Sentiment Analysis For Modern Standard Arabic And Colloquial

TL;DR

This paper presents a feature-based approach for Arabic sentiment analysis, utilizing an expanded lexicon and linguistic features to achieve over 95% accuracy on MSA and dialectal Arabic social media data.

Contribution

It introduces an automatic, expandable sentiment lexicon for Arabic and incorporates novel linguistic features to improve sentence-level sentiment classification accuracy.

Findings

Achieved over 95% accuracy in sentiment classification.

Developed an automatic method for expanding Arabic sentiment lexicons.

Enhanced detection of sentiment polarity using idioms and syntactic features.

Abstract

The rise of social media such as blogs and social networks has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations, therefore many are now looking to the field of sentiment analysis. In this paper, we present a feature-based sentence level approach for Arabic sentiment analysis. Our approach is using Arabic idioms/saying phrases lexicon as a key importance for improving the detection of the sentiment polarity in Arabic sentences as well as a number of novels and rich set of linguistically motivated features contextual Intensifiers, contextual Shifter and negation handling), syntactic features for conflicting phrases which enhance the sentiment classification accuracy. Furthermore, we introduce an automatic expandable wide coverage polarity lexicon of Arabic sentiment words. The lexicon is built with gold-standard sentiment words as a seed which is manually collected and annotated and it expands and detects the sentiment orientation automatically of new sentiment words using synset aggregation technique and free online Arabic lexicons and thesauruses. Our data focus on modern standard Arabic (MSA) and Egyptian dialectal Arabic tweets and microblogs (hotel reservation, product reviews, etc.). The experimental results using our resources and techniques with SVM classifier indicate high performance levels, with accuracies of over 95%.