Sentiment Analysis on Microblogs for Natural Disasters Management

Buscaldi, Davide; Hernandez-Farias, Irazú

doi:10.1145/2740908.2741727

Cited by 40 publications

(28 citation statements)

References 8 publications

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“…Different types of users (e.g., new reporters, general public, celebrities) may witness and experience different aspects of the disaster event. Therefore, a few studies develop different classification schema for the message contributors and consider their sentiment, emotions, and personal feelings of message contributor by classifying messages into categories (e.g., subjective, positive, negative, ironic) or ranges of integer (−5 to +5) that express the user's response to the disaster event (Buscaldi and Hernandez-Farias 2015;Caragea et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Situational awareness extraction: a comprehensive review of social media data classification during natural hazards

Vongkusolkit

Huang

2020

Annals of GIS

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Social media (e.g., Twitter and Facebook) can be regarded as vital sources of information during disasters to improve situational awareness (SA) and disaster management since they play a significant role in the rapid spread of information in the event of a disaster. Due to the volume of data is far beyond the capabilities of manual examination, existing works utilize natural language processing methods based on keywords, or classification models relying on features derived from text and other metadata (e.g., user profiles) to extract social media data contributing to SA and automatically categorize them into the relevant classes (e.g., damage and donation). However, the design of the classification schema and the associated information extraction methods are far less than straightforward and highly depend on: (1) the event type, (2) the study or analysis purpose, and (3) the social media platform used. To this end, this paper reviews the literature for extracting social media data and provides an overview of classification schemas that have been used to assess SA in events involving natural hazards from five different analytical perspectives (content, temporal, user, sentiment, and spatiotemporal) by discussing the prevalent topic categories, disaster event types, purpose of studies, and platforms utilized from each schema. Finally, this paper summarizes classification methods, and platforms that are most commonly used for each disaster event type, and outlines a research agenda with recommendations for future innovations.

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Section: Introductionmentioning

confidence: 99%

Situational awareness extraction: a comprehensive review of social media data classification during natural hazards

Vongkusolkit

Huang

2020

Annals of GIS

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“…Melalui Algoritma Naïve Bayes, Support Vector Machine dan Random Forest klasifikasi dapat dilakukan. Setelah data diambil biasanya data dari twitter ini diolah dengan metode Sentiment Analisis seperti [11] [12]. Selain twitter, penggunaan social media lain seperti Instagram, vine, facebook sampai reddit juga digunakan untuk crisis monitoring [13].…”

Section: Penelitian Terkaitunclassified

Location Based Service for improving Chabot Disaster Management Evacuator Palu

Rohman

2020

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The devastating earthquake that struck Palu on the island of Sulawesi last September ripped through the Earth's crust at a rare high speed, scientists have found. When the disaster is over, many natural disaster victims need immediate help. The call center provided is usually busy with services and complaints from victims of natural disasters. The greater the impact of natural disasters, the more information services that must be carried out. By using CEPAT chatbot for disaster evacuation in Palu, information about evacuation place can be given to victim by access it. Then when the victim shares their location, CEPAT will give the nearest evacuation place information using LBS improvement Chabot.

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“…There are several examples of sentiment analysis in political science and sociology. For instance, sentiment analysis of Twitter has been used to monitor political opinions (Tumasjan et al 2011), to analyse user stances in social media debates (Stranisci et al 2016;Lai et al 2015;Mohammad et al 2015), and to extract critical information during mass emergencies (Verma et al 2011;Buscaldi and Hernández-Farías 2015). Examples from the social sciences include estimations of subjective well-being, and such sentiment analysis has helped derive measures of happiness within economics, complementing more traditional measures of well-being such as Gross Domestic Product (Diener 2000).…”

Section: Related Workmentioning

confidence: 99%

Happy parents’ tweets: An exploration of Italian Twitter data using sentiment analysis

Farías¹,

Lai²,

Patti³

et al. 2019

DemRes

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BACKGROUND Demographers are increasingly interested in connecting demographic behaviour and trends with 'soft' measures, i.e., complementary information on attitudes, values, feelings, and intentions. OBJECTIVE The aim of this paper is to demonstrate how computational linguistic techniques can be used to explore opinions and semantic orientations related to parenthood. METHODS In this article we scrutinize about three million filtered Italian tweets from 2014. First, we implement a methodological framework relying on Natural Language Processing techniques for text analysis, which is used to extract sentiments. We then run a supervised machine-learning experiment on the overall dataset, based on the annotated set of tweets from the previous stage. Consequently, we infer to what extent social media users report negative or positive affect on topics relevant to the fertility domain. RESULTS Parents express a generally positive attitude towards being and becoming parents, but they are also fearful, surprised, and sad. They also have quite negative sentiments about their children's future, politics, fertility, and parental behaviour. By exploiting geographical information from tweets we find a significant correlation between the

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Sentiment Analysis on Microblogs for Natural Disasters Management

Cited by 40 publications

References 8 publications

Situational awareness extraction: a comprehensive review of social media data classification during natural hazards

Situational awareness extraction: a comprehensive review of social media data classification during natural hazards

Location Based Service for improving Chabot Disaster Management Evacuator Palu

Happy parents’ tweets: An exploration of Italian Twitter data using sentiment analysis

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