DOAJ

【 摘 要 】

Sentiment analysis is the process of determining a predefined sentiment from text written in a natural language with respect to the entity to which it is referring. A number of lexical resources are available to facilitate this task in English. One such resource is the SentiWordNet, which assigns sentiment scores to words found in the English WordNet. In this paper, we present an Arabic sentiment lexicon that assigns sentiment scores to the words found in the Arabic WordNet. Starting from a small seed list of positive and negative words, we used semi-supervised learning to propagate the scores in the Arabic WordNet by exploiting the synset relations. Our algorithm assigned a positive sentiment score to more than 800, a negative score to more than 600 and a neutral score to more than 6000 words in the Arabic WordNet. The lexicon was evaluated by incorporating it into a machine learning-based classifier. The experiments were conducted on several Arabic sentiment corpora, and we were able to achieve a 96% classification accuracy.

【 授权许可】

Unknown