UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings

Vineet John, Olga Vechtomova


Abstract
This paper describes the UWaterloo affect prediction system developed for EmoInt-2017. We delve into our feature selection approach for affect intensity, affect presence, sentiment intensity and sentiment presence lexica alongside pre-trained word embeddings, which are utilized to extract emotion intensity signals from tweets in an ensemble learning approach. The system employs emotion specific model training, and utilizes distinct models for each of the emotion corpora in isolation. Our system utilizes gradient boosted regression as the primary learning technique to predict the final emotion intensities.
Anthology ID:
W17-5235
Volume:
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
249–254
Language:
URL:
https://aclanthology.org/W17-5235
DOI:
10.18653/v1/W17-5235
Bibkey:
Cite (ACL):
Vineet John and Olga Vechtomova. 2017. UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 249–254, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings (John & Vechtomova, WASSA 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/W17-5235.pdf