A dual framework for implicit and explicit emotion recognition: An ensemble of language models and computational linguistics

Abstract

One of the research domains in the field of sentiment analysis is automatic emotion recognition in texts which is a worthy topic in human-computer interaction. Text processing has always faced many challenges. The main one is the structural and semantic differences of sentences which have had a significant impact on the malfunction of auto-recognition systems. This problem becomes more prominent in short texts in which words and their concurrences are limited and insufficient. As a result of this, word frequency and TF-IDF weighing cannot well represent the relationship between words and the appropriate feature vector, leading to an undesirable accuracy of emotion recognition. Thus, different strategies should be applied to improve the feature vector and to formulate the features properly. The desired strategy should be able to identify the words that can distinguish between classes well and also to find the relationships between words and meaningful phrases using natural language processing concepts. In this paper, a combination of emotional models, categorical and hierarchical, are used for an emotional text recognition which could discover simultaneously explicit and implicit emotion in a short text. Our approach called DuFER, proposed a weighed method which improves the feature vector using language models and computational linguistics through applying a modified TF-IDF weighing to words as well as Maximum Likelihood Estimation weighing to expressions. Four implicit and explicit emotion datasets are used for the experiments. The results show that the accuracy of both implicit and explicit emotion recognition has increased and DuFER is actually the first successful dual framework in recognizing implicit and explicit emotions from text.