Combining Text Information and Sentiment Dictionary for Sentiment Analysis on Twitter During COVID

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dc.authorscopusidAlaa Ali Hameed / 56338374100
dc.authorwosidAlaa Ali Hameed / ABI-8417-2020
dc.contributor.authorVidushi
dc.contributor.authorJain, Anshika
dc.contributor.authorShrivastava, Ajay Kumar
dc.contributor.authorBhushan, Bharat
dc.contributor.authorHameed, Alaa Ali
dc.date.accessioned2025-04-18T08:56:27Z
dc.date.available2025-04-18T08:56:27Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractPresence of heterogenous huge data leads towards the ‘big data’ era. Recently, tweeter usage increased with unprecedented rate. Presence of social media like tweeter has broken the boundaries and touches the mountain in generating the unstructured data. It opened research gate with great opportunities for analyzing data and mining ‘valuable information’. Sentiment analysis is the most demanding, versatile research to know user viewpoint. Society current trend can be easily observed through social network websites. These opportunities bring challenges that leads to proliferation of tools. This research works to analyze sentiments using tweeter data using Hadoop technology. It explores the big data arduous tool called Hadoop. Further, it explains the need of Hadoop in present scenario and role of Hadoop in storing ample of data and analyzing it. Hadoop cluster, Hadoop Distributed File System (HDFS), and HIVE are also discussed in detail. The Dataset used in performing the experiment is presented. Moreover, this research explains thoroughly the implementation work and provide workflow. Next session provides the experimental results and analyzes of result. Finally, last session concludes the paper, its purpose, and how it can be used in upcoming research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
dc.identifier.citationVidushi, Jain, A., Shrivastava, A. K., Bhushan, B., Hameed, A. A., & Jamil, A. (2023, September). Combining Text Information and Sentiment Dictionary for Sentiment Analysis on Twitter During COVID. In International Conference on Emerging Trends and Applications in Artificial Intelligence (pp. 558-569). Cham: Springer Nature Switzerland.
dc.identifier.doi10.1007/978-3-031-56728-5_46
dc.identifier.endpage569
dc.identifier.isbn978-303156727-8
dc.identifier.issn23673370
dc.identifier.scopus2-s2.0-85193634045
dc.identifier.scopusqualityQ4
dc.identifier.startpage558
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-031-56728-5_46
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6646
dc.identifier.volume960
dc.indekslendigikaynakScopus
dc.institutionauthorHameed, Alaa Ali
dc.institutionauthoridAlaa Ali Hameed / 0000-0002-8514-9255
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofLecture Notes in Networks and Systems
dc.relation.publicationcategoryKitap - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAnalysis
dc.subjectCovid
dc.subjectHadoop
dc.subjectHIVE
dc.subjectSentiment
dc.subjectTwitter
dc.titleCombining Text Information and Sentiment Dictionary for Sentiment Analysis on Twitter During COVID
dc.typeBook Chapter

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