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Analyzing the startup ecosystem of India: a Twitter analytics perspective

Author

Listed:
  • Shiwangi Singh
  • Akshay Chauhan
  • Sanjay Dhir

Abstract

Purpose - The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India. Design/methodology/approach - The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India. Findings - The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India. Research limitations/implications - The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue. Originality/value - Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.

Suggested Citation

  • Shiwangi Singh & Akshay Chauhan & Sanjay Dhir, 2019. "Analyzing the startup ecosystem of India: a Twitter analytics perspective," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 17(2), pages 262-281, November.
    • Handle: RePEc:eme:jamrpp:jamr-08-2019-0164
    DOI: 10.1108/JAMR-08-2019-0164
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    Citations

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    Cited by:

    1. Zachlod, Cécile & Samuel, Olga & Ochsner, Andrea & Werthmüller, Sarah, 2022. "Analytics of social media data – State of characteristics and application," Journal of Business Research, Elsevier, vol. 144(C), pages 1064-1076.
    2. Srivastava, Saurabh & Singh, Shiwangi & Dhir, Sanjay, 2020. "Culture and International business research: A review and research agenda," International Business Review, Elsevier, vol. 29(4).
    3. Yunhwan Kim, 2023. "Exploring Organizational Self-(re)presentations on Visual Social Media: Computational Analysis of Startups’ Instagram Photos Based on Unsupervised Learning," SAGE Open, , vol. 13(4), pages 21582440231, December.
    4. Shiwangi Singh & Sanjay Dhir, 2019. "Structured review using TCCM and bibliometric analysis of international cause-related marketing, social marketing, and innovation of the firm," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 16(2), pages 335-347, December.
    5. Agarwal, Neha Dhruv & Kumar, V.V. Ravi & Gunasekaran, Angappa, 2024. "Leveraging social media to examine sustainability communication of home appliance brands," Technology in Society, Elsevier, vol. 77(C).
    6. Singh, Shiwangi & Dhir, Sanjay & Das, V. Mukunda & Sharma, Anuj, 2020. "Bibliometric overview of the Technological Forecasting and Social Change journal: Analysis from 1970 to 2018," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    7. Paul, Justin & Parameswar, Nakul & Sindhani, Mohit & Dhir, Sanjay, 2021. "Use of microblogging platform for digital communication in politics," Journal of Business Research, Elsevier, vol. 127(C), pages 322-331.

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