How to Start Your Sentiment Analysis Research; A Quick Answer to the Question:
The world is now shifting towards a data-centric ecosystem. With the digital ecosystem growing day by day, it makes it all the more important to ensure that businesses employ various digital strategies to remain up to pace.
A new tool to help in this digital era is sentiment analysis. Before you start wondering on the other aspects of your sentiment analysis research, it’s important to understand what it is.
What is Sentiment Analysis?
A crucial aspect of every brand is to keep a lookout for what consumers are saying about them. This is where sentiment analysis comes in. As the name suggests, sentiment analysis analyses any form of writing from various social networks (and the internet in general) to understand the emotional tone behind them. When it comes to the nature of these sentiments, they can be either of the three; positive, negative, or neutral.
Through sentiment analysis, a system picks up how your customers feel about you, the emotions associated with what they’ve said, and it also quantifies qualitative data for you.
Sentiment analysis isn’t just confined to understanding the online reviews of your customers. Since it understands the human emotion aspect behind a textual piece of content and senses its actual tone, it can be used to filter out spam emails from the real ones too! The possibilities of sentiment analysis are endless really!
How Does Sentiment Analysis Work? A Use Case:
Let’s suppose you’re the owner of an active-wear brand (just like Adidas or Nike). Your customer base would be in the billions probably. And such a large consumer base means large volumes of opinions from your customers.
Now it wouldn’t be very feasible to sit down and sift through millions of reviews to gather market feedback and understand customer satisfaction. It’s possible, yes. But it certainly isn’t feasible. Natural Language Processing (NLP) has revolutionized this process and made it a lot more streamlined.
There are various tools to carry out sentiment analysis and these tools can interpret large volumes of textual data within a matter of minutes! All it would take is a few clicks and you’ll have clearly segregated positive, negative, and neutral sentiments from your customers in form of a snapshot.
What Is a Sentiment Score?
In simple words, a sentiment score is assigned to every piece of textual information. Through deep learning, neural nets are trained to gain a thorough insight into human emotions and how they are put into words and even emojis.
A sentiment score is calculated through the amount of positive or negative words in pieces of text (online reviews, for example) contain and is usually defined within a range of 0 to 10.
Using the Right Approach:
Prior to executing your analysis, it’s very important to understand the approaches and execute the right one accordingly. There are primarily 2 approaches that can be taken up:
This works through setting up rules; which is done manually. Lists of words are created which are classified as positive or negative sentiments. These are also referred to as lexicons. For this, sentences are split into words so that the machine can understand them and from there, the machine counts the number of positive and negative words. A sentiment score is then calculated.
This approach, however, can miss out on complex metaphors (which are a part of natural human language). It also requires the system to be updated regularly to ensure its optimization.
b. Automated/Machine Learning:
Neural Network models are utilized in this approach. Word associations can be taught to these networks through converting texts into numbers. This is referred to as vectorization. Training sets are fed into the machine which enables it to learn these word associations.
Through these training sets, machine learning enables the system to “learn” and even predict labels for new data that the machine has not yet seen. The machine can also decipher other things like grammar rules and even learn to correct itself in the situation where it makes an error (also called deep learning) after being exposed to large volumes of text.
However, training the machine is a long and strenuous process when it comes to setting it up. The more a machine is trained, the more accurate results it’ll give. For the same reason, pre-trained models are also available and is useful if a business does not have data with them on hand.
It’s absolutely crucial to align your business objectives and your capabilities beforehand. With clarity on these two areas, you would have a better idea of which approach to go with. On the other hand, you can also opt for a hybrid approach that incorporates a mix of the rule-based and Automated/machine learning methods.
Some factors that can aid your sentiment analysis research:
- Will the rule-based approach suffice?
- Do you have the resources to train a machine and provide it with relevant data-sets?
- If you are opting for a pre-trained model, will it do the job?
- What are the current limitations of sentiment analysis and how do they apply to your case?
What is the Practical Application of Utilizing Sentiment Analysis?
Machine Learning has revolutionized business practices all over the world. Businesses are using ML to learn more about their customers and market opportunities, as well as making business decisions on the basis of predictions.
Before you move on to thinking around your sentiment analysis research, you would want to know what it can bring to the table. Sentiment analysis with it brings multiple advantages to a business:
1. Managing Brand Reputation:
This is by far the biggest advantage. By collecting information from the entire web, businesses can gain a better insight into public opinion about a product or service. Undoubtedly, sentiment analysis will become a widespread practice in PR management in the coming future.
2. Understanding Customer Feedback:
The most important component of brand management is to address customer feedback in a timely fashion. In order to do that, customer support teams have to keep an eye out for any review a customer might post online. Sentiment analysis can automate this procedure to quite an extent by prompting a brand as soon as any negative feedback has been posted about them. Support teams might not have to walk on eggshells in the future, waiting for something to go wrong.
3. Making Predictions:
Every marketing campaign is run with an objective in mind. What if machine learning allowed qualitative emotions to be interpreted in a quantitative, statistical manner? Well, that’s definitely possible. You can monitor public opinion on a campaign in real-time to ensure its effectiveness; and even predict the outcome.
Sensing the tone behind a customer’s review is crucial to any brand. And if this process can be automated, it definitely speeds up the entire process of understanding user sentiments. Whether it’s a product, campaign, or service, you would want to know if it’s something that the masses are taking in the way it’s supposed to be taken.
Sentiment Analysis may not be the ultimate answer at the moment as it’s a technology that is constantly evolving. But it certainly has its benefits. Without a doubt, businesses can use this tool to make their business processes a lot more efficient!