Deep belief networks have made it possible to train computers to predict if a sentence is positive, negative or neutral. Most sentiment analysis captures headlines because tweets can be analysed. However are there business applications beyond social networking analytics?
Here are five examples:
1) Investment banking – reading complex reports
The financial industry is shaving off microseconds for high-frequency trading. However these algorithms assume that they can predict what a single big trade will be like. What if super computers would analyse any governmental report, news feed, etc. in real-time at a fraction of the time a human can do this. Initially these algorithms could get the most import data in front of analysts but there is no reason why automatic algorithms would not be able to make trades. There could be algorithms that look for natural disasters. Others that look at the sentiment of national bank reports.
2) Telecom: detecting defects and reading complaints
What do you do when call quality is bad? You send an SMS to the other person with your message plus some insult about your mobile provider. If your bill is too high, then you call their call centre or open a complaint on the website. Computers can more efficiently detect patterns in this behaviour than humans and can raise alerts before large groups of customers start to complain on Twitter.
3) IT: log processing and intrusion detection
Often strange user behaviour can be detected by analysing the commands that are introduced on a command line. Are they neutral, positive or negative? A hacker that is trying to exploit a bug and afterwards enters into log files to destroy their tracks could be caught because their commands are highly negative.
4) Retail: product reviews
What if a customer starts leaving bad reviews? Or even worse average reviews because they feel bad about a certain feature or services but not about the overall experience. Would you rather have a computer tell you in advance or wait until a crowd gathers enough tweets?
5) Politics: election sentiment
Real-time dashboards with sentiments for different candidates by analysing all written press. Find out what voters feel strong about.