Sentiment analysis: What Is It, how does it work and why is it important? - Permutable.ai

Sentiment analysis: What Is It, how does it work and why is it important?

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SaffronBlue.aiSentiment analysis: What Is It, how does it work and why is it important?

Sentiment analysis is the practice of analyzing text to determine whether it contains positive, negative, or neutral sentiments. In simpler terms, sentiment analysis is the process of identifying and analyzing people’s emotions in written communication. The goal of this practice is to understand what people are saying about a particular topic and how they feel about it. The advent of digital communication has led to an exponential rise in the amount of written content we produce on a daily basis. This has made sentiment analysis even more important than ever before because it allows businesses to improve their customer service and identify areas for improvement with their brand marketing. Keep reading to learn more about this practice and its importance for your business.

What Is Sentiment Analysis?

As mentioned above, sentiment analysis is the practice of analyzing text to determine whether it contains positive, negative, or neutral sentiments. In other words, it is the process of identifying and analyzing people’s emotions in written communication to help businesses improve customer service and identify areas for improvement with brand marketing. Sentiment analysis is most commonly used to forecast customer behaviour and identify the best times to market products and services to customers. It’s also used to see how customers feel about your business, competitors, and products in the market and is a valuable tool in identifying any reputational risk factors helping companies to sidestep any potential PR crisis.

How Does Sentiment Analysis Work?

Text is like a language that’s been spoken for thousands of years, so analyzing it and finding patterns is easier than you might think – especially when you train and AI to do it for you.  The first step in sentiment analysis is to identify the different types of sentiments in a text. There are four main types of sentiments visualized below: 

Positive Sentiments

Positive sentiments are feelings of admiration, happiness, and positivity. Positive sentiments are great to identify in your marketing materials and create a positive brand image among customers. – 

Negative Sentiments

Negative sentiments are feelings of anger, sadness, and frustration. Negative sentiments are important to identify in your marketing materials so you can avoid offending customers. In fact, it’s even better if you can avoid producing content that generates negative sentiments altogether. 

Neutral Sentiments

Neutral sentiments are feelings of indifference. Neutral sentiments are useful to identify in your marketing materials as they’re often associated with apathy and disinterest among customers.

Confusion Sentiments

Confusion sentiments are questions and uncertainties that don’t have a clear answer or don’t know the answer. Confusion sentiments are important to identify in your marketing materials as they can often lead customers to ask questions and feel uncertainty about your brand and products.

Benefits of Sentiment Analysis in PR, Marketing and Reputational Risk

Below are some of the benefits of sentiment analysis in the areas of PR, marketing and reputational risk:

Increased Customer Satisfaction

Customer satisfaction plays a key role in boosting your brand loyalty. And the best time to delight your loyal customers is before they decide to switch to another brand. There are numerous studies that proven the value of customer satisfcation. One example is that  76% of customers are more likely to stay loyal if they’re satisfied with their current brand experiences.

Predicting Behaviour

Sentiment analysis is a powerful tool to predict customer behaviour across any segment. For example, you can use it to see how customers feel about your business, competitors, and products. It can also help you know which way the market is going to move before it is close to doing so.  This can help you improve your strategic decisions, brand marketing and help you stay one step ahead of the game in every sense. 

Conversion Rates

Timing and targeting are everything. With sentiment analysis, you can identify the best times to market products and services to your customers to increase their brand loyalty and satisfaction. 

Improved Brand Reputation

Sentiment analysis is a great way to build brand reputation by identifying areas for improvement. When consumers see that your brand is making effort to resolve issues and address customer concerns, it shows customers that you truly care about what you do.

Reduced Advertising Costs

Market research is an important part of content strategy. What you discover during market research can help you identify the best times to market products and services to your customers. 

How to do sentiment analysis in business

If you want to start using sentiment analysis in your business, you’ll need to identify the different types of sentiments in your company’s content. This will allow you to identify positive and negative sentiments in your communication and improve your brand image. 

Next, you’ll want to start documenting how your customers feel about your brand image. This will allow you to identify areas for improvement in your business. You can do this by analyzing customer feedback, monitoring social media comments, and collecting feedback forms. However the most potent way is to use an AI capability such as that offered by Permutable’s sentiment analysis tool to monitor sentiment in real time in a fraction of the time it would take compared to traditional man hours. 

 

apple sentiment analysis
apple sentiment analysis

Finally, you’ll want to implement strategies to reduce confusion and increase customer satisfaction. This will allow you to increase your customer loyalty and delight your loyal customers before they decide to switch to another brand.

How to get started with sentiment analysis in marketing

There are many tools and applications out there that claim to do sentiment analysis. Unfortunately, many of them are either inaccurate or can only identify a few types of sentiments. The best way to get started with sentiment analysis is to use an AI tool like like Permutable’s sentiment analysis technology to provide real-time data on your company, brand and even your whole supply chain

From there, you can work with your PR and marketing strategists, departments or agencies to help you build a strategy around it. They will work with you to identify the different types of sentiments, how to respond to them, and how to use different types of content or PR campaigns for maximum effect. 

How sentiment analysis is used in reputation risk management

Sentiment analysis is important for reputation risk management because it allows companies to monitor and analyze public opinion about their brand, products, suppliers and services, in real-time. This can help companies identify and address potential reputation risks before they become major issues. For example, sentiment analysis can be used to detect negative comments or complaints on social media platforms, online forums, and review sites, and then alert the company to respond promptly. This can prevent negative sentiment from spreading and potentially damaging the company’s reputation. 

Additionally, sentiment analysis can help companies understand the sentiment of their key stakeholders such as customers, shareholders, and employees and take actions to improve it. Overall, sentiment analysis provides companies with valuable insight into how they are perceived by the public, which can help them proactively manage their reputation and mitigate risk.

sentiment analysis suppliers

How AI is used in sentiment analysis?

AI is used in sentiment analysis to automatically identify and extract subjective information from text data. This typically involves training a machine learning model on a dataset of labeled text, such as brand or company perception labeled as positive or negative. 

Once trained, the model can then be used to predict the sentiment of new, unseen text. Common techniques used in sentiment analysis include natural language processing (NLP) and machine learning methods such as supervised learning, unsupervised learning, and deep learning. These techniques allow the model to learn patterns and features in the text that are indicative of a particular sentiment, such as the use of certain words or phrases. For examples of use cases see here.

Summing up

Sentiment analysis is important for companies because it allows them to gain insight into how their customers feel about their products or services. This information can be used to improve customer satisfaction, identify areas for improvement, and inform marketing and advertising strategies. Additionally, by tracking sentiment over time, companies can monitor the success of their efforts to address customer complaints or improve their offerings. Sentiment analysis can also be used to monitor social media and online reviews, giving companies a better understanding of their online reputation and how to manage it.

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