Within the past few years, the evolution of technology has completely transformed the lifestyle of humans in various ways. It has revamped the way we connect to one another, the way we shop, the way we travel, and whatnot. It has also changed the way we address our concerns online about anything. You can find thousands of reviews online about anything, a product or service. These reviews and feedback help us in buying better. Since technological advancement never stops, now you can analyze these comments and reviews and gather data to improve your marketing strategies or campaigns to deliver a majestic experience to the users.
Hearing the voice of customers about your business is mandatory to maintain a constant flow of profit. Now, with the tremendous advancement in technology, you can easily perform sentiment analysis with the help of AI. When you engage in analyzing the comments, reviews, feedback, etc., it helps you get a pulse on how the customers are feeling, positively or negatively. However, it is not limited only to analyze the customer’s reviews and feedbacks; if implemented correctly, it can automatically identify the emotional tone in various reviews and gather real-time insights from huge sets of customer data. Further, the data collected is used to improvise the product or the services of a business. Various big companies are already implementing sentiment analysis and turning those comments/reviews into actionable business insights. Let’s understand everything about AI-driven sentiment analysis.
What is Sentiment Analysis?
Sentiment analysis can be described as a type of data mining that identifies and extracts information and help businesses to understand the collective social sentiment of the brand. Further, it helps the businesses to monitor online conversations about them and simultaneously enhances the quality of their products and services as per the collective opinion of the users. In other words, it can be explained as the form of data mining to analyze the inclination of the customer’s opinions with the assistance of natural language processing (NLP), computational linguistics, and text analysis. The mining of the data mainly takes place at various social media platforms and other similar sources.
The collected data is further analyzed to understand the collective opinion of the users about a brand or a particular product or service. Opinions matter online where everyone can read or hear them; these opinions define a brand’s image. Alternatively, it can also be defined as opinion mining. Social media platforms are full of opinions about one’s services and products. These sentiments are the portals to your consumer’s mind. It describes what they think of your product. With the correct implementation of sentiment analysis, you can generate a significant user base. But the field of social media is vast; it is impossible to manually analyze all opinions/reviews and extract insights from them. That’s where AI and deep learning comes into play; with extensive algorithms that can analyze text, you can enhance the quality of insights from these reviews for doing in-depth research.
Why is Sentiment Analysis Important?
Sentiment analysis is a crucial part of an effective business strategy. Just like people are curious to know about the comments related to them, businesses also want to know about their feedback or opinions, whether it is neutral, negative, or positive. The reason for analyzing these data sets is simple, the more you can get into the mind of customers and understand their process of thinking, the more you can customize the experience for them.
It helps businesses to enhance the customer’s experience ultimately, enhancing their user base and revenue figures. Its importance lies in the details, as it is almost impossible to sort and analyze tons of unstructured data and reviews. However, with this tool, you can easily structure the data sets according to various parameters. You can also automate the analysis of customers’ data and turn them into various actionable data insights and save hours of manual data processing. Apart from time-saving and generating insights, it has a lot of benefits.
Insights for Improving Products/Services
When you shed light on various reviews, comments, surveys, etc., it fetches the opinion of users about the product’s flaws or highlights. Further, using these points, products can be improved as per the user’s expectations. It will not only enhance the user’s trust in the brand but also drive-up sales.
Result-oriented Strategies
You can generate insights for carving result-oriented marketing strategies. You can track the percentage of positive or negative reviews and improvise the product. Alternatively, you can keep track of the competitor’s products reviews, note their flaws, improve them in your product and promote those services or products.
Track Customer’s Sentiments
Keeping tabs on the nature of customer’s sentiments is a mandatory part of the business. You should know what your customers are thinking about you. In this constantly changing world, the view of customers can change drastically. It is recommended to check these opinions shift and implement required action.
How to Use Sentiment Analysis for Brand Building?
Creating a competitive brand image in times of digitization is extremely important. Sentiment analysis can help you in building the brand’s image effectively. Engaging with your consumers on various social media platforms helps to enhance the trust level between the users and the brand. Simultaneously, it also helps brands in understanding the user’s sentiments which can be used to enhance the brand value or image. Here are some ways you can implement it for brand building.
Observe the Competitors
Monitoring your customers can be extremely beneficial in the long run. Social media platforms deliver an analysis of the posts or hashtags directed at your brand. Later you can filter out the negative and the positive ones from the data sets. Similarly, look out for the competitor’s products reviews and the number of appreciations on their posts. Monitoring these sentiments will reveal the facts where you are lacking and where your competitors are getting an edge. Use these data insights to enhance the product or improve your social branding. Your brand’s social image should be positive, and analyzing competitors can get you a ton of useful insights.
Acknowledge the User’s Concerns
Social media is the place where you can find unbiased reviews about your brand. Use these feedbacks, posts, or reviews to your advantage. When customers don’t like any feature of a product or service of any business, they either tweet about it or post about the problem with a proper hashtag. Gather all these reviews targeted at your business and act accordingly. Remember, happy customers, are the foundation of a successful business.
Prevention is Better Than the Cure
With correct analysis of the sentiments, you can prevent any type of crisis from occurring. You can spot all the negative comments or reviews about your brand and neutralize them with proper response or service instantly. It is better to correct a mistake before it damages the reputation of the brand. Reply to the customers with negative feedbacks first and slowly build a positive series of comments that reflects the brand’s attentiveness towards its user base. When a customer with a negative perception about a brand gets his/her query sorted within a short amount of time, there is a huge chance of conversion of that negative feedback into positive.
Get Your Brand in Line
Always keep your eyes open to the post and comments of the potential prospects of your brand. Listen to their words and compare them with your marketing campaigns. Analyze the result of your campaigns, whether they have been successful in uplifting the brand’s image or not. However, you can use a brand tracker tool for this purpose that’ll deliver you a complete report on the health of the brand’s reputation. Enhance the number of metrics you are monitoring to get better results.
How to Implement Sentiment Analysis?
Sentiment analysis is based on various algorithms of natural language processing and machine learning; with AI, these algorithms perform extremely efficiently and sort the pieces of text as neutral, positive, or negative. To learn the correct implementation of the sentiment analysis, it is important to know the types of algorithms it uses.
Linear Regression
When an X number of features is given, and the value of Y is to be predicted, the linear regression algorithm is used. Machine learning helps in identifying the relationships between the data sets. In simple terms, it calculates the relationship between the words and phrases, represented by X, and the polarity or nature of them, represented by Y. Later, the results are converted into a range of extremely positive, extremely negative, and all the points in between.
Naive Bayes
It works on the essence of Bayes’s theorem and determines the probability that a specific comment should be called negative or positive. Naive Bayes is the simplest algorithm used in the analysis of sentiments. It does not completely follow the mathematical explanation of Bayes’s theorem, but compares texts against one another, and the machine learning models trained to determine polarity take care of the rest.
Support Vector Machines
It can be defined as an advanced version of the linear regression algorithms. However, it uses algorithms to train and classify text to determine the polarity. The best part about using SVM, the more complex data you will feed it, the more accurate results you will get.
Let’s understand how we can correctly implement the sentiment analysis to attain the most accurate results. While using it, you tend to have a look at the business operation from a user’s view with the help of generated data. Hence the first step, data collection, is mandatory.
Data Collection
While starting a business or a set of algorithms to extract insights, you need a lot of data. In the case of sentiment analysis, you have to sort the data as per some defined criteria to get the desired results. Start by sorting the data as different criteria such as languages, character limit, location, etc. Now that data has been sorted, proceed to analyze the data sets but remove all the unnecessary elements that are prone to slow the process, such as textual content, grammar mistakes, typos, errors, etc. Now the data is ready for ready to be tested; you can run the algorithms to determine the polarity of the text.
Deploy Efficient Tools and APIs
Thanks to technology, there is numerous customer experience software available in the market that collects data from a number of sources and notifies about the mentions of the brand in any post in real-time. They can easily analyze the text and visualize the results. Here are some of the most used customer experience software and APIs.
Clarabridge
It can be defined as the software used for managing the customer experience. It fetches the text data from various sources and compares them to get a wider picture of the sentiments of the users. Apart from sources like chat, blogs, forums, etc., it also gathers data from IVR calls, employee notes, and much more. It is one of the most viable options to perform an analysis of the sentiments, and businesses can customize it as per their needs.
InMoment
It is a combination of five products that make up the complete software. The most important part is the voice of the customer that drives the rich insights. Apart from the algorithms, it delivers real-time notifications, custom dashboards, and various reporting options to the users.
IBM Watson
It works on advanced text analytics systems. You can dig deep with this software to analyze metadata, keywords and achieve better results. Here you can define the industry and domains to get section-wise results. Apart from defining the domains, it can automatically classify the text as per the sentiments and point out the emotions of the users.
Microsoft Text Analytics API
Apart from the software deployed in analyzing the sentiments, APIs are not far behind in extracting excellent results. It can support more than 120 languages and can extract key phrases, entities, etc.
Google Cloud Natural Language API
This API by Google extracts the sentiments from various emails, social media platforms, blogs, forums, etc. With the advantage of the cloud, it can easily analyze all the scanned documents to understand the perspective of the users to create a wonderful experience.
Google Natural Language API
It is one of the most important API used in the analysis of sentiments as it assigns a sentiment score and magnitude for each sentence. With a score, it gets easy to sort the text as positive, negative, or neutral.
Find a Reliable Partner
The software can be trendy or effective, but nothing beats the human touch. There are some things that you don’t get with these tools and APIs. That’s where a data science company comes into the picture. A reliable company with diligent resources will deliver a range of hiring models that easily suffice the varying needs of the business.
The Bottomline
The online world is brimming with free data that contains user’s mindsets about one product, service, or brand. With sentiment analysis, you can leverage the availability of free data to generate result-oriented insights. Customers love the more interactive brand; at the same time, it enhances the trust between the customer and the business. With the correct algorithms and tools, you can easily achieve significant results. Various businesses have revamped their products, services, and customer interaction techniques with these rich insights.
Matellio can help your business with our flawlessly engineered AI-based software development. Our team of highly skilled engineers and experts knows every in and out of sentiment analysis and is ready to develop an efficient solution that fits like a glove with your business needs. Discuss more about the implementation and benefits of AI-driven sentiment analysis with us over a 30-min free consultation call. Fill up the form now!
