TY - JOUR
T1 - Multi-label emotion classification using content-based features in twitter
AU - Ameer, Iqra
AU - Ashraf, Noman
AU - Sidorov, Grigori
AU - Adorno, Helena Gomez
N1 - Publisher Copyright:
© 2020 Instituto Politecnico Nacional. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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).
AB - 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).
KW - Contentbased methods
KW - Multi-label emotion classification
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85095717964&partnerID=8YFLogxK
U2 - 10.13053/CYS-24-3-3476
DO - 10.13053/CYS-24-3-3476
M3 - Artículo
AN - SCOPUS:85095717964
SN - 1405-5546
VL - 24
SP - 1159
EP - 1164
JO - Computacion y Sistemas
JF - Computacion y Sistemas
IS - 3
ER -