Combining Text Information and Sentiment Dictionary for Sentiment Analysis on Twitter During COVID
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Science and Business Media Deutschland GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Presence 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.
Açıklama
Anahtar Kelimeler
Analysis, Covid, Hadoop, HIVE, Sentiment, Twitter
Kaynak
Lecture Notes in Networks and Systems
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
960
Sayı
Künye
Vidushi, 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.
