SEMO

摘要

In this work, we present SEMO, a Semantic Model for Emotion Recognition, which enables users to detect and quantify the emotional load related to basic emotions hidden in short, emotionally rich sentences (e.g. news titles, tweets, captions). The idea of assessing the semantic similarity of concepts by looking at the occurrences and co-occurrences of terms describing them in pages indexed by a search engine can be directly extended to emotions, and to the words expressing them in different languages. The emotional content associated to a particular emotion for a term can thus be estimated using web-based similarity measures, e.g. Confidence, PMI, NGD and PMING, aggregating the distance computed by a model of emotions, e.g. Ekman, Plutchik and Lovheim. Emotions are ranked based on their similarity to the analyzed text, describing each sentence through a vector of values of emotion load, which form the Vector Space Model for the chosen emotion model and similarity measures. The model is tested comparing experimental results to a ground truth in literature. SEMO takes care of both the phases of data collection and data analysis, to produce knowledge to be used in application domains such as social robots, recommender systems, and human-machine interactive systems.