A Large Scale Speech Sentiment Corpus
Eric Chen, Zhiyun Lu, Hao Xu, Liangliang Cao, Yu Zhang, James Fan
Abstract
We present a multimodal corpus for sentiment analysis based on the existing Switchboard-1 Telephone Speech Corpus released by the Linguistic Data Consortium. This corpus extends the Switchboard-1 Telephone Speech Corpus by adding sentiment labels from 3 different human annotators for every transcript segment. Each sentiment label can be one of three options: positive, negative, and neutral. Annotators are recruited using Google Cloud’s data labeling service and the labeling task was conducted over the internet. The corpus contains a total of 49500 labeled speech segments covering 140 hours of audio. To the best of our knowledge, this is the largest multimodal Corpus for sentiment analysis that includes both speech and text features.- Anthology ID:
- 2020.lrec-1.806
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6549–6555
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.806
- DOI:
- Bibkey:
- Cite (ACL):
- Eric Chen, Zhiyun Lu, Hao Xu, Liangliang Cao, Yu Zhang, and James Fan. 2020. A Large Scale Speech Sentiment Corpus. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6549–6555, Marseille, France. European Language Resources Association.
- Cite (Informal):
- A Large Scale Speech Sentiment Corpus (Chen et al., LREC 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.lrec-1.806.pdf
Export citation
@inproceedings{chen-etal-2020-large,
title = "A Large Scale Speech Sentiment Corpus",
author = "Chen, Eric and
Lu, Zhiyun and
Xu, Hao and
Cao, Liangliang and
Zhang, Yu and
Fan, James",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.806",
pages = "6549--6555",
abstract = "We present a multimodal corpus for sentiment analysis based on the existing Switchboard-1 Telephone Speech Corpus released by the Linguistic Data Consortium. This corpus extends the Switchboard-1 Telephone Speech Corpus by adding sentiment labels from 3 different human annotators for every transcript segment. Each sentiment label can be one of three options: positive, negative, and neutral. Annotators are recruited using Google Cloud{'}s data labeling service and the labeling task was conducted over the internet. The corpus contains a total of 49500 labeled speech segments covering 140 hours of audio. To the best of our knowledge, this is the largest multimodal Corpus for sentiment analysis that includes both speech and text features.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>We present a multimodal corpus for sentiment analysis based on the existing Switchboard-1 Telephone Speech Corpus released by the Linguistic Data Consortium. This corpus extends the Switchboard-1 Telephone Speech Corpus by adding sentiment labels from 3 different human annotators for every transcript segment. Each sentiment label can be one of three options: positive, negative, and neutral. Annotators are recruited using Google Cloud’s data labeling service and the labeling task was conducted over the internet. The corpus contains a total of 49500 labeled speech segments covering 140 hours of audio. To the best of our knowledge, this is the largest multimodal Corpus for sentiment analysis that includes both speech and text features.</abstract>
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%0 Conference Proceedings %T A Large Scale Speech Sentiment Corpus %A Chen, Eric %A Lu, Zhiyun %A Xu, Hao %A Cao, Liangliang %A Zhang, Yu %A Fan, James %Y Calzolari, Nicoletta %Y Béchet, Frédéric %Y Blache, Philippe %Y Choukri, Khalid %Y Cieri, Christopher %Y Declerck, Thierry %Y Goggi, Sara %Y Isahara, Hitoshi %Y Maegaard, Bente %Y Mariani, Joseph %Y Mazo, Hélène %Y Moreno, Asuncion %Y Odijk, Jan %Y Piperidis, Stelios %S Proceedings of the Twelfth Language Resources and Evaluation Conference %D 2020 %8 May %I European Language Resources Association %C Marseille, France %@ 979-10-95546-34-4 %G English %F chen-etal-2020-large %X We present a multimodal corpus for sentiment analysis based on the existing Switchboard-1 Telephone Speech Corpus released by the Linguistic Data Consortium. This corpus extends the Switchboard-1 Telephone Speech Corpus by adding sentiment labels from 3 different human annotators for every transcript segment. Each sentiment label can be one of three options: positive, negative, and neutral. Annotators are recruited using Google Cloud’s data labeling service and the labeling task was conducted over the internet. The corpus contains a total of 49500 labeled speech segments covering 140 hours of audio. To the best of our knowledge, this is the largest multimodal Corpus for sentiment analysis that includes both speech and text features. %U https://aclanthology.org/2020.lrec-1.806 %P 6549-6555
Markdown (Informal)
[A Large Scale Speech Sentiment Corpus](https://aclanthology.org/2020.lrec-1.806) (Chen et al., LREC 2020)
- A Large Scale Speech Sentiment Corpus (Chen et al., LREC 2020)
ACL
- Eric Chen, Zhiyun Lu, Hao Xu, Liangliang Cao, Yu Zhang, and James Fan. 2020. A Large Scale Speech Sentiment Corpus. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6549–6555, Marseille, France. European Language Resources Association.