ItemConference Paper

Cooperative Social Entrepreneurship Among Rural Artisans: A YouTube-Based NLP Analysis of Environmental Practices and Livelihood Outcome

Title
Cooperative Social Entrepreneurship Among Rural Artisans: A YouTube-Based NLP Analysis of Environmental Practices and Livelihood Outcome
Creator
Davis, Antony; Lakshmypriya, K.; Basha, Md Shaik Amzad; Daphne, Simita
Description
This paper presents a novel data-driven study of rural artisans in Karnataka by leveraging YouTube video transcripts and natural language processing (NLP) to examine how cooperative social entrepreneurship (CSE) relates to environmental practices and livelihood outcomes. CSE initiatives in India typically rely on primary surveys to understand how artisanal groups adopt eco-friendly practices and how this affects their livelihoods, but such data are costly to collect and difficult to scale. We investigate whether publicly available video narratives can serve as a scalable secondary data source for studying CSE among rural artisans. We compile a corpus of YouTube videos on banana-fibre craft, Anegundi/Hampi artisan collectives, and Karnataka handicrafts. Audio is transcribed using an automatic speech recognition pipeline, and the resulting bilingual/multilingual text (English-Hindi-Kannada) is processed with a rule-based NLP tagger to identify three constructs central to our CSE perspective: (i) artisan and community references (CSE signals), (ii) environmental practices (e.g., "banana waste to fibre,""eco-friendly,""sustainable"), and (iii) livelihood/product mentions (e.g., baskets, mats, runners) as observable proxies for livelihood outcomes. On top of this, we apply text mining techniques topic modeling, sentiment analysis, and supervised classification with fine-tuned transformer models (BERT) to classify transcript segments (e.g., environmental focus vs livelihood focus) and extract key thematic topics. Experimental results show that our BERT-based classifier achieves over 90% accuracy, substantially outperforming traditional baselines such as TF-IDF+SVM and LSTM. The videos frequently encode both CSE signals and explicit environmental practices, and a non-trivial subset articulates marketable products, suggesting that platform narratives can partially capture the CSE-environment-outcome chain without questionnaires. However, explicit statements about market, seasonality, or constraint variables remain sparse, revealing limitations of video-based secondary data. The study contributes methodologically by integrating digital media analytics into rural development research, offers complexity and performance analysis of the employed algorithms, and stresses reproducibility through transparent documentation of data sources, model architectures, training configurations, and evaluation metrics. 2025 IEEE.
Source
Proceedings of 6th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2025;pp.1766-1771
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Cooperative social entrepreneurship; HandiCrafts; Karnataka; Rural Artisans
Coverage
Davis A., CHRIST (Deemed to be University), Christ Academy Institute for Advanced Studies, School of Business & Management, Bangalore, India; Lakshmypriya K., CHRIST (Deemed to be University), School of Business & Management, Bangalore, India; Basha M.S.A., Gandhi Institute of Technology and Management ((Deemed to be University), Christ Academy Institute for Advanced Studies, GITAM School of Business, Bangalore, India; Daphne S., Christ Academy Institute for Advanced Studies, Department of Management Studies, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833151520-1;
Format
online
Language
English
Type
Conference paper

Citation

Davis, Antony; Lakshmypriya, K.; Basha, Md Shaik Amzad; Daphne, Simita, “Cooperative Social Entrepreneurship Among Rural Artisans: A YouTube-Based NLP Analysis of Environmental Practices and Livelihood Outcome,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26018.

Output Formats