Grasp of Disaster Situation and Support Need Inside Affected Area with Social Sensing – An Analysis of Twitter Data Before and After the 2011 Great East Japan Earthquake Disaster Occurring –

2016 ◽  
Vol 11 (2) ◽  
pp. 198-206 ◽  
Author(s):  
Shosuke Sato ◽  
◽  
Kazumasa Hanaoka ◽  
Makoto Okumura ◽  
Shunichi Koshimura
Keyword(s):  
Great East Japan Earthquake ◽  
Earthquake Disaster ◽  
Social Sensing ◽  
Natural Language Text ◽  
Twitter Data ◽  
Before And After ◽  
Disaster Situation ◽  
Social Media Text ◽  
Source Of Information ◽  
Language Text

There are increasing expectations that social sensing, especially the analysis of social media text as a source of information for COP (Common Operational Picture), is useful for decision-making about responses to disasters. This paper reports on a geo-information and content analysis of three million Twitter texts sampled from Japanese Twitter accounts for one month before and after the 2011 Great East Japan Earthquake disaster. The results are as follows. 1) The number of Twitter texts that include geotag (latitude and longitude information) is too small for reliable analysis. However, a method of detecting the tweet’s location from the tweet’s text using GeoNLP (an automatic technology to tag geo-information from natural language text) is able to identify geo-information, and we have confirmed that many tweets were sent from stricken areas. 2) A comparison of Twitter data distribution before and after the disaster occurred does not identify clearly which areas were significantly affected by the disaster. 3) There were very few Twitter texts that included information about the damage in affected areas and their support needs.

scholarly journals The State Art of Text Sentiment from Opinions to Emotion Mining

2020 ◽  
Vol 5 (2) ◽  
pp. 43-52
Author(s):  
Nor Anis Asma Sulaiman ◽  
◽  
Leelavathi Rajamanickam ◽  
Keyword(s):  
Text Mining ◽  
General System ◽  
System Framework ◽  
Natural Language Text ◽  
Social Media Text ◽  
Emotion Model ◽  
Bayes Algorithm ◽  
Language Text ◽  
Emotion Mining ◽  
Processing Language

This study is aiming to analyse the feelings expressed by the users in a text on a comment posted on social media. Text Mining and Emotion Mining can be analysed by using both technique of Natural Processing Language (NLP). Mostly on the previous study of text mining is using unsupervised technique and referring to Ekman’s Emotion Model (EEM) but it has restrained coverage of polarity shifters, negations and lack emoticon. In this study have proposed a Naïve Bayes algorithm as a tool to produce users’ emotion pattern. The most important contribution of this study is to visualize the emotion’s theory with the text sentiment based on the computational methods for classifying users’ feelings from natural language text. Then, the general system framework of extracting opinions to emotion mining has produced and capable use in any domains.

Influences of Stress on Interactions with Decision Support

2020 ◽  
Vol 64 (1) ◽  
pp. 1886-1890
Author(s):  
Kylie Litaker ◽  
Christopher B. Mayhorn
Keyword(s):  
Decision Support ◽  
Information Source ◽  
Negative Influence ◽  
Future Research ◽  
Physiological Measures ◽  
Task Motivation ◽  
Situational Stress ◽  
Before And After ◽  
Scanning Task ◽  
Source Of Information

People regularly interact with automation to make decisions. Research shows that reliance on recommendations can depend on user trust in the decision support system (DSS), the source of information (i.e. human or automation), and situational stress. This study explored how information source and stress affect trust and reliance on a DSS used in a baggage scanning task. A preliminary sample of sixty-one participants were given descriptions for a DSS and reported trust before and after interaction. The DSS gave explicit recommendations when activated and participants could choose to rely or reject the choice. Results revealed a bias towards self-reliance and a negative influence of stress on trust, particularly for participants receiving help from automation. Controlling for perceived reliability may have eliminated trust biases prior to interaction, while stress may have influenced trust during the task. Future research should address potential differences in task motivation and include physiological measures of stress.

Morality Classification in Natural Language Text

2020 ◽  
pp. 1-1
Author(s):  
Matheus C. Pavan ◽  
Vitor G. Santos ◽  
Alex G. J. Lan ◽  
Joao Martins ◽  
Wesley Ramos Santos ◽  
...  
Keyword(s):  
Natural Language ◽  
Natural Language Text ◽  
Language Text

scholarly journals A Mobile and Web Application for Mapping Disaster Volunteers’ Position in Indonesia

2017 ◽  
Vol 11 (3) ◽  
pp. 98 ◽  
Author(s):  
Theresia Devi Indriasari ◽  
Kusworo Anindito ◽  
Eddy Julianto ◽  
Bertha Laroha Paraya Pangaribuan
Keyword(s):  
Disaster Management ◽  
Web Application ◽  
Web Applications ◽  
Management Service ◽  
Tectonic Plates ◽  
Location Data ◽  
Before And After ◽  
Disaster Situation ◽  
The Government ◽  
To Receive

<span>Indonesia is a country located on top of some tectonic plates that bring potential natural disasters. Disaster management system is considered essential in controlling the situation in the site both before and after the disaster takes place. In disaster situation, the government and society are involved in a volunteer team in order to help minimize victims and support survivors. However, the volunteering activities are often hindered since there are problems in the disaster site. One of the problems is late responses due to poor coordination among volunteers that drives the delay in disaster relief. Therefore, it is necessary to have an application that maps the positions of volunteers in a disaster site, so that the disaster management coordinator can disseminate volunteers to disaster areas based on needs. The purpose of the study is to propose an application called ‘MyMapVolunteers’ that effectively and efficiently detects the position of the volunteers in order to improve disaster management service. In this case, real time and location based service technology will able to detect the position of each volunteer. ‘MyMapVolunteers’ is composed of two platforms, which are mobile and web applications. Mobile platform is an application that uses GPS function provided by the smartphone to find the volunteers’ location coordinates and then send the data of the location automatically and manually. The web platform is used to receive volunteers’ location data and to present them in google map, therefore disaster management coordinator can monitor the positions of and search for volunteers faster.</span>

scholarly journals Word-based self-indexes for natural language text

2012 ◽  
Vol 30 (1) ◽  
pp. 1-34 ◽  
Author(s):  
Antonio Fariña ◽  
Nieves R. Brisaboa ◽  
Gonzalo Navarro ◽  
Francisco Claude ◽  
Ángeles S. Places ◽  
...  
Keyword(s):  
Natural Language ◽  
Natural Language Text ◽  
Language Text

scholarly journals Maternal Anxiety 16 Months after the Great East Japan Earthquake Disaster Area: First Report

Health ◽  
2014 ◽  
Vol 06 (10) ◽  
pp. 870-878
Author(s):  
Hatsumi Yoshii ◽  
Hidemitsu Saito ◽  
Saya Kikuchi ◽  
Takashi Ueno ◽  
Kineko Sato
Keyword(s):  
Great East Japan Earthquake ◽  
Maternal Anxiety ◽  
Earthquake Disaster ◽  
First Report ◽  
Disaster Area

scholarly journals Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction

2021 ◽  
pp. 147-151
Author(s):  
Maurizio Romano ◽  
Francesco Mola ◽  
Claudio Conversano
Keyword(s):  
Customer Satisfaction ◽  
Word Of Mouth ◽  
Combined Approach ◽  
Natural Language Text ◽  
Textual Data ◽  
Negative Sentiment ◽  
The Relationship ◽  
Time Information ◽  
Day By Day ◽  
Language Text

The importance of the Word of Mouth is growing day by day in many topics. This phenomenon is evident in everyday life, e.g., the rise of influencers and social media managers. If more people positively debate specific products, then even more people are encouraged to buy them and vice versa. This effect is directly affected by the relationship between the potential customer and the reviewer. Moreover, considering the negative reporting bias is evident in how the Word of Mouth analysis is of absolute interest in many fields. We propose an algorithm to extract the sentiment from a natural language text corpus. The combined approach of Neural Networks, with high predictive power but more challenging interpretation, with more simple but informative models, allows us to quantify a sentiment with a numeric value and to predict if a sentence has a positive (negative) sentiment. The assessment of an objective quantity improves the interpretation of the results in many fields. For example, it is possible to identify crucial specific sectors that require intervention, improving the company's services whilst finding the strengths of the company himself (useful for advertising campaigns). Moreover, considering that the time information is usually available in textual data with a web origin, to analyze trends on macro/micro topics. After showing how to properly reduce the dimensionality of the textual data with a data-cleaning phase, we show how to combine: WordEmbedding, K-Means clustering, SentiWordNet, and the Threshold-based Naïve Bayes classifier. We apply this method to Booking.com and TripAdvisor.com data, analyzing the sentiment of people who discuss a particular issue, providing an example of customer satisfaction.

scholarly journals A Semi-Automatic Semantic Annotation and Authoring Tool for a Library Help Desk Service

2011 ◽  
pp. 100-114 ◽  
Author(s):  
Antti Vehviläinen ◽  
Eero Hyvönen ◽  
Olli Alm
Keyword(s):  
Information Needs ◽  
Semantic Annotation ◽  
Real Life ◽  
Semantic Search ◽  
Data Sources ◽  
Case Based Reasoning ◽  
Help Desk ◽  
Natural Language Text ◽  
Language Text ◽  
Case Based

This chapter discusses how knowledge technologies can be utilized in creating help desk services on the Semantic Web. To ease the content indexer’s work, we propose semi-automatic semantic annotation of natural language text for annotating question-answer (QA) pairs, and case-based reasoning techniques for finding similar questions. To provide answers matching the content indexer’s and end-user’s information needs, methods for combining case-based reasoning with semantic search, linking, and authoring are proposed. We integrate different data sources by using large ontologies. Techniques to utilize these sources in authoring answers are suggested. A prototype implementation of a real life ontology-based help desk application, based on an existing national library help desk service in Finland, is presented as a proof of concept.

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