Deciphering Customer Satisfaction Through Sentiment Analysis


Is your business “socially intelligent ? ?

If you’re not prompt enough in monitoring media that goes way beyond just ‘@’ or ‘#’ mentions, then the answer is obviously ‘NO’. And even if you are doing it then you might be missing the most important factor, i.e. analyzing customer sentiments.

“The most common mistake that people make is they just listen for brands and miss out all the important conversations.”

Web holds vast, valuable and unstructured information about public opinion. Possessing the right method to evaluate these mentions might help you in making a positive brand presence and dissolving what is making your customers unhappy.

What could be better than having technology magically comb out brand-relevant comments from the Web?

Sentiment analysis has been evolving since its existence and has reached to a whole new level in the recent times.

- from experimental to communicative structure

- from coarse to fine-grained analysis

- from mere keywords to more detailed concepts

Often, digging opinions and sentiments from naturally used language is quite challenging, as it requires a thorough understanding of the implicit and explicit, syntactical and semantic, and regular and irregular rules of language.

Consider an example — Generally words like ‘great’ or ‘excellent’ are considered to be positive but in a phrase like ‘it was not great’, the whole meaning is upturned. In a similar event, if a tweet like “Two of the players from the team played exceptionally well — it was deadly” is tweeted around hundred times, then it is likely that the automated sentiment analysis take it as a negative feedback and the results are misguided.

Here, the need is to have human supervision over the automated analysis, so that the results can be filtered and made precisely correct. It is better to listen to what people are saying, and then evaluate how a particular brand fits in. For instance, instead of finding out the percentage of people talking negatively or positively about barbecue sauce, find how they cook or how would they like to cook?

Apart from that, choosing a better PR Analytics service will help you dig out and evaluate each and every mention of your brand in the media. These self-asked questions might prove to be helpful in making a better choice -

1. Does your provider offers multi-language support?
2. Are the sentiments correctly analyzed? (In case of charitable campaigns and natural calamity relief, where sentiments are portrayed in a negative sense but your brand is viewed in positive sense)
3. What level of accuracy you desire from sentiments of different content levels — from tweets to blogs and articles?
4. Does your provider promptly offer better and advanced ways to rectify inaccurate sentiment analysis?

By properly embedding these factors into your decision making process can surely give you results as desired. So, now it depends on you, how you modify your Media Monitoring Service selection and turn every mention into assured brand success.

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