Quite a lengthy, complete and interesting view I am looking at. In Linkedin.
Sentiment Analytics Triology - A master class on extracting sentiments or aspects from data
Published on September 28, 2016 Featured in: Customer Experience, India, Technology ... A master class on extracting sentiments or aspects from data by Snehamoy Mukherjee, Data Science & Analytics Leader
Credit goes to my colleagues Sumit Pratap Singh, Prateek Sharma, Kuntal Basu and Subhamoy Ganguly for delving deep into this field and creating an outstanding capability in this much talked about field. The article below is the first of a series of articles, which we intend to publish over the course of the next few days, and is an attempt to articulate the wisdom and share it with a wider audience, so that others can benefit from it. Hence, I have called it a master class as there is a touch of pedagogy in this attempt to carve out the essence of a complex topic.
One of the areas of analytics/data science that has generated a huge amount of hype and interest of late, in the industry has been “sentiment analytics”. And yet, interestingly, though a lot of research and investment has gone into it, there haven’t been many takers for it from the point of view of business users. One of the major reasons for it has been that sentiment analytics has to deal with a lot of noise in the data, which the conventional, automated, “plug and play” solutions can’t handle. A lot of folks in the industry tried to come up with tools and products and trivialized “sentiment analytics” into a product play and hence, nipped an emerging industry in the bud.
A well-executed sentiment analysis needs a lot of human intelligence to interpret the results as well as to coach the self-learning algorithms to do the right things. In this series of three papers, which we can call the “Sentiment Analytics Trilogy”, we provide a successful approach to performing sentiment analysis that gives outperforms the droves of so called “sentiment analytics products and tools” in the market, which are mostly unstable and error prone. ... "