Resumo

AMEER, Iqra; ASHRAF, Noman; SIDOROV, Grigori  e  GOMEZ ADORNO, Helena. Multi-label Emotion Classification using Content-Based Features in Twitter. Comp. y Sist. [online]. 2020, vol.24, n.3, pp.1159-1164.  Epub 09-Jun-2021. ISSN 2007-9737.  https://doi.org/10.13053/cys-24-3-3476.

Multi-label Emotion Classification is a supervised classification problem that aims to classify multiple emotion labels from a given text. Recently, Multi-label Emotion Classification has appealed to the research community due to possible applications in E-learning, marketing, education, and health care, etc. We applied content-based methods (words and character n-grams) on tweets to show how our purposed content-based method can be used for the development and evaluation of the Multi-label Emotion Classification task. The results achieved after our extensive experimentation demonstrate that content-based word unigram surpassed other content-based features (Multi-label Accuracy = 0.452, MicroF1 = 0.573, MacroF1 = 0.559, Exact Match = 0.141, Hamming Loss = 0.179).

Palavras-chave : Multi-label emotion classification; content-based methods; twitter.