Arabic Metaphor Sentiment Classification Using Semantic Information

In this paper, I discuss the testing of the Arabic Metaphor Corpus (AMC) [1] using newly designed automatic tools for sentiment classification for AMC based on semantic tags. The tool incorporates semantic emotional tags for sentiment classification. I evaluate the tool using standard methods, which are F-score, recall, and precision. The method is to show the impact of Arabic online metaphors on sentiment through the newly designed tools. To the best of our knowledge, this is the first approach to conduct sentiment classification for Arabic metaphors using semantic tags to find the impact of the metaphor. Keywords: Arabic metaphor, sentiment analysis, NLP, Arabic semantic tagger 1 Introduction To the best of our knowledge, there are no existing tools specifically developed for Arabic metaphor identification in the context of sentiment analysis. Identifying Arabic metaphors requires pre-annotated data, and in the absence of pre-annotation, a substantial corpus of Arabic metaphors would be necessary to train advanced machine learning algorithms for automatic identification. So, I am using the Arabic Metaphor Corpus (AMC) [1]. In terms of the Arabic metaphor, the very recent study conducted for Arabic metaphor identification with pre-annotated data without integrating sentiment classification is [2].