Tweet sentiment: From classification to quantification

Sentiment classification has become a ubiquitous enabling technology in the Twittersphere, since classifying tweets according to the sentiment they convey towards a given entity (be it a product, a person, a political party, or a policy) has many applications in political science, social science, ma...

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Main Authors: GAO, Wei, SEBASTIANI, Fabrizio
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access:https://ink.library.smu.edu.sg/sis_research/4574
https://ink.library.smu.edu.sg/context/sis_research/article/5577/viewcontent/p97_gao.pdf
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spelling sg-smu-ink.sis_research-55772019-12-26T08:16:49Z Tweet sentiment: From classification to quantification GAO, Wei SEBASTIANI, Fabrizio Sentiment classification has become a ubiquitous enabling technology in the Twittersphere, since classifying tweets according to the sentiment they convey towards a given entity (be it a product, a person, a political party, or a policy) has many applications in political science, social science, market research, and many others. In this paper we contend that most previous studies dealing with tweet sentiment classification (TSC) use a suboptimal approach. The reason is that the final goal of most such studies is not estimating the class label (e.g., Positive, Negative, or Neutral) of individual tweets, but estimating the relative frequency (a.k.a. "prevalence") of the different classes in the dataset. The latter task is called quantification, and recent research has convincingly shown that it should be tackled as a task of its own, using learning algorithms and evaluation measures different from those used for classification. In this paper we show, on a multiplicity of TSC datasets, that using a quantification-specific algorithm produces substantially better class frequency estimates than a state-of-the-art classification-oriented algorithm routinely used in TSC. We thus argue that researchers interested in tweet sentiment prevalence should switch to quantification-specific (instead of classification-specific) learning algorithms and evaluation measures. 2015-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4574 info:doi/10.1145/2808797.2809327 https://ink.library.smu.edu.sg/context/sis_research/article/5577/viewcontent/p97_gao.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
GAO, Wei
SEBASTIANI, Fabrizio
Tweet sentiment: From classification to quantification
description Sentiment classification has become a ubiquitous enabling technology in the Twittersphere, since classifying tweets according to the sentiment they convey towards a given entity (be it a product, a person, a political party, or a policy) has many applications in political science, social science, market research, and many others. In this paper we contend that most previous studies dealing with tweet sentiment classification (TSC) use a suboptimal approach. The reason is that the final goal of most such studies is not estimating the class label (e.g., Positive, Negative, or Neutral) of individual tweets, but estimating the relative frequency (a.k.a. "prevalence") of the different classes in the dataset. The latter task is called quantification, and recent research has convincingly shown that it should be tackled as a task of its own, using learning algorithms and evaluation measures different from those used for classification. In this paper we show, on a multiplicity of TSC datasets, that using a quantification-specific algorithm produces substantially better class frequency estimates than a state-of-the-art classification-oriented algorithm routinely used in TSC. We thus argue that researchers interested in tweet sentiment prevalence should switch to quantification-specific (instead of classification-specific) learning algorithms and evaluation measures.
format text
author GAO, Wei
SEBASTIANI, Fabrizio
author_facet GAO, Wei
SEBASTIANI, Fabrizio
author_sort GAO, Wei
title Tweet sentiment: From classification to quantification
title_short Tweet sentiment: From classification to quantification
title_full Tweet sentiment: From classification to quantification
title_fullStr Tweet sentiment: From classification to quantification
title_full_unstemmed Tweet sentiment: From classification to quantification
title_sort tweet sentiment: from classification to quantification
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/4574
https://ink.library.smu.edu.sg/context/sis_research/article/5577/viewcontent/p97_gao.pdf
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