Did you know that 90% of the world’s virtual data has been generated in only the last two years?
Not only has it helped make sense of Big Data, but it has also enabled data scientists to use sentiment analysis in python or equivalent language to understand human sentiments.
With so much data, we can’t help but use it to collect crazy insights, which is usually hidden in plain sight.
Machines make it easier for us to take a deeper look in the larger haystack!
This is called Machine Learning, and today we’ll take a closer look at one of its applications known as Sentiment Analysis.
What is Sentiment Analysis?
Sentiment analysis works by identifying and extracting meaningful data from the source.
This helps various businesses understand the social sentiment of customers towards their product, services or brand during online conversations.
It allows us to understand the following two metrics.
The key aspect of a brand’s service or product that consumers care about.
Underlying intentions and reactions of users concerning those aspects.
When used in different combinations, this tool can analyze millions of such conversations with human-level accuracy.
What are its Applications?
Here are two examples of Uber.
A user is sharing her not-so-good experience of one of her trips with Uber.
On the other hand, this user had a very good experience with Uber.
If you see, in both these cases, the machine algorithm will interpret these messages as contextually related to the concept of ‘price’, even though the word ‘price’ is not mentioned even once.
“Brands can definitely make use of sentiment analysis in multiple ways.”
Let's say your brand has around 2,000 followers, you surely wouldn’t want them to post negative comments.
And even if they do, you’ll want to know their experience of your brand.
So, sentiment analysis in Python provides you with the tools to gather statistical data on how your followers are interacting with your brand.
It is a great tool for bloggers, customer service, and support team as they can react efficiently while monitoring dissatisfaction before negative sentiments from potential consumers spread.
Why You Need to Gather Customer Insight through Sentiment Analysis?
Let’s say that your digital marketing team runs a campaign about your brand’s new line of hair wash solution, which uses parabens as a bacteria repellent agent.
After the product launches, your team perceives that a majority of comments are negative due to undesirable effects of paraben.
Upon gathering these customer insights, the brand can adjust their messages accordingly.
They can do damage control by pulling the product from the market, making public donations to environmental agencies, and more.
But why do you need to learn from your mistakes, when you can learn first-hand and correct your mistakes right away using sentiment analysis.
An easier way is to do the market research before and get all your data before offering new features, services, and/or products.
Is Sentiment Analysis Really Required?
Remember when computers were introduced and Steve Jobs couldn’t wait to put the power of modern computing in every person's hand?
Similarly, in every decade there may be some new technology that will come along.
And technology wins if it stands true to the fact that it will work for the good of mankind.
Hence, for the moment, machine learning has won the argument and its applications such as sentiment analysis are helping us more than we could have ever imagined.
What are your thoughts about this? Leave them in the comments below.
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Stay tuned for more on Machine Learning and its applications in our next few blogs.