Detecting speculations, contrasts and conditionals in consumer reviews
Skeppstedt, Maria ; Schamp-Bjerede, Teri LU ; Sahlgren, Magnus ; Paradis, Carita LU
and Kerren, Andreas
(2015)
6th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA '15)
p.162-168
- Abstract
- A support vector classifier was compared to a lexicon-based approach for the task of detecting the stance categories speculation, contrast and conditional in English consumer reviews. Around 3,000 training instances were required to achieve a stable performance of an F-score of 90 for speculation. This outperformed the lexicon-based approach, for which an Fscore of just above 80 was achieved. The machine learning results for the other two categories showed a lower average (an approximate F-score of 60 for contrast and 70 for conditional), as well as a larger variance, and were only slightly better than lexicon matching. Therefore, while machine learning was successful for detecting speculation, a well-curated lexicon might be a more... (More)
- A support vector classifier was compared to a lexicon-based approach for the task of detecting the stance categories speculation, contrast and conditional in English consumer reviews. Around 3,000 training instances were required to achieve a stable performance of an F-score of 90 for speculation. This outperformed the lexicon-based approach, for which an Fscore of just above 80 was achieved. The machine learning results for the other two categories showed a lower average (an approximate F-score of 60 for contrast and 70 for conditional), as well as a larger variance, and were only slightly better than lexicon matching. Therefore, while machine learning was successful for detecting speculation, a well-curated lexicon might be a more suitable approach for detecting contrast and conditional. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8052276
- author
- Skeppstedt, Maria
; Schamp-Bjerede, Teri
LU
; Sahlgren, Magnus
; Paradis, Carita
LU
and Kerren, Andreas
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- consumer reviews, support vector classifier, stance
- host publication
- 6th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, WASSA 2015 : Workshop proceedings
- editor
- Alexandra, Balahur ; Erik, van der Goot ; Piek, Vossen and Andrés, Montoyo
- pages
- 7 pages
- publisher
- Association for Computational Linguistics
- conference name
- 6th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA '15)
- conference dates
- 2015-09-17
- external identifiers
-
- scopus:85032969587
- ISBN
- 978-1-941643-32-7
- project
- StaViCTA - Advances in the description and explanation of stance in discourse using visual and computational text analytics
- language
- English
- LU publication?
- yes
- id
- 900a4c9b-8c53-48c0-8679-7aef36931455 (old id 8052276)
- alternative location
- http://www.aclweb.org/anthology/W/W15/W15-29.pdf#page.162
- date added to LUP
- 2016-04-04 09:59:26
- date last changed
- 2022-02-21 02:29:38
@inproceedings{900a4c9b-8c53-48c0-8679-7aef36931455,
abstract = {{A support vector classifier was compared to a lexicon-based approach for the task of detecting the stance categories speculation, contrast and conditional in English consumer reviews. Around 3,000 training instances were required to achieve a stable performance of an F-score of 90 for speculation. This outperformed the lexicon-based approach, for which an Fscore of just above 80 was achieved. The machine learning results for the other two categories showed a lower average (an approximate F-score of 60 for contrast and 70 for conditional), as well as a larger variance, and were only slightly better than lexicon matching. Therefore, while machine learning was successful for detecting speculation, a well-curated lexicon might be a more suitable approach for detecting contrast and conditional.}},
author = {{Skeppstedt, Maria and Schamp-Bjerede, Teri and Sahlgren, Magnus and Paradis, Carita and Kerren, Andreas}},
booktitle = {{6th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, WASSA 2015 : Workshop proceedings}},
editor = {{Alexandra, Balahur and Erik, van der Goot and Piek, Vossen and Andrés, Montoyo}},
isbn = {{978-1-941643-32-7}},
keywords = {{consumer reviews; support vector classifier; stance}},
language = {{eng}},
pages = {{162--168}},
publisher = {{Association for Computational Linguistics}},
title = {{Detecting speculations, contrasts and conditionals in consumer reviews}},
url = {{http://www.aclweb.org/anthology/W/W15/W15-29.pdf#page.162}},
year = {{2015}},
}