In today’s digital world every brand knows how important social media has become for them to drive their businesses. Every brand tries to get sales or conversions of its products or services by driving its potential customers emotionally on social media through Ads, Posts, videos, memes, etc. Have you ever thought how useful it would be for your business to know the emotions of your customers about your product by analyzing the feedback or comments from your social media posts? This article gives you an idea of the same using text2emotion, a python package developed by me along with three of my colleagues.
Table of Contents:
- Social Media Monitoring
- Working of Text2Emotion
- How to use it?
- Key Takeaways
It helps you in classifying the tone of the text by categorizing it into five different emotions as Happy, Angry, Surprise, Sad, and Fear.
- Processes any textual message and recognizes the emotions embedded in it.
- Compatible with 5 different emotion categories as Happy, Angry, Sad, Fear, and Surprise.
2. Social Media Monitoring:
Let us now look at an Industrial use case where analyzing emotions from text plays a vital role to give us more clarity on this topic.
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 plays a vital role. Understanding how the end-users or customers recognize your brand or product is very useful for every company and organization.
We can implement the text2emotion package to create a software that brings flexibility into the business by giving information about the perception of a brand by the end users and gives more insight into the reputation of the company and its products. 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.
All this helps us in modifying our product and services according to the need of the customers and generate more revenue.
3. Working of Text2Emotion:
Let us now look at the working of this package.
A) TEXT PRE-PROCESSING
In the first step, our aim is to remove all the impurities or unwanted things from our data by data cleaning so that it can become suitable for emotion analysis.
- Remove the unwanted textual part from the content.
- Perform natural language processing techniques.
- Obtain the well-pre-processed text after the text pre-processing.
B) EMOTION IDENTIFICATION
In the second step, we will identify the different emotions from the words obtained from pre-processed text and will keep a count of each and every emotion.
- Find those words which appropriately express emotions or feelings.
- Inspect the emotion category for each word.
- Store the count of all the emotions relevant to all the words which were found.
C) EMOTION ANALYSIS
After the completion of Emotion Identification, we need to analyze the emotions in order to get proper output for the input message.
- We will obtain the output in a dictionary form.
- The keys will be in the form of emotion categories and their values in the form of emotion scores.
- We can decide the category to which a particular message belongs by analyzing the highest score of a particular emotion category.
4. How to use it?
Check Google Collab Demo:
Below given is the demo of code implementation with Streamlit App for the users.
- Enter the text message.
Enter the text message in the box and click on the submit button.
2. Click the submit button.
3. Bingo! Get the output of your message in visual form.
It identifies the emotions in the text and gives you output in visual form accordingly.
Let’s get hands-on experience in the library.
For more information visit:
5. Key Takeaways
Altogether, text2emotion can be used:
- In automating the social media monitoring process.
- In monitoring mentions or reviews of the brand on different social media platforms like Facebook, Twitter, Instagram, etc.
- In categorizing different reviews of the customers and knowing which social media platform and which type of user is important for the company.