Sentiment Analysis: What is it and what is it for?

by Jose Luis Perez August 17, 2021
Sentiment Analysis: What is it and what is it for?

What is Sentiment Analysis?

Sentiment Analysis is one of the fields of natural language processing, dedicated to exploring subjective opinions or feelings collected from various sources on a particular topic.

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In stricter business terms, it can be summarized as:

Sentiment Analysis is the application of a set of tools to identify and extract opinions and feelings to use them for the benefit of the commercial operation.

Such algorithms delve into the content and find what the sentiment indicates towards the product in general or its specific element.

Sentiment Analysis is an opportunity to explore the mindset of audience members and study the perception of the product from the user’s point of view. This makes Sentiment Analysis a great tool for:

  • Deep product analysis.
  • Market research.
  • Reputation management.
  • Customer service.
  • and many others.

How does Sentiment Analysis work?

Sentiment Analysis is a process that uses Artificial Intelligence (AI) and Machine Learning (Machine Learning) aimed at finding a point of view, a keyword or a feeling, and highlighting the information of interest in the process.

The feeling can be defined as the result of “an opinion or judgment formed about something, not necessarily based on facts or knowledge.” But with the use of Sentiment Analysis and for Data Science, opinion or judgment is understood as:

  • It is a subjective evaluation of something based on personal empirical experience. It is made up of objective facts and partly governed by emotions.
  • An opinion can be interpreted as a dimension in the data regarding a particular topic. It is a set of signifiers that in combination present a point of view, that is, a feeling on a particular subject.

With Sentiment Analysis, content in different formats, such as audio or text, can be analyzed in order to interpret the keywords and other aspects of the behavior of the people associated with the content through rules to define the feelings they had at a given moment. on a specific topic.

How Sentiment Analysis software can improve contact centre customer service levels

1. Improved call quality

Your Sentiment Analysis tool can figure out what words and speech patterns cause favourable outcomes with the customer. It builds the call agent to adjust their speaking style to suit the attitude of the customer.

2. Analyse calls

With conversational analytics software,100% of your calls are analysed, leaving no stone unturned. Your experts get to see the negative and positive remarks that make agent performance judgment almost 100% more accurate.

3. Collect actionable data

You can collect actionable data through call analysis and use the result to discover common causes and trends. You could be focusing on the negative outcomes that point to a product or service flaw. Look at the common complaints you can use to improve your product offer and see whether your script contains elements that can change the customer’s sentiment.

4. Point out positive and negative agent attitude

Voice analytics lets you identify speech patterns from call agents. You can tell when your agent performs excellently and agents that need training for better customer service level.

5. Receive feedback

When you integrate conversational analytics, you can ensure instant results. For example, call agents will receive feedback that helps them to improve. They get to know whether a customer will return and work on their abilities to regenerate the interest of customers in subsequent calls.

Popular sentiment analysis use cases

Brand monitoring

Social networks and the popularization of smartphones have opened a new dimension in terms of sharing and consuming data. Consumers write whatever they want online, and their voices are heard globally. That’s why monitoring customer sentiment matters more than ever. Positive customer reviews build trust and boost conversion. Negative ones may ruin your reputation for months.

More importantly, data shows that 95% of customers read online reviews before making a purchase. Consequently, you must do your best to get as many positive opinions as possible. Of course, that’s not always possible. There’s always someone who won’t like your products or service, and they’ll be more than happy to share that online. When they do, you should know about it.

Sentiment analysis tools like Sotrender or Brand24 can help you keep your ear to the ground and monitor customer sentiment around the clock. So, if anything harmful to your brand appears online, you’ll be informed about it. With this functionality, you can handle issues immediately by countering fake news and issuing apologies to angry customers. By getting ahead of it, you’ll prevent unfavorable content from going viral.

Customer service

Since sentiment analysis lets you monitor customers’ emotions in real-time and across channels, it’s a perfect tool to keep an eye on the health of your services. It’ll allow you to learn about the weak points in your customer support and help troubled customers quickly.

Businesses also apply sentiment analysis to discover what customers feel when they contact their customer support teams. This helps to find out which agents succeed in delivering efficient service and which need to improve their performance to boost customer satisfaction. These insights help to determine what makes customers satisfied and to better train new customer service representatives.

On top of that, sentiment analysis helps companies that deliver support using call center software or chatbots to prioritize customer issues. Virtual agents can transfer unhappy customers to human specialists so that agents can take care of customers’ problems before they escalate. The same applies to help desk systems. Sentiment analysis algorithms can detect concern and anger in emails and mark them as cases with the highest priority. This helps customer service agents, who manage a massive number of requests, decide which issues need immediate attention.

Marketing

Sentiment analysis provides marketing intel and allows you to measure the performance of your promotional campaigns and events. Using it, you can find out whether your marketing activities generated any reactions online and then check their sentiment. This helps to rethink your strategies, if needed, and better personalize your messages in the future.

A good example of using sentiment analysis in marketing is the case of Expedia Canada, a travel company, that launched its “escape winter” campaign in 2014. The campaign created quite a stir online. However, when the company took a closer look at the sentiments of the comments, it turned out that many of them were negative. The brand found out that people hated the violin music used in their commercial. Because they were monitoring social sentiment and not just the number of mentions or shares, the brand corrected their course quickly.

Soon after that commercial aired, the brand released a spin-off video that shows a character from the first commercial breaking the violin. They hit the jackpot with that approach. The company showed that they have a sense of humor and listen to their customers, and that helped to improve the social sentiment.

Sentiment analysis can help you to boost your online presence, too. If you pick up on positive comments quickly, you can immediately respond to them and fuel the discussion with customers who have just become your brand’s advocates. This also helps to build better relationships and lets you find good examples for case studies.

Product analytics

Customers’ opinions about products are influenced because of many different factors. They also change on the fly like feature improvements, price increases, and marketing campaigns. All of these factors stir all sorts of emotions up with users.

Product managers need to constantly monitor how users respond to products and the changes they undergo. Sentiment analysis is useful in this area, too.

Sentiment analysis tools such as HubSpot’s Service Hub let you transform lots of qualitative data into quantitative insights on products and features. This helps to find out more about how customers perceive your solutions, how they perform on the market, and what should be done to improve their usage and reception.

Market research

Sentiment analysis isn’t the most important tool for market research. However, it can consolidate data and help overcome the deficiencies of typical research methods, such as surveys or focus groups.

By applying sentiment analysis to research techniques, brands can systematically collect data from a large number of customers across multiple communication platforms. People share tons of experiences online every day and hour. There are more than 330 million active users on Twitter alone, and they send over 500 million tweets each day. They post about various things, including their experiences with brands. Having all this data analyzed and categorized lets marketers get a broader picture of the market and their competitors. This, in turn, helps to better understand customers’ desires and create unique value propositions.

Stock market prediction

Stock prices are difficult to predict. They change out of the blue and depend on multiple economic and political factors. Curiously, the data shows that there is also a strong correlation between stock prices and social media sentiments. Articles, news, reviews, even a single tweet can influence brand reputation and impact stock performance.

In 2018, when an American celebrity, Kylie Jenner, tweeted her displeasure with Snapchat, the company’s stock prices went down by 7%, losing over $1.3 billion of market value.

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