Why Sentiment Analysis Tools Matter in 2022 and Beyond

The modern business thrives on feedback. Thankfully, it’s a very very generous world when it comes to giving feedback. Technological advances have made sure that surveys are easy to conduct, customer reviews barge in from all directions, and social media… Well, social media is a world of its own. Many brands are simply inundated with unstructured data that they can’t make sense of to use in any meaningful way.

However, there is a class of thriving brands that know how to channel this flood of data into sentiment analysis tools for daily, meaningful use. And you can join their ranks!

First, let’s frame the landscape.

how to use social sentiment for competitive analysis

What is Sentiment Analysis?

Sentiment analysis is the use of technology to determine the emotion behind statements or opinions at scale. Sentiment analysis tools use Artificial Intelligence (AI) -based technology to assess unstructured data such as social media posts, customer reviews, and employee survey responses. The social media post, review, response, or any other form of unstructured data is tagged as having a positive, negative, or neutral emotion. This is repeated for thousands or millions of datasets. Further, the results are analyzed and quantified, with dashboards generated for quick interpretation.

Sample Tweet with analysis

How Do Sentiment Analysis Tools Work?

Sentiment analysis tools powered by artificial intelligence (AI) extract sentiment in relation to the primary terms towards which you’ve directed your analysis. And these insights can include emotions, behaviors, likes, or dislikes which are expressed as a soundbite.

Generally speaking, for any given topic, some nuggets of text, such as social media posts, will contain sentiment, and some will not. And when sentiment is found, it is expressed as positive, neutral, negative, or mixed.

The first three are self-explanatory, but mixed sentiment means there is positive and negative language in the text. An example of this would be Brand X’s shoes are affordable, but their sizing is off.

In this case, the first half of the sentence talking about affordability is positive and would be assigned a positive value of 1. Conversely, the second half of the sentence that mentions ill-fit would be given a -1. So, yes, these cancel one another out in theory. Still, the applicable aspect of the sentence is apparent when sorting for either positive or negative attributes.

In other words, when you’re parsing your dataset on Brand X’s positive mentions, you will see that the affordability part of the sentence contributes to overall positivity and vice versa.

All in all, sentiment analysis tools provide the capacity to measure public perception across any topic. This, of course, allows brands to adjust their strategies to best suit their market situation. Let’s look at a few ways brands use sentiment analysis to that end.

How Leading Brands Use Sentiment Analysis Tools to Stay Ahead

Meeting the evolving needs of the 21st century customer requires a keen understanding of their attitudes and emotions. Sentiment analysis can help businesses treat customers “how they want to be treated,” as Brian Solis so aptly put it during a recent NetBase Quid LIVE presentation.

Using sentiment analysis tools, leading brands keep up with current needs by uncovering how consumers feel about them and the market in general. This helps them in a variety of ways:

  • Responding to the voice of the consumer
  • Managing brand perception
  • Managing crises
  • Boosting their marketing performance
  • Developing new products

We are going to look at a couple of these use cases in a moment. But first, here are some statistics to highlight the importance of sentiment analysis tools in business:

  • Consumer attitude vis-a-vis feedback: Approximately 90% of consumers that rate a company as “very good” in customer experience also trust it to handle their needs. Of those who rate a company’s customer experience as “very poor,” only 16% would trust it.
  • Social media as a sharing platform: 48% of American consumers use social media to search out the experiences of other people with a brand.
  • Data volume: Unstructured data accounts for up to 90% of the consumer data available to businesses and it is growing at a rate of at least 55% per year.

And now, let’s see how you can get the most out of a sentiment analysis tool.

Sentiment Analysis Example #1: Voice Analysis

Woe unto the 21st century brand that doesn’t understand consumer voice! Customer service is going to be an uphill battle for businesses that don’t understand the voice of the consumer. Sentiment analysis tools can play a significant role in intelligence gathering, revealing how consumers want to be treated. This is what winning businesses are doing to anticipate and meet customer expectations.

 

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And it’s not just the voice of the customer that needs to be analyzed and understood. It’s also the employees who wake up every day to go and build a big business and strong brand. Leading companies certainly care about what their staff thinks; but it is not very easy to do when you have thousands of employees across multiple territories. The sentiment analysis tool is the solution. This way, brands won’t miss out on important posts that they may want to amplify:

 

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Sentiment Analysis in Healthcare: Pharma

To illustration this, when a leading pharmaceutical company that wanted to understand the mindset of patients with major depressive disorder (MDD) as well as their attitude towards different classes of drugs used in the treatment of their condition, they examined the analytics.

Voice of the patient

Narrowing things to a recent timeline (2018/19), Quid took a deep dive into more than 15,000 patient conversations across various forums and drug review sites to discover key themes, patient experiences, popular medications, and why certain drugs were popular.

This information was fundamental in informing subsequent marketing and outreach actions taken by the drug manufacturer towards this class of patients.

Sentiment Analysis Example #2: Reputation Management

Leading businesses are always monitoring the general perception of their brands, promoting actions that favor them, and getting ahead of those that are less than flattering.

There are many factors that affect brand perception. And public opinion can have a strong impact on how consumers view your company. Further, the actions of the brand itself can affect it in a variety of ways: Through marketing, handling of a crisis, etc. Sentiment analysis can surface the effects of the various factors influencing consumer perception.

Sentiment Analysis in Travel: Airbnb

Is Airbnb in Hospitality, Tech, or Real Estate? Well, that is one of the discoveries made when NetBase Quid did an analysis of close to 4,000 articles about the company.

brand-perception

The study – which was conducted to better understand the prominent topics and issues influencing the public perception of the company – revealed that Airbnb was being discussed as either a tech company or travel agency. Articles describing it as a tech company gained prominence in terms of online engagement. This might suggest how the majority of consumers view their relationship with Airbnb.

There were other pertinent discoveries made that could impact the future success of the company. There were five overarching themes associated with the brand that can be explored and exploited for better customer service. For instance, social issues drove a huge spike in conversation about Airbnb in the months of July and August 2017. Be sure to download the case study to learn more! 

And those are only two examples of intel brands capture using sentiment analysis. What are you capturing with your tool? When choosing a sentiment analysis tool, be sure to find one that allows integration with third-party tools so your brand’s analysis isn’t limited and capture the entire online picture. Reach out for a demo today and we’ll show you sentiment analysis in action, as it relates to your brand!

how to use social sentiment for competitive analysis

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