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Business boosting through sentiment analysis using Artificial Intelligence approach

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Abstract

In the recent years, Artificial Intelligence has conquered every field whether it is health sector, financial sector, satellite system, farming sector and many more. Artificial Intelligence has enhanced the performance of all these sectors. In this paper, the focus will be on business performance and the AI methods will be applied in the form of machine learning and deep learning. This paper will present how Artificial Intelligence has enhance the business through the sentiment analysis. The work has also discussed the sentiment analysis approach for the business applications. The paper has covered all the aspects with respect to artificial intelligence in the business domain with its advantages for enhancing the performance of the business. The work has also described the natural language processing for performing the sentiment analysis through which business performance can be boosted.

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Funding

The study is not from any institutional research project. So no relevant funding information.

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Authors and Affiliations

  1. School of Accounting, Jiujiang University, 551 Qianjin Donglu, Jiujiang, Jiangxi, China

    Alim Al Ayub Ahmed

  2. Department of Business Studies, European International College, Abu Dhabi, United Arab Emirates

    Sugandha Agarwal

  3. Faculty of Economics, Management, Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia

    IMade Gede Ariestova Kurniawan

  4. International Univ Liaison Indonesia, BSD City, Serpong, Tangerang, Indonesia

    Samuel P. D. Anantadjaya

  5. Amity International Business School, Amity University, Noida, India

    Chitra Krishnan

Authors
  1. Alim Al Ayub Ahmed
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  2. Sugandha Agarwal
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  3. IMade Gede Ariestova Kurniawan
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  4. Samuel P. D. Anantadjaya
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  5. Chitra Krishnan
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Correspondence to Alim Al Ayub Ahmed.

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Ahmed, A.A.A., Agarwal, S., Kurniawan, I.G.A. et al. Business boosting through sentiment analysis using Artificial Intelligence approach. Int J Syst Assur Eng Manag 13 (Suppl 1), 699–709 (2022). https://doi.org/10.1007/s13198-021-01594-x

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