Abstract
In this paper, we describe a methodology to develop a large training set for sentiment analysis automatically. We extract Arabic tweets and then annotates them for negativeness and positiveness sentiment without human intervention. These annotated tweets are used as a training data set to build our experimental sentiment analysis by using Naive Bayes algorithm and TF-IDF enhancement. The large size of training data for a highly inflected language is necessary to compensate for the sparseness nature of such languages. We present our techniques and explain our experimental system. We use 200 thousand annotated tweets to train our system. The evaluation shows that our sentiment analysis system has high precision and accuracy measures compared to existing ones.
Highlights
Opinions are difficult to extract and search using the current information retrieval relevancy methods
Concerning Arabic sentiment analysis, machine learning techniques are used more than other approaches such as Lexicon based and hybrid techniques
Other researchers have recently processing text collected from social media and designed to handle both Arabic dialects as well as MSA text
Summary
Opinions are difficult to extract and search using the current information retrieval relevancy methods. It is even more challenging for languages with little resources such as Arabic. Sentiment analysis is becoming an important mean which helps by automating the process of extracting the opinions from diverse content channels. To handle the richness of Arabic and its inflection nature, we need a training set consisting of thousands of tweets that are rich with negative and positive terms. Such a training dataset does not exist [3, 4]. We present the results of the evaluation of our sentiment analysis system
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