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Sentiment (supervised polarity classification) and topic analysis of LastQuake app user's comments - Aegean 2020 earthquake

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Version 4 2022-04-07, 10:59
Version 3 2021-05-27, 13:04
Version 2 2021-05-27, 11:50
Version 1 2021-05-26, 14:23
dataset
posted on 2022-04-07, 10:59 authored by Diana ContrerasDiana Contreras, Sean Wilkinson, Laure Fallou, Matthieu Landès, Rémy Bossu, Yasemin Didem AktasYasemin Didem Aktas

This database contains the sentiment analysis (SA) and topic supervised classification of the comments posted by LastQuake app users related to the 30th October 2020 Aegean earthquake. LastQuake app is a crowdsource-based earthquake information app that allows eyewitnesses to share information about the earthquakes that they felt, combined with seismic data. This app was developed by the European-Mediterranean Seismological Centre (EMSC). Attributes and data contained in the database are:

§ eq_evid : Number of the earthquake the comment is associated with

§ eq_mag : magnitude of the event

§ eq_t0 : Origin time (UTC)

§ intensity: felt report intensity (as before leaving a comment users must leave a felt report)

§ epidist : distance from the event of the comment, in km

§ dt : response time from the origin time of the associated event, in seconds

§ rate_pos : number of positive rates *

§ rate_neg : number of negative rates*

§ device : device from which the comment was left (desktop, mobile ou app)

§ comm_valid : 0 or 1 depending on if we validated the comment or not. We invalidate comments when we consider them inappropriate (violence, insults,...)

§ language: Original language on which the comment was written

§ polarity: Polarity on which the comment is classified, i.e. positive, negative, neutral

§ topic: Main topic addressed in the comment, i.e. building damages, tsunami effects, geotechnical effects, lifelines affected, emergency response, solidarity messages, preparedness, seismic information, intensity and unrealted.

§ comment: comment posted by LastQuake app user translated to English


Funding

Learning from Earthquakes: Building Resilient Communities Through Earthquake Reconnaissance, Response and Recovery

Engineering and Physical Sciences Research Council

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  • Engineering