Acknowledging discourse function for sentiment analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Acknowledging discourse function for sentiment analysis. / Smith, Phillip; Lee, Mark.

Computational Linguistics and Intelligent Text Processing : 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part II. ed. / Alexander Gelbukh . Vol. 8404 LNCS Springer, 2014. p. 45-52 (Lecture Notes in Computer Science ; Vol. 8404).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Smith, P & Lee, M 2014, Acknowledging discourse function for sentiment analysis. in A Gelbukh (ed.), Computational Linguistics and Intelligent Text Processing : 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part II. vol. 8404 LNCS, Lecture Notes in Computer Science , vol. 8404, Springer, pp. 45-52, Computational Linguistics and Intelligent Text Processing, 15th International Conference, CICLing 2014, Kathmandu, Nepal, Nepal, 6/04/14. https://doi.org/10.1007/978-3-642-54903-8-4

APA

Smith, P., & Lee, M. (2014). Acknowledging discourse function for sentiment analysis. In A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing : 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part II (Vol. 8404 LNCS, pp. 45-52). (Lecture Notes in Computer Science ; Vol. 8404). Springer. https://doi.org/10.1007/978-3-642-54903-8-4

Vancouver

Smith P, Lee M. Acknowledging discourse function for sentiment analysis. In Gelbukh A, editor, Computational Linguistics and Intelligent Text Processing : 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part II. Vol. 8404 LNCS. Springer. 2014. p. 45-52. (Lecture Notes in Computer Science ). https://doi.org/10.1007/978-3-642-54903-8-4

Author

Smith, Phillip ; Lee, Mark. / Acknowledging discourse function for sentiment analysis. Computational Linguistics and Intelligent Text Processing : 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part II. editor / Alexander Gelbukh . Vol. 8404 LNCS Springer, 2014. pp. 45-52 (Lecture Notes in Computer Science ).

Bibtex

@inproceedings{9fa7df806de942b7af091639527ec445,
title = "Acknowledging discourse function for sentiment analysis",
abstract = "In this paper, we observe the effects that discourse function attribute to the task of training learned classifiers for sentiment analysis. Experimental results from our study show that training on a corpus of primarily persuasive documents can have a negative effect on the performance of supervised sentiment classification. In addition we demonstrate that through use of the Multinomial Na{\"i}ve Bayes classifier we can minimise the detrimental effects of discourse function during sentiment analysis.",
author = "Phillip Smith and Mark Lee",
year = "2014",
doi = "10.1007/978-3-642-54903-8-4",
language = "English",
isbn = "9783642549021 ",
volume = "8404 LNCS",
series = "Lecture Notes in Computer Science ",
publisher = "Springer",
pages = "45--52",
editor = "{Gelbukh }, {Alexander }",
booktitle = "Computational Linguistics and Intelligent Text Processing",
note = "Computational Linguistics and Intelligent Text Processing, 15th International Conference, CICLing 2014 ; Conference date: 06-04-2014 Through 12-04-2014",

}

RIS

TY - GEN

T1 - Acknowledging discourse function for sentiment analysis

AU - Smith, Phillip

AU - Lee, Mark

PY - 2014

Y1 - 2014

N2 - In this paper, we observe the effects that discourse function attribute to the task of training learned classifiers for sentiment analysis. Experimental results from our study show that training on a corpus of primarily persuasive documents can have a negative effect on the performance of supervised sentiment classification. In addition we demonstrate that through use of the Multinomial Naïve Bayes classifier we can minimise the detrimental effects of discourse function during sentiment analysis.

AB - In this paper, we observe the effects that discourse function attribute to the task of training learned classifiers for sentiment analysis. Experimental results from our study show that training on a corpus of primarily persuasive documents can have a negative effect on the performance of supervised sentiment classification. In addition we demonstrate that through use of the Multinomial Naïve Bayes classifier we can minimise the detrimental effects of discourse function during sentiment analysis.

UR - http://www.scopus.com/inward/record.url?scp=84899909608&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-54903-8-4

DO - 10.1007/978-3-642-54903-8-4

M3 - Conference contribution

SN - 9783642549021

VL - 8404 LNCS

T3 - Lecture Notes in Computer Science

SP - 45

EP - 52

BT - Computational Linguistics and Intelligent Text Processing

A2 - Gelbukh , Alexander

PB - Springer

T2 - Computational Linguistics and Intelligent Text Processing, 15th International Conference, CICLing 2014

Y2 - 6 April 2014 through 12 April 2014

ER -