Sentiment Analytics – Judging Your Customers’ Statements Polarity

How you know whether your sentiment analytics engine is exactly guessing the sentiment oozed by every word that it reads correctly and finally, letting you know the sentiment polarity of the entire text? 

Welcome to sentiment analytic applications, which promise you to tell everything about what your customers are feeling and mentioning about your product/service on different social media. Facebook, Twitter, MySpace, Pinterest, WhatsApp, etc., are popular social media that our current generation is using. Nowadays, to understand the market sentiment of one’s own product or service, the respective company can skim through the text/audio/etc. mentions of its patrons and know its sentiment polarity.

This blog is trying to find out about how many sentiment analytic applications in this current market are exactly in a position to decipher precisely what a product/service patron are talking about it in the social media.  What are the parameters they are taking into consideration before judging whether a particular word usage is positive or negative?

Besides, whether the Natural Language Processing [NLP] engine in the application understands in what context does that statement was mentioned by the product/service patrons? What they mean by that based on the area they are residing in [area slang matters a lot in understanding the statement! For example, “that was nasty, man!” is a positive statement in US whereas in India it is treated as a negative statement.]. Unless the sentiment analytic application has this kind of capability, companies cannot expect a genuine feedback about their respective product/service which they are offering to the market.

In summary, before running the application on a particular set of statements, the operator has to customize the application by telling it that a particular way of saying is good or bad based on that respective geography. Without telling all this stuff if you try to run the application, then you won’t get correct results. ‘Geographies factor’ matter a lot when it comes to judging the results of a sentiment analytics application. 

Does your application have this kind of features?