"This is a big data use case because we need to capture, analyse and store a lot of data. There is a lot of information, it is being refreshed often, and there are a lot of different sources," he said.
The company created a prototype in less than two months with open source tools, using Microstrategy to build dashboards.
"We chose to start with Twitter as it is relatively easy to get data from its free API. It is a little bit more complex for Facebook," he said.
The system relies on MongoDB as a NoSQL database to store the tweet information extracted from the Twitter API. The data is then processed in Hadoop using the Hive programming language, with Impala to create queries.
The tool allow the company to view information such as time and data of tweet, geo-location, number of tweets, what industry influencers are discussing, and monitor positive or negative keywords being used in relation to the company.
"For example, this can show that a certain time is better for sending a particular tweet as part of a marketing campaign."
There are now plans to incorporate other feeds, such as Facebook, and integrate the R programming language into Microstrategy to create more robust algorithms to enable customer sentiment analysis.
"We were looking at whether words were positive or negative, but if you are doing only that and you see Michelin near a word that is good or bad, it does not mean the whole sentence is good or bad," he said.
"We are starting to look at sentiment analysis, and are trying to build a more advanced algorithm. For that we need to use R, which is a very complex language, and then we will see the results in Microstrategy."
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