Twitter is one of the most common social networking platforms, and millions of tweets are generated every hour. Various non-profit organizations and relief agencies monitor Twitter data to control and help the people in emergency and need. The popularity and accessibility of smartphone to people has made this scenario possible. It allows the user to announce the emergency they are facing in real time. This paper proposes a novel framework incorporating a deep learningDeep Learning architecture transformerTransformers to predict the tweets that signify the disaster situation. The disaster dataset based on Twitter is collected, preprocessing the data. The preprocessing steps include tokenization and lemmatization, stop word removal, and word entity recognition. The individual informative tweets are extracted and enriched using semanto sim and Twitter semantic similarityTwitter Semantic Similarity after preprocessing the dataset. Disaster ontology is generated using ontocolabOntocolab, and it is incorporated with the enriched words to generate metadataMetadata for the disaster dataset.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    TPredDis: Most Informative Tweet Prediction for Disasters Using Semantic Intelligence and Learning Hybridizations


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Sharma, Sanjay (Herausgeber:in) / Subudhi, Bidyadhar (Herausgeber:in) / Sahu, Umesh Kumar (Herausgeber:in) / Arulmozhivarman, M. (Autor:in) / Deepak, Gerard (Autor:in)

    Kongress:

    International Conference on Robotics, Control, Automation and Artificial Intelligence ; 2022 November 24, 2022 - November 26, 2022



    Erscheinungsdatum :

    2023-11-18


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Tweet, tweet

    Online Contents | 2010


    Tweet analytics and tweet summarization using graph mining

    Naik, Apeksha P. / Bojewar, Sachin | IEEE | 2017


    Fake Tweet Data Analysis using Machine Learning Methods

    Garg, Shivani / Dubey, Akshay | IEEE | 2021



    TWEET CREATION ASSISTANCE DEVICE

    NANBA TOSHIYUKI | Europäisches Patentamt | 2015

    Freier Zugriff