Detecting implicit expressions of emotion in text: A comparative analysis

Sentiment analysis is one of the recent, highly dynamic fields in Natural Language Processing. Most existing approaches are based on word-level analysis of texts and are mostly able to detect only explicit expressions of sentiment. However, in many cases, emotions are not expressed by using words with an affective meaning (e.g. happy), but by describing real-life situations, which readers (based on their commonsense knowledge) detect as being related to a specific emotion. Given the challenges of detecting emotions from contexts in which no lexical clue is present, in this article we present a comparative analysis between the performance of well-established methods for emotion detection (supervised and lexical knowledge-based) and a method we propose and extend, which is based on commonsense knowledge stored in the EmotiNet knowledge base. Our extensive evaluations show that, in the context of this task, the approach based on EmotiNet is the most appropriate.

► The article deals with implicit affect in text using commonsense knowledge stored in EmotiNet, extended with onto-lexical resources.
► The approach deals with gathering and exploiting knowledge on emotion-triggering situations based on the Appraisal Theories.
► The approach is compared with traditional emotion detection methods, showing important improvements.
► The challenge remains to gather new knowledge from onto-lexical resouces and their integration.