Business Sentiment Quotient Analysis using Natural Language Processing

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.