How Indian libraries tweet? Word frequency and sentiment analysis of library tweets

131-139The study attempts to map Twitter activity of selected Indian libraries using word frequency and sentiment analysis. Tweets of 18 libraries’ (5 academic libraries, 5 government libraries, 5 school libraries and 3 public libraries) were downloaded during June and July 2019. ‘R’ software was used for the analysis. The study finds that Indian libraries are less active on Twitter. The word cloud based on the most frequently occurring words from the Tweets observed variations in Tweets depend upon the type of libraries. Sentiment analysis of Tweets showed that most of the libraries’ Tweets are positive in nature. The study recommends that Indian libraries should use twitter to promote their collections and services

How Indian libraries tweet? Word frequency and sentiment analysis of library tweets

The study attempts to map Twitter activity of selected Indian libraries using word frequency and sentiment analysis.Tweets of 18 libraries’ (5 academic libraries, 5 government libraries, 5 school libraries and 3 public libraries) were downloaded during June and July 2019. ‘R’ software was used for the analysis. The study finds that Indian libraries are less active on Twitter. The word cloud based on the most frequently occurring words from the Tweets observed variations in Tweets depend upon the type of libraries. Sentiment analysis of Tweets showed that most of the libraries’ Tweets are positive in nature. The study recommends that Indian libraries should use twitter to promote their collections and services

Sentiment analysis of text with lossless mining

Social networks are becoming more and more real with their power to influence public opinions, election outcomes, or the creation of an artificial surge in demand or supply. The continuous stream of information is valuable, but it comes with a big data problem. The question is how to mine social text at a large scale and execute machine learning algorithms to create predictive models or historical views of previous trends. This paper introduces a cyber dictionary for every user, which contains only words used in tweets - as a case study. Then, it mines all the known and unknown words by their frequency, which provides the analytic capability to run a multi-level classifier

Big Data in Health Care Analytics

In the 21st century, availability of large data makes the task of handling it even more difficult. Big data serves to tackle these problems. The growth of data in recent years has been exponential, therefore we explore the prospects, challenges and applications of Big Data. Digitization of Health Care data has seen health records, grown enormously. A patient’s medical insurance data, DNA data, medical test results and health history all stored electronically. This data, being unstructured make it very difficult to extract information and analyze patterns that can be useful. We examine the ways in which we can use Big data in health care so that it’s potential can be fully tapped