Multimodal Hinglish Tweet Dataset for Deep Pragmatic Analysis

Pratibha, Amandeep Kaur, Meenu Khurana and Robertas Damaševičius ()
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Pratibha: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140601, Punjab, India
Amandeep Kaur: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140601, Punjab, India
Meenu Khurana: Chitkara University School of Engineering and Technology, Chitkara University, Baddi 173205, Himachal Pradesh, India
Robertas Damaševičius: Department of Applied Informatics, Vytautas Magnus University, LT-53361 Kaunas, Lithuania

Data, 2024, vol. 9, issue 2, 1-19

Abstract: 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.

Keywords: hinglish; pragmatic analysis; sentiment analysis; tweet dataset (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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