Business Sentiment Quotient Analysis using Natural Language Processing
Syed Salim1, Madhu B K2
1Syed Salim*, Research Scholar, Department of Computer Science & Engineering, Vidya Vikas Institute of Engineering & Technology, Mysuru,
2Visvesvaraya Technological University, Belagavi, India.
3Dr. Madhu B K, Professor and Head, Department of Computer Science & Engineering, Vidya Vikas Institute of Engineering & Technology, Mysuru, Visvesvaraya Technological University, Belagavi, India.
Manuscript received on March 30, 2020. | Revised Manuscript received on April 05, 2020. | Manuscript published on April 30, 2020. | PP: 1350-1352 | Volume-9 Issue-4, April 2020. | Retrieval Number: D8721049420/2020©BEIESP | DOI: 10.35940/ijeat.D8721.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Online business has opened up several avenues for researchers and computer scientists to initiate new research models. The business activities that the customers accomplish certainly produce abundant information /data. Analysis of the data/information will obviously produce useful inferences and many declarations. These inferences may support the system in improving the quality of service, understand the current market requirement, Trend of the business, future need of the society and so on. In this connection the current paper is trying to propose a feature extraction technique named as Business Sentiment Quotient (BSQ). BSQ involves word2vec[1] word embedding technique from Natural Language Processing. Number of tweets related to business are accessed from twitter and processed to estimate BSQ using python programming language. BSQ may be utilized for further Machine Learning Activities.
Keywords: Word2vec, Business Sentiment Quotient, Natural Language Processing.