What is Emotion Detection? What are the methods used for Emotion Detection in Text Analytics?

Emotions play a huge part in human experience, and several times, they affect our decision-making abilities. How?

Have you ever noticed that we tend to repeat the activities that make us happy and avoid those that make us sad?

In the digital world, information spreads very quickly, and most of our interactions are filled with words that express our feelings and emotions. If these emotions are undealt with, more than often, they tend to intensify and spread like wildfire.

Natural Language Processing (NLP) has enabled us to detect such emotions from written text such as reviews, publications, recommendations, conversations, etc., and take immediate action accordingly.

Emotion Detection refers to the accurate identification of emotion from contextual data. When you need to gain a deeper insight into the underlying tone of a text, we often observe that emotion analysis comes into play and significantly influences business decisions.

What is Emotion Detection?

We can describe Emotion Detection as a process of identifying human emotion from any form of written text. Due to the advanced use of NLP, machine learning, and computational linguistics for extracting emotion and satisfaction relevance in text analysis, this tool has become a prevalent topic for research studies.

While Sentiment Analysis seeks to obtain a polarity from the text, that is, to gauge if a sentiment expressed is positive, negative, or neutral, Emotion Detection takes it a step further.

It provides us with an insight into the underlying reasons for the sentiment output: happy, sad, angry, confusion, curiosity, and so on.

For example, here are three sentences:

1. I want to return this product, maybe order a replacement.
2. This is not a friendly product.
3. I hate this product; not useful at all.

Although all three sentences deliver us with the same sentiment, which is negative, when we examine the emotion behind each sentence, it is quite different. According to this, the approach to customer service would also be very different and should be handled by marketers and client support teams in distinct ways.

What is the significance of using Emotion Detection?

During interactions over digital media, emotions become a significant component in the communication between people of different cultural languages. Generally, we can divide emotions into six types which are: Joy, Surprise, Love, Anger, Sadness, and Fear.

In the context of written text, emotions may be expressed by a single word, for example,

• Great.
• Nope.
• Recommend.

Or it can be a group of words,

• This is great!
• Nope, won’t buy again!
• Definitely recommend this product!

Thus, phrase- and sentence-level emotion detection methods play a vital role in tracking emotions or questing the indications for recognizing emotions. A document always consists of sentences that have words or phrases. The detection of emotions from word level, phrase level, and document level is very complex. But at the same time, it is highly crucial for an accurate simulation of customer behaviour.

The fast-flowing internet services have facilitated increased online communication and written content over the websites. That has led to the flow of large amounts of online content rich in user opinions and varies across digital platforms.

The exchange of emotions through plain text, messages, tweet posts, comments, etc., is primarily unstructured and informal.

Hence, the need to broaden our horizons to detect the emotions from such text data has increased tremendously. It has become a significant challenge for any individual handling data to detect complex emotions from large data volumes.

With the help of advanced computational methods to efficiently analyze this online content, Emotion Detection recognizes and pictures out valid conclusions and detection of emotions.

At Textrics, we have trained our analytics suite to categorise emotions into four categories: Happy, Sad, Angry, and Fear, based on RNN deep-learning technology. The detection of accurate emotions helps you to correctly judge the context of plain text and gives a better insight into what the author wants to convey.

What are the methods of Emotion Detection?

There are four different text-based emotion recognition techniques, namely:

Keyword spotting method, Lexical Affinity Method, Learning-based method, and Hybrid methods.

A. Keyword Spotting Method

This method is straightforward to implement and intuitive since it involves identifying words from the text. The keyword pattern matching problem can be described as finding keywords from a given data set as substrings in a given string.

These words are classified as disgust, sadness, happiness, anger, fear, surprise, etc.

The sentences are tokenized, and the individual words are matched with the various categories of emotions by considering the intensity and negation of words. The class of emotion is recognized, and the behavior is judged.

B. Lexical Affinity Method

This method is an extension of the keyword spotting method. This assigns a probabilistic affinity for a particular emotion to arbitrary words rather than detecting predefined emotional keywords from the text.

The probabilities assigned by this method are part of linguistic corpora. This has some limitations as the assigned probabilities are prejudiced toward corpus-specific metaphors of texts. They do not recognize the emotions from the text that do not reside at the word level on which this method operates.

Let us consider the sentence, “This comedian is so funny, he’s killing me! ”.

The phrase “killing me” indicates the high probability of having a negative emotion in the above sentence. But the exact situation in this sentence does not represent the negative assessment from the “killing” word.

C. Learning-Based Approach

Learning-based methods attempt to detect emotions based on previously trained results and classifiers, which are mapped with various machine learning classifiers such as support vector machines, specific statistical learning methods, and decision trees, to detect which emotion class the text belongs to. This method has the hurdle of classifying sentences into only two categories — positive and negative due to insufficient feature gathering.

D. Hybrid Based Approach

This approach is based on combining the keyword-based method and learning-based method, which offers accurate results and manages high costs in information retrieval tasks. Although quite effective, this lacks hidden emotional patterns and in-depth analysis of context and semantic components.

How can Emotion Detection help your business?

As we have seen, Emotion Detection is beneficial for any business or individual handling data. Let us look at some scenarios where Emotion Detection can be applied and used to optimize business services:

1. Customer Feedback: By monitoring the tone of feedback provided by customers, you can quickly identify customer needs and complaints. This is key to preserving the competitiveness of a company.
2. Integration with Chatbots: The Chatbot feature is a growing field where customers expect personalities to respond to their queries efficiently. Companies can detect customer feelings by building believable dialogues. It also becomes fundamental that chatbots can adapt to a conversation and empathize with customers.
3. Social Media Analysis: With the help of Emotion Detection, we can extract valuable information regarding consumers’ emotions over social media interactions. On this basis, you can define alerts and priorities for action, provide insights, and improve user experience.
4. Improving Marketing Tasks and Efforts: Emotion Detection can help marketers identify customers’ needs and pain points. It also helps in enhancing customer relationship management.
5. Measuring Employee Satisfaction: Discovering internal problems beforehand is crucial for a company’s human resource team. Happier employees can contribute to better performance and thereby avoid talent leaks.

For businesses and individuals to provide optimal services to customers, there is a need to identify the different emotions expressed by people and provide tailored recommendations to meet the individual needs of their customers.

Textrics is a powerful text analysis platform that allows you to detect emotions quickly and understand the needs and criteria of your business for real-time, data-driven decisions.

Take a look at all of our tools and solutions to see how we can help you understand the emotions of your customers and employees more efficiently.

Sign up for our free demo for a personal walk through the emotion detection process.

Visit our website: https://www.textrics.ai/