Digital Media and Business Analytics

Digital media analytics  gathers qualitative and quantitative data from social media websites and analyzes that data using social media analytics tools to facilitate business decisions. Used largely to mine customer behaviour for support marketing and customer service activities, it measures the performance of digital properties and extracts the information in such a way that actionable insights can be deduced from the analysis. To understand the contribution of digital media better, we aim to analyze an organization’s varied requirement and specific contribution of business analytics to a firm’s performance.


Digital Technologies for Innovation labs:
The Atal Innovation Mission’s Atal Tinkering Labs aim to nurture the creative minds of the youth in India and provide a safe, accessible and interactive environment to help experiment and innovate. Keeping this aim in mind, NITI Aayog has set up over 2000 labs and is set to cross 5000 labs by the end of 2018 and they are equipped with 3D printer, sensors,  micro-controllers boards and more.
SRITNE will estimate the impact of these labs on students creativity and innovation after suggesting a set of best practices. 

Digital Media and Doing Business:
The world bank defines a nation's ranking on the Doing Business Index as an average of ten sub-indices. These sub-indices are measures of regulations directly affecting businesses. Although the effects of regulations may be captured through the indices, the perception of the general media and companies of the Ease of Doing Business in these regions is not captured. There is a lack of measure and also, measurement of the dissonance in the perception of Ease of Doing Business between popular media and the NSE500 companies' head officials.

To accomplish this, we will use the Twitter data from official handles of NSE500 companies, their heads (CEOs and MDs) and the official handles of news channels specific to these regions. Using this data, we plan to measure sentiment scores on the corpus of tweets that are relevant to our analysis This corpus will be filtered from the Twitter corpus using Machine Learning based text classifiers trained for classification using lexicons for the ten sub-indices. Also, we plan to assess the variation across cities in the ten sub-indices through this data, after classification and the effects of economic shocks, such as the announcement of demonetization on these metrics through the analysis of time series data obtained from the Twitter corpus.