The world is in a period of great change, virtually and non. Today, each and everyone speaks their mind freely on social media. No matter the topic – political, ethical, sociological – from forums to comments and reviews, internet users express their opinions in their own human way. But how can we use those opinions and feelings to know our brand better? Sentiment analysis is the key to the question.
What is Sentiment Analysis?
Sentiment analysis is the use of Natural Language Processing (NLP) in order to analyze raw text and determine the public’s opinion on a specific topic, issue or brand. In that way we are able to determine whether our audience feels positive, negative or neutral towards our brand/product/post. The relevant data can be found in forums, comments and reviews in any Social Media.
How does Sentiment Analysis work?
In the same way, machines analyze and understand images, they can be built to analyze and understand the raw text and classify it according to a specific score. In order for us to understand better how this segmentation works, let’s assume that our categories are:
- Very Positive
- Very Negative
Let’s take a look at some examples:
😠 😐 😍
I loved that chocolate -> Very positive
I liked that chocolate -> Positive
That chocolate was ok -> Neutral
I didn’t like that chocolate -> Negative
I hated that chocolate -> Very Negative
To put the above into words, we give the model inputs (raw text) to produce, that we call, sentiment analysis. In that way, the system reads the text/sentence and understands whether it is positively or negatively adhering to the issue.
Of course, it’s really hard for the results to be 100% accurate. Considering that the raw text is much less simplistic than the image, we must take into account irony, colloquialism, complex sentences, and concepts. In these cases, we need to give much larger data to the model in order to be able to evaluate the text correctly.
Why is Sentiment Analysis important?
In combination with other analyzes, whether a user’s content is positive or negative, we may be able to have a first prediction of how a topic or product might be perceived to the target audience. So, it’s really important to build models to inspect how internet users may interact with a new product that will be launching soon or with a sociopolitical issue.
While using sentiment analysis, we can define the degree of users’ predispositions, if their perspective may change negatively or positively, and even why we will have this correspondence (e.g. taste, smell, use of product). This knowledge gives us the advantage of knowing the right message to send in the interest of changing the audience’s attitude as we wish.
Where to use Sentiment Analysis?
A very important aspect of the brand is how the targeted audience feels and thinks about it. By knowing that, you will be able to find ways to ameliorate this perspective. Brand monitoring is the combination of social listening and social monitoring. Using sentiment analysis, helps you read between the lines and understand your audience’s reaction to specific posts, news and products as well as their feeling about your business.
Keep in mind that how you communicate with the audience, whatever their opinion is, plays an important role in your business growth.
Interested in releasing a new product or service? Using sentiment analysis in our market research helps us to understand and predict how the market will react to it. Isn’t that awesome?
Whether you’re analyzing entire markets, niches, segments, products, their specific features, or assessing any market buzz, sentiment analysis provides you with tremendous amounts of invaluable information: what consumers like, dislike, or what their expectations are.
There’s another reason to look at sentiment based on keywords that matter to your industry. You’re not looking for what your brand does well – you should already know that. You need to know what others are doing well (or not), and whether that information should change your approach.
Even on stock trading, sentiment analysis can be very useful. Because stock may be very incomprehensible to the ones that are not familiar with them, let’s make the explanation simple:
- Negative content towards a brand -> Their stock will drop.
- Positive content towards a brand -> Their stock will rise.
Large companies are already analyzing the public so that they can predict the course of a particular stock, reducing the risk and making very, very careful moves. Of course, wherever we use sentiment analysis we can not have 100% validity.
All these useful techniques, and much more, are slowly coming to the offline world. How will that be? Think of a mini personal psychologist who will help us understand our feelings by informing us how we feel. Via mobile, this kind of personal psychotherapy will be based on machine learning, reading biological indicators, and our digital behavior.