The popularity of social media being used to interact with consumers has given businesses a lot of insight. However, the insights are always in the form of likes, shares, and comments.
We can always decipher the meaning of a person liking our post and sharing it. This, undoubtedly, means the feedback is positive.
It is the comments that require a lot of time to read to understand whether the person has a positive or negative feeling towards our ad or product.
Comments are textual reactions, just like online reviews and feedback. It is easier to understand the mood of a person when they are speaking to us through voice calls or face-to-face.
In texts, it is a bit complex to determine whether a remark is positive, negative, or neutral.
While texts written without a hidden meaning can easily determine the mood of the person, it is the texts that are written with a sarcastic mood or irony in mind that only experts can decipher.
Hence, businesses require a tool that can measure the negative and positive feedback from social media comments and online reviews.
There are multiple meanings to the things that people say through texts and written communication.
Since there is a huge amount of insight data that is to be processed, there has to be a way to categorize all of it as neutral, positive, or negative. Hence, the need for sentimental text analysis arises.
Sentiment analysis, by definition, is a program that analyzes the mood or emotion that has been expressed in any text. It is a way in which business analysts can take huge amounts of text reviews, produced in the form of comments or reviews, and provide a verdict about whether the ad, product, or marketing campaign is in the positive or negative views of the audience. Let’s understand what it is.
What is sentiment analysis?
Sentiment analysis reads textual data from reviews, comments on social media, and survey forms. It then analyzes the text to determine whether the user has reacted positively, negatively, or neutrally in a certain instance.
Sentiment analysis is an AI or machine learning-based program that can be used by businesses for social media monitoring, reputation management, customer experience, etc.
For example, if a business analyzes thousands of text reviews of their product being sold on a certain platform, it gives them a complete idea of the changes they can make in pricing and features if needed.
Sentiment analysis is a form of text analytics that is developed with the use of natural language processing (NLP) and machine learning. Sentiment text analysis is also known by the industry as “opinion mining” or “emotional artificial intelligence”.
There are two types of sentiment analysis most commonly used. One of which gives output as a score-based rating on any long-form or short-form text. The other can provide opinion-based ratings on specific items or concerns. Here’s how.
Sentiment scoring
An essential thing that sentiment analysis does in its first steps is, classify the polarity of the given text.
In sentiment analysis, the meaning of polarity is the kind of mood that the given text is showing. The text’s polarity is thus classified into different categories: very negative, negative, neutral, positive, and very positive.
The polarity score determines the classification, with a scoring unit that goes from -100 on the negative side to 100 on the plus side. In that context, a zero will mean that the text is exhibiting a neutral tone.
The scoring of sentiment text analysis can be acquired on either a large text or on a single phrase in the text.
Thus, it converts the text reviews and comments into the popular star rating system of the internet, where 1 star means very negative, 2 stars are neutral, 3–4 stars are positive, and 5-star ratings mean very positive.
With the use of machine learning, this scoring system can also be refined into sentiments such as excited, happy, impressed, disappointed, trusting, and more.
Aspect Based Sentiment Analysis (ABSA)
The importance of sentiment analysis grows when it is used to determine the emotions of an audience based on a specific aspect.
Every product and marketing campaign has certain attributes that businesses are required to work on or upgrade over time. These attributes are generally the things that we would expect feedback on from experienced buyers.
The utilization of such attributes and basing them on certain sentiments is what creates an aspect-based sentiment analysis.
For example, for a car manufacturer, an important aspect would be the braking system of the car.
An algorithm based on this exact aspect can be used to determine the positive or negative emotions of the public whenever the braking system of a car is mentioned in online texts.
This property makes the ABSA model effective for monitoring online sentiments on specific attributes of a product.
The ABSA model is efficient enough to be used for real-time monitoring of online texts. This is done in the area of AI known as learning. Learning in AI teaches computers to perform calculative tasks by looking at data.
The computers are then enabled to discover patterns in the data by using machine learning.
In the ABSA model of analysis, the algorithms of machine learning are trained to analyze data with high degrees of accuracy through any new text. The possibility is then achieved of reading and scoring texts when they are used in a slightly different phrase.
In context with the earlier example, if the user comments “quick braking” or “slower halt”, the learning module still picks up that the text refers to braking and can score it as positive, negative, or neutral.
This machine learning module is used by companies on their social media channels, online communities, online retail channels, review sites, and internal customer review communications.
Sentiment learning can then be used to produce outputs in data visualization to explore factors where improvement is needed. The visual data can then be used to determine overall sentiment, sentiment over time, and sentiment by rating an individual set of data.
By using this technique for real-time monitoring, we can increase the response time to consumer concerns and identify major issues before they affect the business’s reputation.
What is the importance of sentiment analysis?
In any business, improving sales and retaining customers are important goals. This makes reputation monitoring even more essential.
According to a business review, every time a user gives us a four- or five-star review, it causes the revenue to increase by 5–9%. It was found that businesses with 5-star reviews had 18% higher revenue than businesses with three stars.
This sums up the fact that businesses that calculate their reviews efficiently can always make improvements to their customer experience, thus giving them better reviews and more revenue over time.
With sentiment analysis, we can understand the feelings of people toward our brand on a larger scale. This allows for improvements that can be game-changers.
Comments, text reviews, and survey texts are largely untapped areas that can provide important insights into our consumer experience.
However, the huge amounts of data make it almost impossible to process and measure manually. This is why sentiment analysis is being used as a SaaS to get deeper insights into the emotions of consumers through textual data.
This includes social media posts and comments, online reviews, and surveys. They can help make better and more informed decisions in business. Let’s learn about the most important sentiment analysis benefits to businesses.
Benefits of sentiment analysis
Sentiment analysis provides a plethora of benefits to businesses. The most prominent ones are discussed below.
More trustworthy
Sentiment analysis is more trustworthy, meaning it removes the biased decisions of the human mind.
Human sentiment, whether in text or speech, is highly subjective. Humans use tone, context, and language to add meaning to their conversations. The understanding of that meaning comes to another human based on the experience of conversation and subconscious biases.
Taking that into consideration, understanding the meaning behind millions of text reviews and comments becomes a tricky task to accomplish manually.
The best examples of sentiment analysis being unbiased can be seen in texts with opposing opinions in the same sentence.
For example, a sentence for a product that says “does the job efficiently, but it’s not cheap”.
Now, any human analyzing this text will say the text is overall positive if they are biased towards the functionality and not the price. Whereas a person biased towards providing budgeted products will say the sentence is negative.
The text has both negative and positive sentiments, but human bias and error will either categorize it as positive or negative.
With a sentiment analysis tool, the judgment is unbiased, and both aspects of the text are measured. The nature of such a text will be defined as neutral by a sentiment analysis program while keeping the details of the conversation intact.
Powerful processing
Sentiment analysis can work on large amounts of data that are difficult to read and measure manually. When we are working with text, even a small amount of data, like 50 comments, starts to feel like big data within a few minutes.
It is thus important to use machine learning to make sense of huge quantities of unstructured data. Especially with social media, sentiment analysis is important to categorize and measure the sentiment of large amounts of text.
Take an example of a company that recently announced a new product launch on the market. The company will, of course, have to go through a lot of comments on its social media ads and campaigns.
With that amount of data, quick decisions before the launch are impossible to make if the data is processed manually.
Rather, the company can feed all the comments on the sentiment analysis program and get collective results as to how many of the audience members have considered it positive and how many are seeing the negative sides of the product.
If a significant number of the audience has found a similar aspect negative, there is a higher chance of reversing the features before the launch. If the feedback is highly positive, the launch date can be announced as soon as possible, and the upgrades can be made in later stages.
Save time with automation
Automation is the biggest need for a business that deals with online consumers or critics.
A development team needs more time to look at the feedback and then go about improving their product. They also cannot make decisions on changes to the product without having a clear view of consumer reactions from the consumer management teams.
Thus, sentiment analysis is important to make sure that all chances of improving the customer experience can be taken well in advance.
This makes the customers feel that the company is actually listening to their concerns and feedback. This, in turn, polishes the reputation of the company.
It is, however, difficult to make a faster decision if we wait for manual measurement of the sentiments of the public. There are hundreds of megabytes of data to be processed, which can actually take months. In that amount of time, it is easy for a company to lose both its reputation and revenue.
Sentiment analysis programs can do the job in a matter of hours, allowing developers to focus on making important changes to product features and creating new products.
What is sentiment analysis used for?
The business applications for sentiment analysis programs are multifold and can be used in many areas of customer experience evolution. Here are some of the most common uses:
Voice of Customer programs
Voice of Customer, or VoC, programs are made to listen to consumer concerns and feedback through multiple channels. One of the ways to run such a program is to create customer communities and send out feedback forms right after purchase.
Another more efficient way is to listen to them on social media and review platforms. This holds even for a small company that can create large amounts of data.
Here, sentiment analysis can be used to compare whether the metrics are negative or positive. If so, what is driving it in either direction and how can we rectify it if the reactions are highly negative?
Customer service
Customer services today use a lot of feedback forms and chatbots to record and manage the concerns of their customers on online platforms. The app store allows reviews, and anyone can provide feedback and complain about any feature.
Sentiment analysis can track the exact aspect on which negativity is rising and allow the company to resolve the error faster before it makes customers turn to competitors.
Social media sentiment analysis
Social media is a powerful platform today and holds the power to make or break businesses. Taking care of consumers that interact with our company through social media is crucial.
With the use of sentiment analysis of social media posts mentioning the company and comments on the company’s campaigns, it is possible to automate social media analysis.
Wrapping Up
All kinds of businesses that have an online presence can improve their services and reputation among consumers with the use of sentiment analysis. There are a number of sentiment analysis tools out there that are free.
However, they tend to limit the application of this tool and the complexity it can handle. We suggest you use a paid sentiment text analysis tool to make sure that you can utilize sentiment analysis to its full potential.
Konnect Insights offers a highly advanced sentiment analysis feature in its omnichannel customer experience management suite that can analyze large datasets.
Our platform is designed to be intuitive and can provide unparalleled insights into the customer experience. It’s the perfect tool for businesses looking to grow their brand reputation, get ahead of negative sentiment, build relationships with customers, and measure customer satisfaction throughout the customer journey. Try it out today!
