Multi-label emotion classification using content-based features in twitter

Iqra Ameer, Noman Ashraf, Grigori Sidorov, Helena Gomez Adorno

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

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).

Original languageEnglish (US)
Pages (from-to)1159-1164
Number of pages6
JournalComputacion y Sistemas
Volume24
Issue number3
DOIs
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • General Computer Science

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