Classification of Social Media Messages Posted at the Time of Disaster

Kemavuthanon, Kemachart; Uchida, Osamu

doi:10.1007/978-3-030-48939-7_18

Cited by 3 publications

(4 citation statements)

References 16 publications

(12 reference statements)

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“…This process checks the submitted question for similarities by comparing it with previous questions contained in the database. If a question has already been asked, the system will To facilitate answer creation, the system's database maintains important data in two parts: parts extracted from the social media dataset during our previous research [8] and basic data such as location, contact, and coordinate information. By comparing the question keywords with the dataset keywords and basic supplemental data, answer sets can be produced.…”

Section: Question Processing Phase Frameworkmentioning

confidence: 99%

“…For example, "Cat" has a higher compatibility with the category "animal" than the other categories. Moreover, it also counts how frequently the words of each category are used in the sentence [8], as shown in Figure 5.…”

Section: Data Classification Of Social Media Messagesmentioning

confidence: 99%

“…After that, we classified these social media messages using an ontology, word similarity, and keyword frequency, and then we evaluated the natural language processing results to organize those messages into ten categories. The results were then statistically compared with the keyword used to classify each category [8].…”

Section: Introductionmentioning

confidence: 99%

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Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster

Kemavuthanon

Uchida

2020

Information

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Natural disasters are events that humans cannot control, and Japan has suffered from many such disasters over its long history. Many of these have caused severe damage to human lives and property. These days, numerous Japanese people have gained considerable experience preparing for disasters and are now striving to predict the effects of disasters using social network services (SNSs) to exchange information in real time. Currently, Twitter is the most popular and powerful SNS tool used for disaster response in Japan because it allows users to exchange and disseminate information quickly. However, since almost all of the Japanese-related content is also written in the Japanese language, which restricts most of its benefits to Japanese people, we feel that it is necessary to create a disaster response system that would help people who do not understand Japanese. Accordingly, this paper presents the framework of a question-answering (QA) system that was developed using a Twitter dataset containing more than nine million tweets compiled during the Osaka North Earthquake that occurred on 18 June 2018. We also studied the structure of the questions posed and developed methods for classifying them into particular categories in order to find answers from the dataset using an ontology, word similarity, keyword frequency, and natural language processing. The experimental results presented herein confirm the accuracy of the answer results generated from our proposed system.

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Section: Question Processing Phase Frameworkmentioning

confidence: 99%

Section: Data Classification Of Social Media Messagesmentioning

confidence: 99%

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Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster

Kemavuthanon

Uchida

2020

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“…Currently, new technologies such as machine learning based on social media and location services are widely recognized for their use in disaster management [3]. The application of social media data analysis technology in disaster emergency plans and crisis management can effectively improve the efficiency of emergency response management [6].…”

Section: Introductionmentioning

confidence: 99%

A multimodal resource balancing approach based on cross-attention and bidrectional associative memory networks

Wang,

Cheng,

Liu

et al. 2023

Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023)

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In the event of a disaster, mining social media tweets containing disaster information to analyze the dynamics of the disaster can help relevant authorities make quick emergency decisions and public opinion analysis. The data on social media jointly describe a thing to make it semantically related, but there are often structural and semantic imbalances between heterogeneous modalities. Most of the current research is devoted to formal complementation or semantic balancing, and multi-granularity resource balancing is beneficial to better obtain consistent information of heterogeneous modalities and remove redundancy. Based on the above problems, this paper proposes an end-to-end Multimodal Resource Balancing (MMRB) model, which designs cross-attention and two-way associative memory network modules while avoiding the loss of important information of unimodal modalities. The cross-attention module is used to capture the deep semantic correlations between modalities and weight the modal features to achieve semantic-level resource balancing. The associative memory module uses each modal feature to generate a complete feature representation of another modality on the pre-trained migration model to complement multimodal information at the feature level to improve the modal imbalance and modal missing problem of unstructured social media data. Finally, the fused features are weighted by the joint feature codec to reasonably focus on the contribution of different modal features in the joint features and the intrinsic association. Experiments are conducted on CrisisMMD, a public data of social media graphics used for disaster detection, and the accuracy of the model is stronger than the current multimodal resource balancing model, which verifies the effectiveness of our model.

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Text Analytics on Twitter Text-based Public Sentiment for Covid-19 Vaccine: A Machine Learning Approach

Adamu

Jiran

Gan

et al. 2021

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

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Classification of Social Media Messages Posted at the Time of Disaster

Cited by 3 publications

References 16 publications

Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster

Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster

A multimodal resource balancing approach based on cross-attention and bidrectional associative memory networks

Text Analytics on Twitter Text-based Public Sentiment for Covid-19 Vaccine: A Machine Learning Approach

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