An Introductory Guide to Emotion Analytics

Emotions guide us through our daily lives; they are an essential part of the human experience, and they inevitably influence our decisions. We tend to do things that make us happy over and over again, but we avoid doing things that make us angry or sad.

 

This is particularly when it comes to a business brand. Our emotions are often predetermined and biased before even using a particular product or service. But how do companies decipher the thoughts and emotions of people regarding their product or service? 

 

This is exactly where Emotion Analytics steps in. 

 

 

What is Emotion Analysis?

 

Emotion analysis is the process of identifying and interpreting the underlying emotions expressed in textual data. Emotion analytics uses text data from a variety of sources to analyse subjective data and understand the emotions that underpin it. 

 

In some cases, sentiment analysis may fall short of capturing the customer's true feelings. The process of identifying and interpreting the underlying emotions expressed in textual data is known as emotion analysis. 

 

Fear, rage, happiness, sadness, love, inspiration, or neutrality are all examples of emotions that can be detected and classified in a text. The main goal is to extract views, ideas, and thoughts from human language by assigning polarities (negative, positive, or neutral).

 

These customers' feelings about their purchases are encoded in the information encoded in their reviews. Reviews and ratings are necessary preludes for some businesses in order to provide some intelligence in the decision-making process and ensure corporate progress.

 

 

Methods of using Emotion Analysis on Text Data

 

Below we’ve discussed a couple of the methods through which Emotion Analysis is used on Text Data. 

 

1. Google Natural Language API

 

Any service article would be incomplete without Google's involvement. So it's no surprise that Google NLP API plays a great role here. The Google NLP API performs entity recognition as well as sentiment recognition at the entity level.

 

 This isn't an emotion analysis, but when we combine entities with sentiment, the end result can be quite impressive. Entities, sentence-level sentiment, entity-level sentiment, syntax-level analysis, and the text's possible category are all returned by the API after it analyses the text.

 

2. Text2emotion

 

Text2emotion is a library that can be found on the PyPi website. The library analyses the data and derives the sentence's emotion from these five emotions (Happy, Angry, Sad, Surprise, and Fear). 

 

It's simple to install using pip on your machine, and it produces a dictionary with the key being the emotion and the value being the emotion's score in relation to your text.

 

3. Microsoft Azure Text Analysis

 

Microsoft is the company that comes in second place behind Google. The Microsoft Azure Text Analysis API can provide text analytics results such as key detection, sentiment detection, named entities, personal entities, and linked entities recognition, to name a few.

 

4. IBM Tone Analyzer

 

IBM has made the IBM Tone Analyzer accessible to us via an API, an online service, and a chatbot. The tone analyzer produces two tone levels: document tone and sentence tone Document tone sets the tone for the entire corpus, whereas sentence tone breaks the document down into sentences (if possible) and provides three arguments.

 

Returning to the application side, the above two approaches have limitations in that, while they were able to detect the words that contributed to specific emotions, those words were unable to understand my data.

 

Text, tone, and sentence identification The IBM Tone Analyzer is capable of detecting seven different tones (4 emotions: Anger, Fear, Joy, Sadness and 3 Language Styles: Analytical, Confident and Tentative). 

 

A tone with a score of less than 0.5 is ignored, indicating that the emotion will not be taken into account in the content. A score of more than 0.75 indicates that the tone is likely to be perceived.

 

5. NRCLex

 

The National Research Council of Canada (NRC) is a government-funded research organisation in Canada (NRC). NRCLex is based on the lexicon dictionary, as well as NRC's Wordnet synonym sets for the NLTK library. 

 

Fear, Anger, Anticipation, Trust, Surprise, Sadness, Disgust, and Joy are among the eight emotions found in NRClex, as well as two sentiments (Positive, Negative). The library, like its counterpart, can be easily imported using pip, with the result being a dictionary.

 

Here's a great explanation of how the algorithm works. The library offers a wide range of functions. So go ahead and experiment with them to see which one works best for you. Affect frequencies were the one in my case. 

 

Also Read | Latest Social Network Analysis Software

 

 

What makes Emotion Analysis better than Sentiment Analysis?

 

To figure out and quantify how people react to new products, both emotion analysis and sentiment analysis are used. Emotion and Sentiment Analytics can help "content creators" respond with tailored offerings, whether it's measuring the response to a new TV series or an event.

 

Although Emotion and Sentiment Analytics may appear to be the same, the two techniques have some significant differences. Both techniques aim to better understand the needs of the customer, but they do so in different ways. The words and emojis used in text can be analysed using software.

 

1. The data is oversimplified by Sentiment Analysis

 

Because human emotions are so complex, simplifying them to positive and negative is a huge mistake. You'll need a holistic view of your audience's feedback if you want to leverage your marketing.

 

Only a rudimentary understanding of your audience can be gained by categorising emotional feedback as positive, negative, or neutral. If you want to understand your customers' motivations and roadblocks, you'll have to dig a little deeper.

 

2. It provides Useful Information

 

"Positive" and "negative" attributes can be quite frustrating if you're trying to improve your content. You'd have no idea what to do with that bit of information. It's a little too generic.

 

It's much easier to take action if you know the specific emotions your readers are experiencing. Emotional states become part of the other metrics you track to improve the performance of your content. And if you know what your content inspires, you can easily identify where it succeeds and where it fails, and take appropriate action.

 

3. It Provides More Useful Information

 

Unlike sentiment analysis, emotional analysis provides a more in-depth understanding of people's actions. You'll need more than a negative/positive percentage to figure out why someone bounced or stuck through a post. 

 

You'll need a precise number that reflects how enjoyable, frustrating, or boring your content was for them. Because that's the key to figuring out why your content is succeeding or failing.

 

While sentiment analysis can tell you how well your content is doing, emotional analysis can help you figure out why. In some ways, sentiment analysis captures only one facet of the vast world of human emotions.

 

Also Read | Emotion vs Sentiment Analysis

 

 

Customer Service Impacts of Sentiment and Emotion Analysis

 

Understanding customer sentiment in relation to customer service, as well as the emotional context surrounding sentiment, allows businesses to improve customer interactions in ways that matter to them. 

 

Sentiment analysis, when combined with emotion analysis, removes the guesswork from the process, revealing the positive or negative impact of each service decision made by your team.

 

1. Prioritizing Specific Interactions: It can be beneficial to use sentiment and emotion analysis to actively highlight how customers are feeling while on the phone, interacting with a chatbot, or chatting with an agent. 

 

A powerful sentiment and emotion analysis system can alert your team to interactions that need to be escalated to a more appropriate representative as they progress, allowing you to optimize even the most difficult customer contact scenarios.

 

2. Agents of surveillance: When it comes to customer service, one out of every three customers says that a competent, friendly agent is the most important factor to them. As a result, it makes sense to assess sentiment and emotion by agent in order to identify those who could benefit from guidance in providing better customer service.

 

In addition, sentiment and emotion analysis can help you determine the best actions and behaviours to take. 

 

For example, if a collection agency analyses sentiment and emotion and discovers that callers are more likely to make payment promises and express fewer negative emotions when an agent expresses empathy, these findings can be used to inform agent training and performance management programmes.

 

Also Read | AI in Customer Service

 

 

Impact of Emotion Analytics

 

The use of emotion-sensing technology, such as facial recognition software, in conjunction with digital assistant apps is expected to improve UX significantly. According to Gartner, by 2022, people's smart devices will know more about their emotional state than their own family.

 

Customers who have had a positive emotional experience with a company are 15 times more likely to recommend the company than customers who have had a negative emotional experience, as per a 2016 study by customer experience firm Temkin Group. 

 

Customers who have had a positive experience are six times more likely than those who have had a negative experience to forgive a company if it makes a mistake.