Emotion, the private state of a human entity, is becoming an important topic in Natural Language Processing (NLP) with increasing use of search engines. The present task aims to manually annotate the sentences in a web based Bengali blog corpus with the emotional components such as emotional expression (word/phrase), intensity, associated holder and topic(s). Ekman's six emotion classes (anger, disgust, fear, happy, sad and surprise) along with three types of intensities (high, general and low) are considered for the sentence level annotation. Presence of discourse markers, punctuation marks, negations, conjuncts, reduplication, rhetoric knowledge and especially emoticons play the contributory roles in the annotation process. Different types of fixed and relaxed strategies have been employed to measure the agreement of the sentential emotions, intensities, emotional holders and topics respectively. Experimental results for each emotion class at word level on a small set of the whole corpus have been found satisfactory.
REF aims to manually annotate the sentences in a web-based Bengali blog corpus with the emotional components such as emotional expression (word/phrase), intensity, associated holder and topic(s).
Labeling Emotion in Bengali Blog Corpus – A Fine Grained Tagging at Sentence Level
Proceedings of the Eighth Workshop on Asian Language Resouces
Psychology