Building a Libyan Dialect Lexicon-Based Sentiment Analysis System Using Semantic Orientation of Adjective-Adverb Combinations

Twitter is a social media network website, where its users can post their opinions and sentiments about issues, objects, services, places or people in short text messages called tweets. The sentiment information which is extracted from analyzing tweets is very useful in various aspects such as business, governments and so on. Although Arabic dialects social media sentiment analysis has attracted several studies, yet there has been almost no work on the Libyan dialect sentiment analysis. In this research, an adjective priority scoring algorithm which calculates the sentiment orientation of adjective-adverb combinations is used to build a fine-grained sentiment analysis system for classifying Libyan dialect tweets into seven categories. Therefore, we exploit a freely available Libyan dialect twitter corpus, which contains 5000 sentences or tweets to carry out our work, the tweets in the corpus were equally divided into two data sets (study and test). Adjectives and adverbs in the study data set were manually collected to construct sentiment dictionaries or lexicons. Consequently, approximately 108 adjectives were stored in a adjectives dictionary, the polarities or semantic orientation scores of these adjectives were manually assigned by two annotators in the range of +2,-2. Likewise, each adverb of degree was scored in the range from 0 to 1 and stored them in a separate dictionary which totally contains 27 adverbs. Our system yields an F-score of 82.19% on the test data set.