CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons

Raj Kumar Gupta, Yinping Yang


Abstract
While sentiment and emotion analysis has received a considerable amount of research attention, the notion of understanding and detecting the intensity of emotions is relatively less explored. This paper describes a system developed for predicting emotion intensity in tweets. Given a Twitter message, CrystalFeel uses features derived from parts-of-speech, n-grams, word embedding, and multiple affective lexicons including Opinion Lexicon, SentiStrength, AFFIN, NRC Emotion & Hash Emotion, and our in-house developed EI Lexicons to predict the degree of the intensity associated with fear, anger, sadness, and joy in the tweet. We found that including the affective lexicons-based features allowed the system to obtain strong prediction performance, while revealing interesting emotion word-level and message-level associations. On gold test data, CrystalFeel obtained Pearson correlations of 0.717 on average emotion intensity and of 0.816 on sentiment intensity.
Anthology ID:
S18-1038
Volume:
Proceedings of the 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
SemEval | *SEM
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
256–263
Language:
URL:
https://aclanthology.org/S18-1038
DOI:
10.18653/v1/S18-1038
Bibkey:
Cite (ACL):
Raj Kumar Gupta and Yinping Yang. 2018. CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 256–263, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons (Gupta & Yang, SemEval-*SEM 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/S18-1038.pdf