An Introduction to Emotion Detection
If you’re interested in NLP (natural language processing), you’ve come to the right place. In this article, we demonstrate a great application of NLP: emotion detection. We also discuss the difference between emotion detection and sentiment analysis. Want to see emotion detection in practice? Then check out the notebook we created for this purpose.
Our daily decisions are strongly influenced by our emotions. These feelings are a crucial element in the human experience of our daily lives. When something makes us happy, we repeat it often, but when something makes us feel anger or sadness, we avoid it (García, 2019). This aspect of our daily lives is a crucial element of human nature.
With the rapid development of technology, the analysis of emotion detection is a topic that has been extensively researched in many disciplines. Using Natural Language Processing (NLP) and Machine Learning, we can infer an individual’s emotional behavior from a text.
NLP uses computational linguistic techniques to help machines understand and generate human languages in the form of texts or speech. Through textual data, we can identify small subsets of behaviors that may reflect overall emotion.
We can imagine you wondering what then is the difference between emotion detection and sentiment analysis. The primary goal of both tasks is to analyze and better understand an author’s condition. Sentiment analysis focuses on analyzing and characterizing a text in terms of polarity (“positive,” “negative” or “neutral”).
Emotion detection can be considered the broader concept of sentiment analysis, which allows us to identify emotions such as: happiness, fear, anger, etcetera, for example. Want to read more about the difference between these tasks and what advantages and disadvantages they each have? This blog from analyticsteps gives a nice overview of the subject matter. Now let’s talk a little more about the history and practical component of emotion detection.
Detecting emotion through text
Textual data can contain different associated emotions, and there are different approaches to analysis. The first approach to detecting emotions was introduced in 1997 as “affective computing” by Since Picard. Since then, there has been a great development of different ways someone can analyze text data to identify the different emotions of an author or speaker.
Being able to identify an individual’s emotional state can be useful in many areas, for example, education, politics, suicide prevention, improving customer experience and employee satisfaction, generating emotion-friendly chatbots, etc.
We can use these forms of textual input and emotion detection for many purposes, such as understanding behavior, as well as generating and predicting conversational content. By identifying the emotions represented by the author’s text, we can respond appropriately by extracting subjective information from textual sources such as reviews, feedback, posts on social media, transcribed conversations, and more.
Organizations can use emotion detection technology in many areas within a company. Consider monitoring the tone of customer feedback, customer experience, employee satisfaction and improving responses from an automated system such as chatbots.
Using these tasks, organizations can improve their annual profits/products by analyzing customers’ emotions toward their products. Based on reviews and feedback, they can get a better understanding of customers’ emotions.
This kind of information can help improve a company’s marketing strategy, as well as the quality of a product.
For the professional
The main purpose of this paper was to introduce an emotion detection task and highlight the importance of detecting emotions through textual data.
With this blog, we show the value of an NLP task such as emotion detection. These types of NLP tasks can benefit an organization in many areas. Think customer experience but also internal insights such as employee attitudes and satisfaction.
Remarkably, sociolinguistic components can be vital to the performance of an emotion detection model. For example, an individual’s cultural component may influence the results; not every culture expresses emotions in the same way.
Moreover, other factors such as gender and age also matter, as each individual reacts and feels in their own unique way.
Finally, another aspect is codeswitching, when the speaker/author uses more than one language. It is often used in the informal text environment, such as social media, and it contains a large number of emotional expressions (Wang et al., 2015). In this case, the emotional state or expression must be taken into account in both forms.
Should you want to get started with emotion detection yourself after reading this article, look here for a practical example, including code.
Did you like this article and are you interested in more NLP articles? Then be sure to read our Natural Language Processing series.
