Human experience and reactions are displayed with emotions. By looking at the face of a human you can tell if he or she liked the product or not. In this digital world, the Millennial and Gen-Z population is expressing themselves through social media platforms either by uploading a picture or sharing their emotions through a tweet. Detecting emotions gives a lot of insights into their reactions to what they see or view or feel. These insights can help companies get immediate and automated feedback about whether their product or advertisement was loved by people or not. They can make more informed decisions to improve the marketing strategy and their products.
Understanding the value of decoding emotions, a lot of companies around the world are working to understand human emotion by reading human faces through pictures or videos. Tech giants like Google, Microsoft, and Amazon are the pioneers in this field as they offer basic emotion analysis. To give you an example, emotion detection cameras have been installed in China where Uyghur Muslims are being held in detention camps. This is done to identify criminal suspects by analyzing their reactions and mental state.
Emotion Detection
Facial detection is an evolved methodology to identify human faces in pictures or videos. A step forward in this field is identifying the emotion a human is displaying in a picture or video or text. Emotion Detection is a machine learning methodology that is focused on building ML models for identifying human emotion. Supervised machine learning or NLP algorithms trained on large datasets can learn and predict emotions accurately.
Let’s understand emotion detection with an example. Below are two sentences:
1. I hate you.
2. I disagree with you and everything you stand for.
Both the sentences have the same negative sentiment, but the emotions are different. In the first sentence, there is more hatred than the second one. Thus, implementing both Sentiment and Emotion analysis helps in understanding the right state of the human mind.
Detecting emotions from text
There are a lot of text-based emotion recognition techniques:
1. Keyword Spotting method: It is one of the basic methods of sensitivity analysis. In this method, critical words are identified by splitting the text into substrings. The keywords are then classified into emotions like fear, anger, happiness, sadness, etc. The class of emotion is recognized after matching the tokenized sentences with emotion categories.
2. Lexical Affinity method: In this method, a probabilistic ‘affinity’ is assigned to arbitrary words in a sentence. For example, the word ‘accident’ will be assigned a high probability to indicate a negative event in the sentence. One drawback of this method is that the probabilities can get biased to certain words or metaphors in a text.
3. Machine Learning based approach: Previously trained emotion detection models and classifiers are used to predict emotions in a text. Any of the trained models like SVMs, decision trees, deep learning models, etc., can be used for prediction emotions.
4. Hybrid approach: This approach is a combination of keyword search and ML learning based methods for detecting emotions. More accurate results can be obtained by implementing a hybrid approach for emotion detection.
Emotion detection can help a business in multiple ways. The first is monitoring the tone of customer feedback. Social media analysis, Employee satisfaction measure, improvement of Chabot responses, etc., are some other benefits that emotion detection offers. Hopefully, this blog has been able to shed some light on the importance of emotion detection for businesses to have a competitive edge in the market.
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