Text2emotion: Python package to detect emotions from textual data | by Amey Band | Towards Data Science

Emotion is the state of mind that is aligned with feelings, and thoughts usually directed toward a specific object. Emotion is a behavior that reflects personal significance or opinion regarding the interaction we have with other human beings or related to a certain event. The human being is able to identify the emotions from textual data and can understand the matter of the text. But if you think about the machines, can they able to identify the emotions from the text?
From this article, you will understand how to use this python package and extract the emotions from the text data.
What is text2emotion?How to use it?Industry Use Case DetailsConclusionKey Takeaways
What is text2emotion?
How to use it?
Industry Use Case Details
Conclusion
Key Takeaways
Don’t worry, I will take care that you won’t get tired of reading this article and will try to explain it in the easiest way.
Let’s get started,
Text2emotion is the python package developed with the clear intension to find the appropriate emotions embedded in the text data. The research says that when the human is in the thinking process and he is damn sure about his statement then he will express his emotions in the right context of manner and it will be proper aligned in case of words expressing those emotions.
If I want to tell you in simple words then consider if the customer did not like the product by investing the large amount then he will surely give his feedback like “I am very angry by your product services and gonna file a complaint regarding this issue”. When you read this kind of feedback then you will be sure that the customer is completely angry about product services and we have to improve it as soon as possible. Text2emotion works, in the same manner, to extract the emotions from the text.
Let’s conclude this, Text2Emotion is the python package that will assist you to pull out the emotions from the content.
Processes any textual data, recognizes the emotion embedded in it, and provides the output in the form of a dictionary.
Well suited with 5 basic emotion categories such as Happy, Angry, Sad, Surprise, and Fear.
The best way to install the text2emotion package is by using pip.
#Install package using pippip install text2emotion
Let’s import the necessary libraries that you require to build your model.
#Import the modulesimport text2emotion as te
Get some content to evaluate the performance.
text = "I was asked to sign a third party contract a week out from stay. If it wasn't an 8 person group that took a lot of wrangling I would have cancelled the booking straight away. Bathrooms - there are no stand alone bathrooms. Please consider this - you have to clear out the main bedroom to use that bathroom. Other option is you walk through a different bedroom to get to its en-suite. Signs all over the apartment - there are signs everywhere - some helpful - some telling you rules. Perhaps some people like this but It negatively affected our enjoyment of the accommodation. Stairs - lots of them - some had slightly bending wood which caused a minor injury."
Now we have to call the get_emotion() function using the above-defined text parameter.
#Call to the functionte.get_emotion(text)#The output we received,{'Angry': 0.12, 'Fear': 0.42, 'Happy': 0.04, 'Sad': 0.33, 'Surprise': 0.08}
Here, we got the output in terms of the dictionary where we have emotion categories along with the respective score.
Now, if we think about the scores of the relative emotion categories then the Fear score is 0.42 & the Sad score is 0.33. So on the overall analysis, we can say that the statement we took as input has the Fear & Sad tone.
The good thing about the package is, it is able to identify the emotion from the emojis which describes human behavior. Let’s take an example,
text = "Day was pretty amazing😃😃"te.get_emotion(text)#Output{'Angry': 0.0, 'Fear': 0.0, 'Happy': 0.8, 'Sad': 0.0, 'Surprise': 0.2}
From the output, you can conclude that the text input belongs to the Happy and Surprise emotion category.
We have designed a web app for you where you can experiment with all these things. I provide the link below, feel free to check it.
Enter the text in the input box.
Enter the text in the input box.
2. Hit the submit button and get the output.
Check out the web app here.
Consider as an organization, we always concern about the opinions, feedback, or improvements from the customers. What does the customer think about our product? he may be happy or sad or angry also because of the services provided by the organization you never know about it. This is the condition where text2emotion can be your savior. Here, I will discuss two real industry use cases.
Analyzing the input received from customers through various sources such as textual data from chat-bots, logs from contact centers, emails, etc. Tracking these tone signals can help Customer Service Managers improve how their teams interact with customers.
In today’s digital world Brand Monitoring and reputation management has become one of the most important aspects of every business unit. This is where emotion analysis comes into the picture. It will help companies by allowing them: In tracking the perception of the company by the consumers, in pointing out the attitude of the consumers by giving specific details, finding different patterns and trends, in keeping a close look on the demonstration by the influencers.
I hope you got the idea about the basic functionalities provided by this library and how it can be useful for different industry use cases. If you like the package and want to design some interesting implementations and come up with innovative industry use case examples then it will be our pleasure to showcase it.
Text2emotion Documentation
Google Colab Demo
Website Details
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