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Register a new userARAACOM: ARAbic Algerian Corpus for Opinion Mining
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Added by
Mahieddine Djoudi
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Authors
Zitouni Abdelhafid
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No Arabic abstract
Nowadays, it is no more needed to do an enormous effort to distribute a lot of forms to thousands of people and collect them, then convert this from into electronic format to track people opinion about some subjects. A lot of web sites can today reach a large spectrum with less effort. The majority of web sites suggest to their visitors to leave backups about their feeling of the site or events. So, this makes for us a lot of data which need powerful mean to exploit. Opinion mining in the web becomes more and more an attracting task, due the increasing need for individuals and societies to track the mood of people against several subjects of daily life (sports, politics, television,...). A lot of works in opinion mining was developed in western languages especially English, such works in Arabic language still very scarce. In this paper, we propose our approach, for opinion mining in Arabic Algerian news paper. CCS CONCEPTS $bullet$Information systems~Sentiment analysis $bullet$ Computing methodologies~Natural language processing
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Sentiment analysis is a highly subjective and challenging task. Its complexity further increases when applied to the Arabic language, mainly because of the large variety of dialects that are unstandardized and widely used in the Web, especially in social media. While many datasets have been released to train sentiment classifiers in Arabic, most of these datasets contain shallow annotation, only marking the sentiment of the text unit, as a word, a sentence or a document. In this paper, we present the Arabic Sentiment Twitter Dataset for the Levantine dialect (ArSenTD-LEV). Based on findings from analyzing tweets from the Levant region, we created a dataset of 4,000 tweets with the following annotations: the overall sentiment of the tweet, the target to which the sentiment was expressed, how the sentiment was expressed, and the topic of the tweet. Results confirm the importance of these annotations at improving the performance of a baseline sentiment classifier. They also confirm the gap of training in a certain domain, and testing in another domain.
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