Well, OK, if you're like me, you'd probably buy a winning lottery ticket or two, quit working for a living, and retire immediately.
Linguistic sentiment analysis won't make you psychic, but as this field develops, it promises to provide near-psychic predictive abilities.
Linguistic sentiment analysis studies the words used in various types of content and looks for indicators of certain sentiment, opinions, or emotions. It tests how the content fits into multiple classes (such as "positive" vs. "negative"), a polar range (such as how much someone likes a movie on a scale of 1 to 5), or a range of opinion strength. From there, the content can be classified as expressing a certain sentiment, being about a certain topic, coming from a certain kind of document, or even being written by a person from a certain demographic group.
Consequently, there's a lot more these days to social media analytics than measuring "Likes." Companies can derive business intelligence from the sentiment in social networking content. In this way, they can get perhaps the most effective BI of all, getting information directly from the horses' mouths -- the general populace.
Most of the social deployment of sentiment analysis has been limited to searching for positive and negative sentiment about particular brands and features. This is a bit like owning a Porsche when the only driving you do is to the 7-Eleven two blocks away.
There is an entire world of information that sentiment analysis can harness -- through subtle cues beyond mere positivity and negativity.
Research has shown automated sentiment analysis can tell us a lot from as little as a single Tweet or other social network post, with human-like or better-than-human accuracy. Here are some examples:
- Basic facts about the author's identity, such as gender, political affiliation, and geographic region.
- How the author uses social media, and what "type" of social media user the author is.
- Whether the author is feeling calm, anxious, worried, fearful, happy, alert, sure, or numerous other emotions.
More excitingly, as researchers are able to determine correlations between sentiment expressions and particular events, linguistic sentiment analysis can predict the future. For instance, researchers have found that the sentiment in Tweets could be analyzed to predict such things as film box-office receipts and election outcomes. Additionally, linguistic sentiment analysis has repeatedly been shown to be able to predict stock market performance. One investment firm has even launched a hedge fund that bases investment decisions on linguistic sentiment analysis of Tweets. Its algorithm showed 87.6 percent accuracy in a study.
It would hardly be unreasonable to suggest that sentiment analysis of social media content could be used one day to predict such things as what toy will sell best in a particular Christmas season and which political party will be in control after an election.
As AllAnalytics.com Community Editor Shawn Hessinger recently commented, "The kind of analytics we are discussing here goes beyond the garden variety and could perhaps be seen as a macro view of the social media ecology with very unique insights far beyond the reach of predicting, say, most people's favorite antacid."
Scientific developments in the past few years have only started to reveal the predictive potential of these insights. When it comes to the full breadth, depth, and value of the BI that linguistic sentiment analysis of social content has to offer, we are only limited by our imagination and our ingenuity.