You did not need any kind of scientific sentiment analysis to decode the message that you read on social media the day of your daughter’s birthday.
When your daughter’s ex-boyfriend’s mother posted about making time for those that you care about, the message was clear. She was not happy with the break up, and while you have never really found out who initiated the split, the following message was a pretty strong indicator that much of the blame is being put on your daughter:
Never force people to be in your life. There’s only room for those that want to be in your life, and you make time only for those that want to be. Do not take the time for the ones that make excuses for not being able to be in your life.
Kind of subtle, kind of not, the message posted on the day when your daughter would have been celebrating her 20th birthday with her boyfriend seemed to send a loud message.
In a time when so many people post their emotions online, it should come as no surprise that companies, political campaigns, and other groups are making use of technology like sentiment analysis and text mining software to measure the mood and the interests of the general public. By looking for certain words, for instance, sentiment analysis platforms can help companies test the timing for a new product or predict the kind of commercials that consumers might respond the most positively to. Using semantic extraction and geotagging software, for instance, a company can not only find out how close a customer is to a specific retail location, but they can also predict the current mood of online posts that day in an attempt to post pertinent offers.
Consider some of these statistics about the use of social media posts and the valuable information that they can provide:
- 1.97 billion people are active on Facebook every month. These worldwide users produce an enormous amount of data every single second.
- 319 million monthly active users across the world use Twitter.
- Estimates indicate that there will be 2.67 billion social media users worldwide by the year 2018, this would be an increase from 2.34 billion in the year 2016.
- Entity resolution is the text mining practice of finding and linking mentions of the same entity within and across data sets. The three primary tasks involved in entity resolution are deduplication, canonicalization, and record linkage.
- $3 billion is the current value of the text analytic market today, but it is forecasted to reach almost $6 billion by the year 2020.
As social beings, many of us spend a lot of time trying to read the meanings behind the messages that we receive. Companies have found a way to use geotagging software, sentiment analysis, and other online data to help them read the current location, and even moods, of their average customer.
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