Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

Hernandez-Suarez, Aldo; Sánchez-Pérez, Gabriel; Toscano-Medina, Karina; Pérez, Héctor; Portillo-Portillo, Jose; Sánchez, Vı́ctor

doi:10.20944/preprints201808.0269.v1

Cited by 4 publications

(6 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some researchers used named entity recognition (NER) to extract data from Twitter. Hernandez et al proposed NER locations with a kernel density estimation function for different subtopics originating from a natural disaster and that maps them into a geographic space (Hernandez-Suarez et al, 2018). This research is conducted for earth Mexico 2017 and used four classes NER to extract information.…”

Section: Six Classes Named Entity Recognition 395mentioning

confidence: 99%

Six classes named entity recognition for mapping location of Indonesia natural disasters from twitter data

Girsang,

Noveta

2024

IJICC

View full text Add to dashboard Cite

PurposeThe purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter data. The Twitter text is extracted by using named entity recognition (NER) with six classes hierarchy location in Indonesia. Moreover, the tweet then is classified into eight classes of natural disasters using the support vector machine (SVM). Overall, the system is able to classify tweet and mapping the position of the content tweet.Design/methodology/approachThis research builds a model to map the geolocation of tweet data using NER. This research uses six classes of NER which is based on region Indonesia. This data is then classified into eight classes of natural disasters using the SVM.FindingsExperiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data Twitter. The results also show good performance in geocoding such as match rate, match score and match type. Moreover, with SVM, this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region, which originate from the tweets collected.Research limitations/implicationsThis study implements in Indonesia region.Originality/value(a)NER with six classes is used to create a location classification model with StanfordNER and ArcGIS tools. The use of six location classes is based on the Indonesia regional which has the large area. Hence, it has many levels in its regional location, such as province, district/city, sub-district, village, road and place names. (b) SVM is used to classify natural disasters. Classification of types of natural disasters is divided into eight: floods, earthquakes, landslides, tsunamis, hurricanes, forest fires, droughts and volcanic eruptions.

show abstract

Section: Six Classes Named Entity Recognition 395mentioning

confidence: 99%

Six classes named entity recognition for mapping location of Indonesia natural disasters from twitter data

Girsang,

Noveta

2024

IJICC

View full text Add to dashboard Cite

show abstract

“…In this section, the detailed description of the RDTFD process is explained which is utilized to compute the term frequency and input data frequency. The term frequency is computed from the total number of words and a total number of terms [19]. This calculation of the words is utilized to compute the similarity which enhances the detection process.…”

Section: Rdtfd Featuresmentioning

confidence: 99%

“…The term frequency is computed from the total number of words and a total number of terms. 23 This calculation of the words is utilized to compute the similarity which enhances the detection process. This feature extraction method is mathematically formulated as follows,…”

Section: Rdtfd Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced conditional random field‐long short‐term memory for name entity recognition in English texts

Bhumireddypalli¹,

Koppula²,

Koppula³

2023

Concurrency and Computation

View full text Add to dashboard Cite

Named Entity recognition (NER) is the essential topic in the real world during the advanced development of technologies. Hence, in this paper, to develop Enhanced Conditional Random Field-Long Short-Term Memory (ECRF-LSTM) for NER in English language. The proposed ECRF-LSTM is combination of Conditional Random Field-Long Short-Term Memory (ECRF-LSTM) and Arithmetic Optimization Algorithm (AOA). This proposed method is utilizing to NER from the English texts. The proposed method is working with three phases such as preprocessing phase, feature extraction phase, and NER phase. Initially, the datasets are collected from the online system. In the pre-processing phase, removal of URL, removal of special symbol, username removal, tokenization and stop word removal are done. After that, the essential features such as domain weight, event weight, textual similarity, spatial similarity, temporal similarity, and Relative Document-Term Frequency Difference (RDTFD) are extracted and then applied for training the proposed model. To empower the training phase of CRF-LSTM method, AOA is utilized to select optimal weight parameter coefficients of CRF-LSTM for training the model parameters. The proposed method is validated by statistical measurements and compared with the conventional methods such as Convolutional Neural Network-Particle Swarm Optimization (CNN-PSO) and Convolutional Neural Network (CNN) respectively.

show abstract

“…The other attribute is its in situ nature, and that it can be gathered in the case of local conditions such as street accessibility, blocked streets, injured people, damages to buildings, etc. [18]. These data can be used in post-crisis assessments in the provision of information layers that are less time-consuming than those based on remote sensing methods.…”

Section: Introductionmentioning

confidence: 99%

Earthquake Damage Assessment Based on User Generated Data in Social Networks

Ahadzadeh

Malek

2021

Sustainability

View full text Add to dashboard Cite

Natural disasters have always been one of the threats to human societies. As a result of such crises, many people will be affected, injured, and many financial losses will incur. Large earthquakes often occur suddenly; consequently, crisis management is difficult. Quick identification of affected areas after critical events can help relief workers to provide emergency services more quickly. This paper uses social media text messages to create a damage map. A support vector machine (SVM) machine-learning method was used to identify mentions of damage among social media text messages. The damage map was created based on damage-related tweets. The results showed the SVM classifier accurately identified damage-related messages where the F-score attained 58%, precision attained 56.8%, recall attained 59.25%, and accuracy attained 71.03%. In addition, the temporal pattern of damage and non-damage tweets was investigated on each day and per hour. The results of the temporal analysis showed that most damage-related messages were sent on the day of the earthquake. The results of our research were evaluated by comparing the created damage map with official intensity maps. The findings showed that the damage of the earthquake can be estimated efficiently by our strategy at multispatial units with an overall accuracy of 69.89 at spatial grid unit and Spearman’s rho and Pearson correlation of 0.429 and 0.503, respectively, at the spatial county unit. We used two spatial units in this research to examine the impact of the spatial unit on the accuracy of damage assessment. The damage map created in this research can determine the priority of the relief workers.

show abstract

Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

Cited by 4 publications

References 38 publications

Six classes named entity recognition for mapping location of Indonesia natural disasters from twitter data

Six classes named entity recognition for mapping location of Indonesia natural disasters from twitter data

Enhanced conditional random field‐long short‐term memory for name entity recognition in English texts

Earthquake Damage Assessment Based on User Generated Data in Social Networks

Contact Info

Product

Resources

About