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IMS at EmoInt-2017 : Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning
Köper, Maximilian; Kim, Evgeny; Klinger, Roman (2017): IMS at EmoInt-2017 : Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning, in: Alexandra Balahur, Saif M. Mohammad, Erik van der Goot, u. a. (Hrsg.), Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Association for Computational Linguistics, S. 50–57, doi: 10.18653/v1/W17-5206.
Faculty/Chair:
Author:
Title of the compilation:
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
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Conference:
8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis ; Copenhagen, Denmark
Publisher Information:
Year of publication:
2017
Pages:
Language:
English
DOI:
Abstract:
Our submission to the WASSA-2017 shared task on the prediction of emotion intensity in tweets is a supervised learning method with extended lexicons of affective norms. We combine three main information sources in a random forrest regressor, namely (1), manually created resources, (2) automatically extended lexicons, and (3) the output of a neural network (CNN-LSTM) for sentence regression. All three feature sets perform similarly well in isolation (≈ .67 macro average Pearson correlation). The combination achieves .72 on the official test set (ranked 2nd out of 22 participants). Our analysis reveals that performance is increased by providing cross-emotional intensity predictions. The automatic extension of lexicon features benefit from domain specific embeddings. Complementary ratings for affective norms increase the impact of lexicon features. Our resources (ratings for 1.6 million twitter specific words) and our implementation is publicly available at http://www.ims.uni-stuttgart.de/data/ims_emoint.
GND Keywords: ;
Computerlinguistik
Emotion
Keywords:
Emotion Intensity Prediction
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RVK Classification:
Peer Reviewed:
Yes:
International Distribution:
Yes:
Open Access Journal:
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Type:
Conferenceobject
Activation date:
March 13, 2024
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Question on publication
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https://fis.uni-bamberg.de/handle/uniba/93978