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Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator

08/21/2017

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by Venkatesh Duppada, et al.

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The paper describes experiments on estimating emotion intensity in tweets using a generalized regressor system. The system combines lexical, syntactic and pre-trained word embedding features, trains them on general regressors and finally combines the best performing models to create an ensemble. The proposed system stood 3rd out of 22 systems in the leaderboard of WASSA-2017 Shared Task on Emotion Intensity.

Venkatesh Duppada
5 publications
Sushant Hiray
5 publications

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∙ 08/11/2017

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This paper examines the task of detecting intensity of emotion from text...

0 Saif M. Mohammad, et al. ∙

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∙ 08/18/2017

EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity

In this paper we describe a deep learning system that has been designed ...

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SELF FEIL: Emotion and Intensity Lexicons for Finnish

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∙ 04/17/2018

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∙ 12/10/2020

Exploring Deep Neural Networks and Transfer Learning for Analyzing Emotions in Tweets

In this paper, we present an experiment on using deep learning and trans...

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∙ 05/28/2018

UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish

The present study describes our submission to SemEval 2018 Task 1: Affec...

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∙ 02/26/2018

EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets

This paper describes our system that has been used in Task1 Affect in Tw...

0 Mohammed Jabreel, et al. ∙