Data is the key. Data is the strategy maker. Data is the future. And lastly, Data is gold, not because of its relative rarity like Gold, but because its future enabling ability & resourcefulness is making it that much more significant.
Look, Deloitte is already propagating a theory that suggests—By 2030, data collection and analysis will become the basis of all future offerings and business models.
Here, in this write-up, the focus will revolve around a dimensional use of data known as “Sentimental Analysis. Elaborately & succinctly, it is defined as the automated process of identifying the emotional tone behind a series of words or phrases. It is used to understand the context in the form of—attitudes, opinions & emotions within an online mention.
In a world where we generate 2.5 quintillions of bytes of data every day. Without a doubt, Sentimental Analysis is a quintessential tool for making sense of that data.
Putting in terms of data science, Sentimental Analysis has already become a hot favorite topic in the area of Natural Language Processing (NLP) and Machine Learning. If we put Sentimental Analysis as a system for text analysis, it combines natural language processing & machine learning techniques to assign weighted sentiment scores to the entities, topics, themes, and categories within a sentence or phrase.
Putting aside what we have explained. Can you really browse the Web, finding relevant texts by reading and analyzing the tone they carry Manually? Well, of course not. If yes, then this write-up is really on an auto-reader mode by other machines using a Sentimental analysis.
The particular analysis helps data analysts within large enterprises gauge public opinion, identify market research, brand monitoring, product reputation, and understand customer experiences. In regular operations, some data analytics companies use to integrate third-party sentiment analysis APIs into customer experience management, social media monitoring, or workforce analytics platform to deliver a more personalized & interactive user experience.
Now, it is time to come down to different types of Sentiment Analysis
Sentiments could be of different types, so it’s Analysis. In this section, you will find a glimpse of the different types that Sentiments—
Fine-Grained Sentiment Analysis
Sometimes the level of the polarity of the opinion is so precise that instead of making about positive, negative, or neutral opinion, the user considers the categories like—Very Positive, Very Negative, Very Recommended or Not Recommended. This is referred to as fine-grained sentiment analysis, and can be mapped into a 5-star rating in a review- Very Positive- 5 Stars & Very Negative.
Some systems also provide different flavors of polarity by identifying if the positive or negative sentiment is associated with a particular feeling, such as anger, sadness, love, or enthusiasm.
This detection particularly aims to identify emotions like- Happiness, frustration, anger, sadness, and many alike in the category. Many emotion detection systems resort to lexicons (i.e lists of words and the emotions they convey) or complex machine learning algorithms.
Unfortunately, there is one drawback of this particular type. People express their emotions in different ways and so do the lexicon items they use. Let’s understand with an explicit example. Shit & kill used to express both anger & happiness. This product is shit or this product is killing me can also express happiness like Your personality is killing me or what an epic shit.
Aspect-Based Sentimental Analysis
It focuses on analyzing the sentiment in subjects. When you are interested in not only restricted to taking people’s positive, neutral or negative polarity about the product but also which particular aspect of the product they are talking about. An explicit example of this analysis is- the battery of this phone is too short. This sentence is particularly expressing a negative opinion about the camera, but not about the whole phone.
This analysis detects what is the actual intent people want to express with text rather than what they say with that text. Let’s understand with the example-
Your customer support is a disaster. I have been told to hold for 15 minutes.
I would like to know how to replace the cartridge.
However, the human brain is made to detect the complaint in the first text, the question in the second text, and the request in the third text. But machines don’t work like that, so it contextual knowledge to understand this.
Multilingual Sentiment Analysis
This analysis is something class apart & difficult as well. It requires a lot of preprocessing and the use of multiple resources. The use of sentiment lexicons, and noise detection algorithms, takes a lot of coding and time to implement.
Why Sentiment Analysis is important?
Now, putting forward the real world of sentimental analysis through its significance and why it is necessary. Primarily, it allows companies to automate their business processes, get actionable insights, and save the time required for manual data processing, basically making them more efficient.
Let’s talk more specifically and dig deeper into its applications
Customers love being heard.
Every time a customer mentions your brand name, you should be listening. Because each time they mention you, it gives your company the chance to get a glimpse of their sentiment towards your brand and your products.
You can do this by watching what customers say about you after you launch a new product or even a certain marketing campaign.
Improving Your Customer Support
More than 25% of customers drop a product or brand after just one bad customer experience. With the rise of social media, consumer forums, and opinions, one bad experience can cost your business.
So if you are able to track sentiment across the web, your customer service team can be ready for queries coming their way. So in case, if your product has been widely criticized, your customer support team can use opinions to tackle it effectively in a timely manner.
This can help them to be more personable with your customers, drive sales, and boost your bottom line.
Providing Better Product Analytics
Sentimental analysis is a significant tool for product analytics and could tell what is working for you or what is not. This helps in the segmentation of features of your product through analysis and helps in creating specific marketing campaigns to target specific audiences showing interest in some specific feature.
The most amazing thing with product analysis is that when customers give feedback, they really interested to give it. God knows, they can mention some additions in the product you hadn’t thought of including.
Monitoring Market Research
Monitoring marketing is a must include a tool for companies launching their very first product in the market, or are trying or get into a new market. It helps you to track down your target market and gives you a clear idea of how your target market is receiving your product in the first weeks or days.
So it can save a lot of time & money as you can change any feature of the product or even the target market before the product establishment.
Keeping an Eye on Your Competition
The direct benefit of sentiment analysis here is that you can analyze customers’ opinions of your brand compared to your competitors. It empowers you to keep an eye on your brand as well as your direct competitors, and you can measure your marketing campaigns against your competitors.
Uncovering Brand Influencers
You just run from the fact that social media plays a big part in every product’s success or failure. And the real thing starts when a social media influencer steps in. The opinion of influencers holds equal value as a friend’s opinion when it comes to making a buying decision.
Nearly 40% of people have said in a survey that they bought something just because an influencer had posted about it on some social media platform.
So, it is certainly acting as a goldmine if you track influencers talking about your product and use that to engage your audiences.
Managing a Crisis in a Timely Manner
They said it is never too late. But it can’t be justified as per today’s market dynamics. Because nothing is worse than discovering a problem with your product or services in later stages.
The sentimental analysis comes here for your rescue. It empowers you to track opinions in real-time so that you can evaluate sudden negativity towards your brand. So if you find anything like that, you just have to take immediate PR reforms and host it down before it explodes.
It can save your brand from immediate disaster and help you build efficient & time-to-market crisis management techniques along the way.
A Thoughtful Summary
There is no denying that organizations from different domains are harnessing sentimental analysis for their productivity. But it is not a once-and-done effort. It is an ongoing process and businesses have to review customers’ feedback to be more proactive regarding the changing dynamics in the marketplace.
Now, this phenomenon has moved further from being an innovation. Now it has become an indispensable tool for organizations competing in this digital age. Don’t panic if you think it is required you to change your existing systems and analytics. It is an add-on thing to make you more proactive in brand monitoring and product analysis. It can be integrated with the existing data infrastructure without much problem.