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Sentiment Analysis – Understanding Your Digital Audience

Sentiment analysis – otherwise known as opinion mining – is a much-discussed about but often misunderstood term.

In essence, it is the process of determining the emotional tone behind a series of words, used to gain an understanding of the attitudes, opinions, and emotions expressed within an online mention.

It is the measurement of positive and negative language.

It is a way to evaluate written or spoken language to determine if the expression is favorable, unfavorable, or neutral, and to what degree.

Today’s algorithm-based sentiment analysis tools can handle huge volumes of customer feedback consistently and accurately. Paired with text analytics, sentiment analysis reveals the customer’s opinion about topics ranging from your products and services to your location, your advertisements, or even your competitors.

In the world of CX and data science, sentiment analysis is a key piece of the puzzle in determining customer intent. In Stratifyd’s case, we take a piece of text to determine whether it’s positive, negative, or neutral and leverage it to help companies determine how consumers feel about their products, services, and overall brands. Sounds simple enough, right?

The role of sentiment analysis is growing significantly due to the explosive amount of unstructured data from the rise of globalization and the interconnectedness of systems. Customers like to give feedback in any way that’s convenient to them, whether it’s a phone call, chat session, or open-ended survey. Collecting this unstructured data is the most valuable way to uncover sentiment. With these insights, companies can better understand CSAT, campaign effectiveness, and competitive intelligence.

Why Is Sentiment Analysis Important?

Sentiment analysis is critical because it helps you to see what customers like and dislike about you and your brand.

Customer feedback—from social media, your website, your call center agents, or any other source—contains a treasure of useful business information. But, it isn’t enough to know what customers are talking about. You must also know how they feel. Sentiment analysis is one way to uncover those feelings.

Sentiment analysis can let you know if there has been a change in public opinion toward any aspect of your business. Peaks or valleys in sentiment scores give you a place to start if you want to make product improvements, train sales or customer care agents, or create new marketing campaigns.

Sentiment analysis is not a once and done effort. By reviewing your customer’s feedback on your business regularly, you can be more proactive regarding the changing dynamics in the market place.

Obama Administration used Sentiment Analysis Too!

The Obama administration used sentiment analysis to gauge public opinion to policy announcements and campaign messages ahead of the 2012 presidential election. Being able to quickly see the sentiment behind everything from forum posts to news articles means being better able to strategize and plan for the future.

It can also be an essential part of your market research and customer service approach. Not only can you see what people think of your products or services, but you can also see what they think about your competitors too.  The overall customer experience of your users can be revealed quickly with sentiment analysis, but it can get far more granular too.

Benefits Of Sentiment Analysis For Your Business

  • Allows you to adjust marketing strategies based on how customers feel about it
  • Measures ROI on various marketing campaigns and messages
  • Know customers’ opinions on products/services and better align with their tastes
  • Improve customer service
  • Helps in developing a crisis management protocol
  • Identifies sales, retention, and growth opportunities
  • Boosts sales revenue

Why Perform Sentiment Analysis?

It’s estimated that 80% of the world’s data is unstructured, in other words, it’s unorganized. Huge volumes of text data (emails, support tickets, chats, social media conversations, surveys, articles, documents, etc), is created every day but it’s hard to analyze, understand, and sort through, not to mention time-consuming and expensive.

Sentiment analysis, however, helps businesses make sense of all this unstructured text by automatically tagging it.

Types of Sentiment Analysis

Sentiment analysis models focus on polarity (positive, negative, neutral) but also on feelings and emotions (angry, happy, sad, etc), and even on intentions (e.g. interested v. not interested).

Here are some of the most popular types of sentiment analysis:

Fine-grained Sentiment Analysis

If polarity precision is important to your business, you might consider expanding your polarity categories to include:

  • Very positive
  • Positive
  • Neutral
  • Negative
  • Very negative

This is usually referred to as fine-grained sentiment analysis, and could be used to interpret 5-star ratings in a review, for example:

  • Very Positive = 5 stars
  • Very Negative = 1 star
Emotion Detection

This type of sentiment analysis aims at detecting emotions, like happiness, frustration, anger, sadness, and so on. Many emotion detection systems use lexicons (i.e. lists of words and the emotions they convey) or complex machine learning algorithms.

One of the downsides of using lexicons is that people express emotions in different ways. Some words that typically express anger, like bad or kill (e.g. your product is so bad or your customer support is killing me) might also express happiness (e.g. this is badass or you are killing it).

Aspect-based Sentiment Analysis

Usually, when analyzing sentiments of texts, let’s say product reviews, you’ll want to know which particular aspects or features people are mentioning in a positive, neutral, or negative way. That’s where aspect-based sentiment analysis can help, for example in this text: “The battery life of this camera is too short”, an aspect-based classifier would be able to determine that the sentence expresses a negative opinion about the feature battery life.

Multilingual Sentiment Analysis

Multilingual sentiment analysis can be difficult. It involves a lot of pre-processing and resources. Most of these resources are available online (e.g. sentiment lexicons), while others need to be created (e.g. translated corpora or noise detection algorithms), but you’ll need to know how to code to use them. 

Survey2Connect is a global award-winning customer experience management (CXM) solution that offers 3 core suites designed to help businesses seamlessly improve customer experience, employee experience, and market research. Survey2Connect is an agile CX solution that constantly learns and improves with every experience to give its clients best-in-class experiences through the successful deployment and delivery of 5,703 surveys, 68,804 tickets, 1,743,203 responses, and 756 dashboards


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