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Estimate Sentiment of Crowds from Social Media during City Events. / Gong, Vincent X.; Daamen, Winnie; Bozzon, Alessandro; Hoogendoorn, Serge P.

In: Transportation Research Record, 2019.

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@article{51c6fbac8346410091bca61eb4d09689,
title = "Estimate Sentiment of Crowds from Social Media during City Events",
abstract = "City events are being organized more frequently, and with larger crowds, in urban areas. There is an increased need for novel methods and tools that can provide information on the sentiments of crowds as an input for crowd management. Previous work has explored sentiment analysis and a large number of methods have been proposed relating to various contexts. None of them, however, aimed at deriving the sentiments of crowds using social media in city events, and no existing event-based dataset is available for such studies. This paper investigates how social media can be used to estimate the sentiments of crowds in city events. First, some lexicon-based and machine learning-based methods were selected to perform sentiment analyses, then an event-based sentiment annotated dataset was constructed. The performance of the selected methods was trained and tested in an experiment using common and event-based datasets. Results show that the machine learning method LinearSVC achieves the lowest estimation error for sentiment analysis on social media in city events. The proposed event-based dataset is essential for training methods to reduce estimation error in such contexts.",
author = "Gong, {Vincent X.} and Winnie Daamen and Alessandro Bozzon and Hoogendoorn, {Serge P.}",
year = "2019",
doi = "10.1177/0361198119846461",
language = "English",
journal = "Transportation Research Record",
issn = "0361-1981",
publisher = "US National Research Council",

}

RIS

TY - JOUR

T1 - Estimate Sentiment of Crowds from Social Media during City Events

AU - Gong, Vincent X.

AU - Daamen, Winnie

AU - Bozzon, Alessandro

AU - Hoogendoorn, Serge P.

PY - 2019

Y1 - 2019

N2 - City events are being organized more frequently, and with larger crowds, in urban areas. There is an increased need for novel methods and tools that can provide information on the sentiments of crowds as an input for crowd management. Previous work has explored sentiment analysis and a large number of methods have been proposed relating to various contexts. None of them, however, aimed at deriving the sentiments of crowds using social media in city events, and no existing event-based dataset is available for such studies. This paper investigates how social media can be used to estimate the sentiments of crowds in city events. First, some lexicon-based and machine learning-based methods were selected to perform sentiment analyses, then an event-based sentiment annotated dataset was constructed. The performance of the selected methods was trained and tested in an experiment using common and event-based datasets. Results show that the machine learning method LinearSVC achieves the lowest estimation error for sentiment analysis on social media in city events. The proposed event-based dataset is essential for training methods to reduce estimation error in such contexts.

AB - City events are being organized more frequently, and with larger crowds, in urban areas. There is an increased need for novel methods and tools that can provide information on the sentiments of crowds as an input for crowd management. Previous work has explored sentiment analysis and a large number of methods have been proposed relating to various contexts. None of them, however, aimed at deriving the sentiments of crowds using social media in city events, and no existing event-based dataset is available for such studies. This paper investigates how social media can be used to estimate the sentiments of crowds in city events. First, some lexicon-based and machine learning-based methods were selected to perform sentiment analyses, then an event-based sentiment annotated dataset was constructed. The performance of the selected methods was trained and tested in an experiment using common and event-based datasets. Results show that the machine learning method LinearSVC achieves the lowest estimation error for sentiment analysis on social media in city events. The proposed event-based dataset is essential for training methods to reduce estimation error in such contexts.

UR - http://www.scopus.com/inward/record.url?scp=85068215280&partnerID=8YFLogxK

U2 - 10.1177/0361198119846461

DO - 10.1177/0361198119846461

M3 - Article

JO - Transportation Research Record

T2 - Transportation Research Record

JF - Transportation Research Record

SN - 0361-1981

ER -

ID: 55113131