Idioms-Proverbs Lexicon for Modern Standard Arabic and Colloquial Sentiment Analysis

Although, the fair amount of works in sentiment analysis (SA) and opinion mining (OM) systems in the last decade and with respect to the performance of these systems, but it still not desired performance, especially for morphologically-Rich Language (MRL) such as Arabic, due to the complexities and challenges exist in the nature of the languages itself. One of these challenges is the detection of idioms or proverbs phrases within the writer text or comment. An idiom or proverb is a form of speech or an expression that is peculiar to itself. Grammatically, it cannot be understood from the individual meanings of its elements and can yield different sentiment when treats as separate words. Consequently, In order to facilitate the task of detection and classification of lexical phrases for automated SA systems, this paper presents AIPSeLEX a novel idioms/ proverbs sentiment lexicon for modern standard Arabic (MSA) and colloquial. AIPSeLEX is manually collected and annotated at sentence level with semantic orientation (positive or negative). The efforts of manually building and annotating the lexicon are reported. Moreover, we build a classifier that extracts idioms and proverbs, phrases from text using n-gram and similarity measure methods. Finally, several experiments were carried out on various data, including Arabic tweets and Arabic microblogs (hotel reservation, product reviews, and TV program comments) from publicly available Arabic online reviews websites (social media, blogs, forums, e-commerce web sites) to evaluate the coverage and accuracy of AIPSeLEX.

Sentiment Analysis For Modern Standard Arabic And Colloquial

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%.