Since 2010 automated sentiment analysis has been a source of debate around the Net with questions like: What is this methodology? How does sentiment analysis work? In what context is it useful for a business? But the truth is that what is useful for a company may not be for others. In this post we will answer some of these questions:
Searches about "sentiment analysis" are increasing, particularly related to Twitter. However not all the tools avaliable in the market can face the problem analyzing 140 characters. In this post we will show you:
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Real comments analized
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See how text is structured
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Short texts are not an issue
Every business has only one reputation, and in a highly competititve market like the hospitality one, there is just one chance to give a good impression to your clients.
A recent report from Accenture suggested 87% of people now use a second screen while watching TV to comment not only shows but advertisement. Interactions in Social Media generate dialogue and analyzing all those coments can be overwhelming, but it's necessary to track the success of off-line campaigns. In this post we want to share with you how to measure these second screen interactions based in our experience with real clients.
Last week we talked about how much time and effort is saved by sentiment analysis tools, which greatly reduce the amount of time and effort required to prepare your datasets for analysis and gain insight into what customers are telling you.