Sentiment analysis has become somewhat of a buzzword — akin to
blockchain — many have heard of it, yet most fail to understand how it works, what it is used for, why it is such an important revelation for a modern-day business, and more importantly — how it can be used even for micro-enterprises of today’s business landscape.
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This article aims to de-mystify the new-age technology and give insights into how businesses can use data-driven tech to enhance their operations further.
The misconception
Here’s something that many in today’s world fail to realize, sentiment analysis has existed since the early 1950s. It was toyed with by mathematicians and linguists long before
artificial intelligence (AI) was involved.
In fact, the aforementioned statement is a trick — sentiment analysis may not exactly be what you think.
When most uninformed individuals are asked about sentiment analysis, they often envision big data, AI, and
algorithms that can predict consumer behavior and evaluate responses based on written data found online.
In reality, taking blocks of texts and mining them for opinions via algorithms that use big data gathered from the internet, or domain which the algorithms are given access to, is called Natural Language Processing (NLP).
Therefore, what most consider sentiment analysis is, in fact, NLP.
What is ‘sentiment’?
To put it simply, it’s the identification of a general feeling towards a particular product, brand, entity, or message. In other words, it’s what the public thinks — it’s the unified voice that more accurately represents the voices of thousands, if not millions of individuals within a single statement.
Let’s assume you have a room filled with 100 people who are given pudding and asked to state how they feel about it. Of those 100, 95 would say they love the pudding, while five say they didn’t care much for it. Therefore, we can deduce that 95 percent of the pudding-tasters have had a pleasant experience with the product of the pudding manufacturing company; therefore, sentiment is positive.
Therefore, sentiment on a rudimentary level can be broken down into three categories: positive, neutral, and negative.
All that data won’t analyze itself
When we talk about 100 individuals within a controlled environment with limited responses (like or dislike), it can be straightforward to identify, to an extreme degree of certainty, the group’s sentiment.
However, realistically, nowhere in the world, or the internet, will you ever see such balanced environments.
Sentiment analysis is the methodic use of advanced algorithms to identify sentiment towards a particular object or topic over the course of hundreds of thousands of data points.
However, unlike humans, machines aren’t quite as capable of understanding language (fundamentally) as people do. This is where NLP comes in: the process develops ‘vectors’ for different words to identify their true meaning. This is done by converting words into a more understandable language for machines — numeric values.
While NLP has several methodologies to it, word vectorization is one of the fundamental ways through which machines are taught to understand the human language. By taking individual words or phrases and mapping them to other words or phrases from vocabularies, the process can map out the most likely outcome of the initial word or phrase to a degree; that degree is a vector.
There is no doubt that you have, at some point, experienced NLP in action; just take the act of Googling something as an example. When entering something into the search bar, Google’s algorithm attempts to predict what you will type next, most often to the degree of spitting out entire phrases that are, for the most part, accurate and relevant to what you were searching for.
This is done precisely through the process of NLP through word vectorization. For instance, when looking up ‘Places to’, the algorithm will track all of the potential outcomes for this phrase across the internet (and its own platform), evaluate the searches within the last determined timeframe to gauge popularity, and then rank recommendations based on these criteria. Therefore, the outcome of the recommendation bar would most likely read ‘Places to eat, places to eat around me, places to hang out” or others.
As a result of the internet essentially giving access to the hivemind of the general populace, it has become significantly easier to teach machines the nature of the human language. Once an algorithm can understand human language, sentiment analysis can be applied by having the computer determine the degree to which a sentence or phrase is positive.
The algorithm then stores said data and make it easily accessible for its users, more often than not accessible through visualization tools that organize all data into easily accessible charts, graphs, and statistics.
Reaping the benefits
There are hundreds, if not thousands, of applications of this technology that have already been deployed or are currently under development that is certain to give a significant edge to enterprises and micro-businesses alike.
Social media has revolutionized the world through its ability to give users a voice that they can use whenever, wherever, and however to communicate with their preferred counterparty. Whether it’s to send a message to their friend or make a public posting on a page of a company complaining about their product, all of it can be done on a whim with minimal resistance — therefore creating an easily accessible engagement platform that companies may now utilize to their benefit.
Most, if not all, large enterprises have sentiment analysis tools being applied across all of their modes of communication, with social media and traditional digital media (such as emails) being analyzed, segmented, cleaned, and compartmentalized for easy access by leadership.
Whereas without this technology, one would have to scour each post for different comments and predict what the general feelings about a company or its product are, companies are now able to, with a click of a button, identify not only general sentiment around their brand — but in fact target specific keywords to determine their sentiment as well.
An example of this would be Samsung releasing a new phone and posting about it across all of their social media platforms. The phone, named “X”, has had mixed reception worldwide and has attracted many comments across all forms of media for the company.
Samsung determines that an in-depth look into the problems with “X” must be performed, and sets up their sentiment analysis tool to track all responses that mention X, comments that are related to posts whose subject is “X”, and tracks any alternate digital media (such as unofficial posting boards) in order to gain a better understanding into why this phone has not hit the targets that they anticipated.
Samsung then can segment all sentiment by region to identify their customers’ pain points easier.
And voila — it turns out that the phone happens to have significantly more negative sentiment in the
MENA region, where the phone is prone to overheating as well as often being sent back to Samsung for repairs for water damage — most definitely linked to the higher levels of humidity within the region counter to that of, let’s say, Europe.
Samsung identified this by seeing that sentiment analysis was negative within a specific region of the world, seeing that negative sentiment frequently included the word “overheating” and “water damage”.
The best part — this probably took them less than a day to identify as sentiment analysis tools are extraordinarily powerful.
Where are we headed next?
Sentiment analysis, as mentioned, is a relatively old concept by today’s standards. Most companies are already using the tech, and those that aren’t are getting left behind on the curb.
With the
Internet of Things (IoT) we are bound to experience further enhancements and involvement of the technology in our daily lives. Whereas currently we require individuals to go through sentiment analysis, it is likely that in the future, algorithms be able to be customized to the point where human input wouldn’t be necessary — wherein the algorithm would simply build “paths” to a solution for its user and simply have them select the most preferred option.
Sentiment analysis remains to be one of the most important technologies of our time, and through its use individuals and companies alike are able to extrapolate the public’s emotions into more digestible data giving them the opportunity to address problems en-masse. If you haven’t yet experienced this technology, do yourself (and your business) a favor and start experimenting — you will most definitely be surprised to discover just how useful and powerful, this technology can be.
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