Multimodal Hinglish Tweet Dataset for Deep Pragmatic Analysis

被引:1
作者
Pratibha [1 ]
Kaur, Amandeep [1 ]
Khurana, Meenu [2 ]
Damasevicius, Robertas [3 ]
机构
[1] Chitkara Univ, Inst Engn & Technol, Rajpura 140601, Punjab, India
[2] Chitkara Univ, Sch Engn & Technol, Baddi 173205, Himachal Prades, India
[3] Vytautas Magnus Univ, Dept Appl Informat, LT-53361 Kaunas, Lithuania
关键词
hinglish; pragmatic analysis; sentiment analysis; tweet dataset; SENTIMENT ANALYSIS; MODEL;
D O I
10.3390/data9020038
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Wars, conflicts, and peace efforts have become inherent characteristics of regions, and understanding the prevailing sentiments related to these issues is crucial for finding long-lasting solutions. Twitter/'X', with its vast user base and real-time nature, provides a valuable source to assess the raw emotions and opinions of people regarding war, conflict, and peace. This paper focuses on collecting and curating hinglish tweets specifically related to wars, conflicts, and associated taxonomy. The creation of said dataset addresses the existing gap in contemporary literature, which lacks comprehensive datasets capturing the emotions and sentiments expressed by individuals regarding wars, conflicts, and peace efforts. This dataset holds significant value and application in deep pragmatic analysis as it enables future researchers to identify the flow of sentiments, analyze the information architecture surrounding war, conflict, and peace effects, and delve into the associated psychology in this context. To ensure the dataset's quality and relevance, a meticulous selection process was employed, resulting in the inclusion of explanable 500 carefully chosen search filters. The dataset currently has 10,040 tweets that have been validated with the help of human expert to make sure they are correct and accurate.
引用
收藏
页数:19
相关论文
共 49 条
[31]  
2019, International Journal of Innovative Technology and Exploring Engineering, V8, P18, DOI [10.35940/ijitee.i1003.0789s19, 10.35940/ijitee.I1003.0789S19, DOI 10.35940/IJITEE.I1003.0789S19, 10.35940/ijitee.J9326.0881019]
[32]  
Sasidhar T. Tulasi, 2020, Procedia Computer Science, V171, P1346, DOI [10.1016/j.procs.2020.04.144, 10.1016/j.procs.2020.04.144]
[33]   An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning [J].
Sharif, Waqas ;
Mumtaz, Shahzad ;
Shafiq, Zubair ;
Riaz, Omer ;
Ali, Tenvir ;
Husnain, Mujtaba ;
Choi, Gyu Sang .
APPLIED SCIENCES-BASEL, 2019, 9 (18)
[34]  
Shevtsov A, 2022, Arxiv, DOI arXiv:2204.08530
[35]  
Siapera E., 2022, Inf. Commun. Soc, V22, P1297
[36]  
Sievert C., 2014, Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, P63, DOI 10.13140/2.1.1394.3043
[37]  
Smart Bridget, 2022, Social Informatics: 13th International Conference, SocInfo 2022, Proceedings. Lecture Notes in Computer Science (13618), P34, DOI 10.1007/978-3-031-19097-1_3
[38]  
Srivastava Ananya, 2021, Applications of Artificial Intelligence and Machine Learning: Select Proceedings of ICAAAIML 2020. Lecture Notes in Electrical Engineering (778), P83, DOI 10.1007/978-981-16-3067-5_8
[39]  
Srivastava V., 2021, arXiv
[40]  
Srivastava V, 2020, Arxiv, DOI arXiv:2004.09447