Social Media Provides a lot of data that can be used to find patterns and make predictions by analyzing use cases of social media applications. In this article, I’m going to introduce you to machine learning projects on social media analysis solved and explained using Python.
Machine Learning Projects on Social Media Analysis
Social media apps like Facebook, Instagram, WhatsApp, and even Tinder are the best at the services they provide. From online content creators to offline service providers, social media apps help everyone.
But how do you use machine learning in social media analytics? The use of machine learning will be different in each application. For example:
- WhatsApp data can be used to analyze what people are texting.
- Instagram data can be used to analyze how to reach more people.
- Youtube data can be used to analyze the type of trending videos.
- Facebook data can be used to analyze sentiments.
- Tinder data can be used to find who the person is going on a date with.
So there are so many use cases that we can use for social media analysis with Machine Learning. Below are the social media applications for social media analysis with Machine Learning:
- Instagram Algorithm
- Facebook Posts Sentiment Analysis
- WhatsApp Chats Sentiment Analysis
- Youtube Trending Videos Analysis
- Predict Tinder Matches
Now let’s go through all these use cases one by one.
Machine Learning Use Cases For Social Media Analysis
Predicting the reach of Instagram posts is one of the most important tasks for every business that relies heavily on social media customers. So in such competition, it is very important to know how the Instagram algorithm works.
What people see in their Instagram posts and stories depends on a combination of the user behaviour they display in most of the content they see. The most important characteristics that contribute to the reach of your Instagram posts are the type of posts people interact with, the type of posts they like and engage in discussion.
When Instagram’s algorithm receives a positive signal according to the above features in your posts, the algorithm gives more opportunities for your post to gain greater reach among your audience. In this project, you will learn how to implement the Instagram algorithm with Machine Learning.
Facebook is a very good platform to perform sentiment analysis task because users are free to express their opinions on any topic be it political or environmental, users are free to share their opinions.
For the Facebook posts sentiment analysis task, you need to extract your data from Facebook first, which is a very easy task, just follow the steps mentioned below:
- Go to settings & privacy
- Then go to settings
- From the left click on Your Facebook Information
- Click on view at Download your information
- Then only select posts and click on create file.
Facebook will send you a notification in the next 60 minutes to download your data. You have to search for “your_posts_1.json” file in the downloaded data, as we only need this data fro the task of Facebook posts sentiment analysis with Python. In this project, you will learn how to analyze the sentiments of Facebook posts.
So I am a part of a WhatsApp group named as “Data Science Community”, recently I thought to explore the chat of this group and do some analysis on it. If you don’t know how to extract the messages from any chat then just open any chat click on the 3 dots above, select more and then select explore chat, and share it with any means, most preferable your email.
The chat you will get at the end does not need any cleaning and preparation it can be used directly for the task. In this project, you will learn how to analyze the chats going in a WhatsApp group and how to analyze the messages sent by each person in the group.
YouTube is the world’s most popular and widely used video platform today. The dataset that I will be using for the analysis of Youtube trending videos was collected over 205 days. For each of those days, the dataset contains data on trending videos for that day. It contains data on over 40,000 trending videos.
We will analyze the data to get insight on trending YouTube videos, to see what is common among all trending videos. This information can also be used by people who want to increase the popularity of their videos on YouTube.
To predict tinder matches we will be solving a case study where we will be simulating the tinder algorithms with Machine Learning. The case study for simulating the tinder algorithms is mentioned below.
My friend Hellen has used some online dating sites to find different people to date. She realized that despite the site’s recommendations, she didn’t like everyone she was matched with. After some soul-searching, she realized that there were three types of people she was dating:
- People she didn’t like
- The people she loved in small doses
- The people she loved in large doses
After finding out about this, Hellen couldn’t figure out what made a person fall into one of these categories. They were all recommended to her by the dating site. The people she liked in small doses were good to see Monday through Friday, but on weekends she preferred spending time with the people she liked in large doses. Hellen asked us to help him filter future matches to categorize them. Also, Hellen has collected data that is not recorded by the dating site, but she finds it useful in selecting who to date.
So these were the case studies where we can implement machine learning for analyzing social media applications. I hope you liked this article on Machine Learning projects on Social Media analysis. Feel free to ask your valuable questions in the comments section below.