With the arrival of text analytics, Voice of Customer data become a crucial resource which provides the managers and marketing practitioners with consumer’s indirect opinion and requirements. The use of VOC data improves the customer responsiveness and satisfaction and entually improves business performance. It can be used for predicting service time based on voice of customer data. I have studied customer sentiments on social media for the gadgets of automobiles.
I have used Lexicon based and Machine Learning approaches for the social media sentiment analysis. The customers use social network sites like Twitter, Facebook, Instagram etc. so on as they offer very user friendly environment and comfort, user express their views freely. The scientists and researchers can use the social media platforms to get the data and can understand the customer behaviour and their sentiment towards the products and brands. The consumers’ comments and expressions over social networks can directly affect the brand. The companies also adopt social networks for their product promotions and Twitter and Facebook are the most widely used social media platform by them.The consumers react, complain and appreciate them on the comments which will be valuable data for the companies. Customers also spend more time in online marketing rather the actual one. The requirement is here is to identify what customer enjoys the most of the products and revisit.
The sentiment analysis is classifying the polarity of the text or sentence to be positive, negative, or neutral. Sentiment classification also makes uses of the emotional states like "angry", "sad", and "happy". I would like to use Text Analytics modelling approach to find the VOC from social media. The data has been extracted by web scrapping and to combine the voice of customers from social media like Facebook, Twitter and web scrapping from other platforms. The research will help the companies to get the voice of customer from multiple social media and can consider as input for business decision making on their products and commercializing the product for promotional and aftermarket voice of customers.
Here, I want to propose a dashboard of consumer sentiment for the Automotive Gadgets from Twitter and Facebook and finally an API which helps the enterprises for their market research, virtual market surveys and to identify voice of customers.
I am grateful to Mr. Akshay Kulkarni for the mentoring and for the valuable guidance provided as my project guide to understand the concept and in executing this project.
I am highly indebted to Dr. Shinu Abhi, Director of Corporate Training, REVA Academy For Corporate Excellence, REVA University and Chief Mentor, Dr. Jay Bharateesh Simha for all the support and guidance during the course and my project.
Suresha Kukkaje and Akshay Kulkarni