Proceedings of the 2018 2nd International Conference on Education, Economics and Management Research (ICEEMR 2018)

Sentiment Analysis of Music Criticism Based on Data Mining

Authors
Yao Liang, Hu Wang
Corresponding Author
Yao Liang
Available Online June 2018.
DOI
10.2991/iceemr-18.2018.84How to use a DOI?
Keywords
commentary emotion mining, space vector model, musical emotion classification
Abstract

With the development of web2.0, more and more people like to express their emotions by making comments anytime and anywhere. Music comments are an emotional expression of the listener's mood for listening to songs. On the basis of the original Hevner emotion loop, this paper proposes an optimized emotional model that fits Chinese people's thinking and language habits, constructs a new musical sentiment dictionary, analyzes the polarity of musical emotions, and proposes an emotional vector space model based on emotional units. Through experiments, it is proved that the emotional vector space model has higher accuracy and convenience than artificial emotion annotation.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2018 2nd International Conference on Education, Economics and Management Research (ICEEMR 2018)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
June 2018
ISBN
10.2991/iceemr-18.2018.84
ISSN
2352-5398
DOI
10.2991/iceemr-18.2018.84How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yao Liang
AU  - Hu Wang
PY  - 2018/06
DA  - 2018/06
TI  - Sentiment Analysis of Music Criticism Based on Data Mining
BT  - Proceedings of the 2018 2nd International Conference on Education, Economics and Management Research (ICEEMR 2018)
PB  - Atlantis Press
SP  - 368
EP  - 371
SN  - 2352-5398
UR  - https://doi.org/10.2991/iceemr-18.2018.84
DO  - 10.2991/iceemr-18.2018.84
ID  - Liang2018/06
ER  -