Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis

Minglei Li, Qin Lu, Yunfei Long


Abstract
In this paper, we investigate the effectiveness of different affective lexicons through sentiment analysis of phrases. We examine how phrases can be represented through manually prepared lexicons, extended lexicons using computational methods, or word embedding. Comparative studies clearly show that word embedding using unsupervised distributional method outperforms manually prepared lexicons no matter what affective models are used in the lexicons. Our conclusion is that although different affective lexicons are cognitively backed by theories, they do not show any advantage over the automatically obtained word embedding.
Anthology ID:
I17-2025
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Editors:
Greg Kondrak, Taro Watanabe
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
146–150
Language:
URL:
https://aclanthology.org/I17-2025
DOI:
Bibkey:
Cite (ACL):
Minglei Li, Qin Lu, and Yunfei Long. 2017. Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 146–150, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis (Li et al., IJCNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/I17-2025.pdf
Data
SSTSST-2