If you are interested in the evolution of technology when it comes to text mining tools and Natural Language Processing (NLP) tasks, then you are in the right place! This article presents an interesting NLP task: Emotion Detection. We are introducing what emotion detection is as well as the beneficial function that many fields can gain implementing it. In this post we are presenting different ways of implementing this type of task as well as the difference between emotion detection and sentiment analysis. If you also would like to see this model in practice, we generated a notebook implementing one approach mentioned.
Our daily decisions are strongly influenced by our emotions. These strong feelings are a crucial element in the human experience of our daily lives. When something makes us happy, we often repeat it, 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 has been a subject widely researched in many disciplines such as psychology, neuroscience, and has drawn the attention of many more such as linguistics, computer science and Natural Language Processing (NLP). With the help of NLP and Machine Learning we can observe emotional behaviors associated with an individual 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 behavioral subsets through textual indicators or verbal signals that can represent the overall emotion. You might be wondering, but how can we benefit from such a task?
Being able to identify the emotional state of an individual can be beneficial in many domains. Identifying emotional signals is key in many fields, for example, educational environment, political matters, 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, for example, behavior understanding, generating, and predicting conversational content. Identifying the emotions represented by the author’s text we can respond appropriately by extracting subjective information from textual sources like reviews, feedback, posts on social media, transcribed conversations, etc.
In this blog we will focus on the contribution of emotion detection in the business field/customer experience. Organizations can use emotion detection technology in many domains within a business, such as monitoring tone of customer feedback, customer experience, employee satisfaction and improving responses from an automated system such as chatbots. With the help of this task organizations can improve their annual profit/products by analyzing the emotions of customers towards their products. Based on reviews and feedback they can have a better understanding of customer emotions. This type of information can help improve the marketing strategy of a company as well as the quality of a product.
Some of you might be reading this article and wonder but what is the difference between emotion detection and sentiment analysis. Before we dive into the practical component of how an individual can carry out an emotion detection task, let me tell you what makes these tasks different.
Emotions Detection Vs Sentiment analysis
Due to the similarity of these tasks and overlapping advantages, many people tend to use these terms interchangeably. However, they are two different tasks. The primary goal of both tasks is to analyze and better understand the state of an author. Sentiment analysis focuses on analyzing and characterizing a text in terms of polarity. The main aim is to understand the general feeling of the author, without providing in-dept information of specific emotions. In this case, when we are analyzing textual data in terms of sentiment the labels most frequently used are ‘positive’, ‘negative’ or ‘neutral’. While in the case of emotion detection there is a larger set of labels that can be used to describe a text. Emotion detection can be considered the broader concept of sentiment analysis. It is a more complex analysis to understand the broader spectrum of human emotions, for example, with this we can identify emotions such as: happiness, fear, anger, etc.
Sometimes it can be the case that due to many aspects in textual data such as ambiguity, irony or sarcasm, polarity labels are not enough to represent the view of the author. Therefore, emotion detection is the way to go here. If you are interested more in the difference between these tasks and what advantages and disadvantages each one might have, a blog by analyticsteps gives a great overview of the matter. Now let’s talk a bit more about the history and the practical component of Emotion Detection.
Detecting Emotion through text
Textual data can contain several associated emotions and there are multiple approaches of analyses. The first approach of detecting emotional states was introduced as ‘affective computing’ in 1997 by Since Picard. This concept proposed the role of emotions in human computer interaction. Since then, there has been great research on the matter in many disciplines from computer science to cognitive science (Shivhare and Khethawat, 2012). There has been a great development of different ways that an individual can analyze text data to identify the different emotional state of an author or speaker. This article will present some different approaches that can be used. The following methods were based on emotion detection research paper written by Canales and Martinez-Bacco, 2014.
For this task, the techniques can be divided into lexicon based and machine learning based. When we are talking about lexicon-based methods we are referring to tasks such as ‘keyword spotting’. This is a simple technique where specific keywords within the data are associated with an emotional state. Let us imagine that we have the sentence “The dog at the park looked scary”, in this case we can see that the keyword ‘scary’ can give us a hint on the emotional state, ‘fear’. These keywords are classified into emotion labels and texts are classified per emotion based on the keywords within the text. Of course, based on the text context and elements such as ambiguities, or sarcasm identification of the emotion cannot be possible only based on keywords. In this case it might be better to approach the task with a machine learning based approach. With the use of a pre-trained emotion classifier model, we can easily implement an emotion detection task. Machine Learning approaches apply algorithms based on the linguistic features of the data to learn and detect emotions on labelled training data. Some examples of emotion trained models can be: Support Vector Machine (SVM), deep learning models, Naïve Bayes models, etc. Finally, both methods can be combined to generate a hybrid approach. In this case we can combine keywords with a machine-based approach.
As with any other task, there are some limitations that you need to take into consideration when implementing and evaluating such models. The performance of an emotion detection model is based on the accuracy of the emotion classification but there are some sociolinguistic components that can be vital. For example, a cultural component of an individual can influence the results, not every culture expresses emotions in the same way. Moreover, other factors such as gender and age are important as every individual reacts and feels in their own unique way. Finally, another aspect of the data that can influence the performance of an emotion detection task is codeswitching. Codeswitching is when a speaker/author is using more than one language. It is commonly used in the informal text environment, such as social media, and it contains a great number of emotional expressions (Wang et al., 2015). In this case the emotional state or expression needs to be taken into consideration in both forms.
The main aim of this article 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 kinds of tasks can benefit an organization in many domains. For example, customer experience as well as internal insights such as employ attitude and satisfaction. If you are also interested in learning how to implement this kind of task, we got you! We generated a notebook that executes an emotion detection task based on a machine learning approach. Finally, if you enjoyed reading this article and you would like to know more about NLP tasks and their value, feel free to look at our series: Natural Language Processing.