DENS: A Dataset for Multi-class Emotion Analysis

10/25/2019
by   Chen Liu, et al.
0

We introduce a new dataset for multi-class emotion analysis from long-form narratives in English. The Dataset for Emotions of Narrative Sequences (DENS) was collected from both classic literature available on Project Gutenberg and modern online narratives available on Wattpad, annotated using Amazon Mechanical Turk. A number of statistics and baseline benchmarks are provided for the dataset. Of the tested techniques, we find that the fine-tuning of a pre-trained BERT model achieves the best results, with an average micro-F1 score of 60.4 in emotion analysis that requires moving beyond existing sentence-level techniques.

READ FULL TEXT
research
05/01/2020

GoEmotions: A Dataset of Fine-Grained Emotions

Understanding emotion expressed in language has a wide range of applicat...
research
07/24/2022

ArmanEmo: A Persian Dataset for Text-based Emotion Detection

With the recent proliferation of open textual data on social media platf...
research
02/09/2022

TamilEmo: Finegrained Emotion Detection Dataset for Tamil

Emotional Analysis from textual input has been considered both a challen...
research
02/21/2019

ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN

In this paper we present our model on the task of emotion detection in t...
research
08/17/2021

A Weak Supervised Dataset of Fine-Grained Emotions in Portuguese

Affective Computing is the study of how computers can recognize, interpr...
research
12/04/2018

Practical Text Classification With Large Pre-Trained Language Models

Multi-emotion sentiment classification is a natural language processing ...
research
07/23/2019

EmotionX-HSU: Adopting Pre-trained BERT for Emotion Classification

This paper describes our approach to the EmotionX-2019, the shared task ...

Please sign up or login with your details

Forgot password? Click here to reset