Fi-senti: A language-independent model for figurative sentiment analysis

Abstract

This paper focuses on identifying the polarity of figurative language in the very short text collected from Social Network Services. Although this topic is not new, most computer scientists have solved this issue by using natural language processing techniques. This seems difficult for non-native English speakers because they have to rely on heuristics in language. Therefore, our target in this work is to find a languageindependent approach to solve the problem without using any semantic resources (e.g., dictionaries and ontologies). A statistical method based on two main features (i.e., (i) textual terms and (ii) sentimental patterns) is proposed to determine the sentiment degree of three popular types of figurative language (i.e., sarcasm, irony, and metaphor). We experimented on two Test sets with about 3,800 tweets and used Cosine similarity as the correlation measurement for evaluating the performance. The results show that our Fi-Senti model (Figurative Sentiment analysis model) well performs in determining the sentiment intensity of the figurative language with the best achievement is 0.8952 with sarcasm and 0.9011 with irony.