How I Helped a Coaching Class Decode Student Sentiment with Python & Hootsuite

To begin with my case study, let me quickly revise you what Sentiment Analysis and Social Listening are and how marketers have been using these techniques to turn insights into action.

In today’s digital-first world, consumers talk about brands more often online than they do directly to the brands themselves. Whether it’s a tweet, a YouTube comment, or a Google review, these conversations hold invaluable insights. That’s where Social Listening and Sentiment Analysis come in.

What is Social Listening?

Social Listening is the process of tracking and analyzing online conversations happening across social media platforms, blogs, forums, and other digital spaces. It goes beyond just counting mentions - it helps brands understand how people feel, what they’re saying, and why it matters.

Using tools like Hootsuite, Sprout Social, or Brandwatch, businesses can monitor:

• Brand mentions

• Competitor mentions

• Industry trends

• Customer feedback

What is Sentiment Analysis?

Sentiment Analysis, a subset of Natural Language Processing (NLP), takes social listening a step further. It’s the technique of using AI to automatically determine the emotional tone behind online comments, reviews, and messages.

The output is usually:

• Positive

• Negative

• Neutral

Advanced models can even detect specific emotions like anger, joy, sarcasm, or frustration.

CASE STUDY : -

How I Helped a Pune-Based Coaching Institute Turn Student Feedback into Actionable Insights Using Sentiment Analysis

When I was working with BizMode Tech in Pune, one of our clients was a well-known coaching institute that catered to high school and entrance exam students across the city. Despite having a strong student base, they were struggling with retention and declining engagement in their newly launched online programs.

They came to us with a simple ask:“We’re hearing mixed things from students and parents online. Can you help us figure out what’s actually going wrong?”

Digging Through the Noise: Enter Social Listening

I proposed starting with social listening, using Hootsuite to monitor and collect public feedback from platforms like Facebook, Twitter, and Quora, where students often vent or praise coaching classes in India. Over two weeks, we gathered hundreds of comments, mentions, and reviews.... some were direct feedback, others were indirect mentions or conversations.

But reading through each one manually wasn’t scalable. That’s when I brought in Python and NLP (Natural Language Processing) to do the heavy lifting.

The NLP Process

I first cleaned the text data using common preprocessing techniques like tokenization, removing stopwords, and lemmatization. Once that was done, I applied sentiment analysis using a hybrid approach:

• VADER for quick polarity scoring on social media comments

• Custom-trained models using TextBlob and scikit-learn for deeper review analysis

What we uncovered was eye-opening:

• 45% of the negative feedback revolved around online class delivery issues — mainly poor audio and lack of interactivity

• Positive comments focused on a few particular teachers who were highly engaging

• Parents were worried about lack of progress reports and felt “left in the dark”

Turning Insights into Action

We compiled these insights into a clear sentiment dashboard using Plotly and Streamlit, and worked with the coaching institute to:

• Revamp their online delivery platform

• Assign their top-performing teachers to larger virtual batches

• Introduce a monthly email performance report system for parents

The Outcome

Within 2 months, the coaching class reported:

• A 28% drop in student churn for online batches

• Over 120 positive mentions across platforms after the changes

• Increased word-of-mouth referrals from parents who felt heard

Looking back, this project reminded me of the real power of data. It’s not just about dashboards or models — it’s about empathy at scale. By combining social listening tools like Hootsuite with NLP and Python, we helped the client listen and act where it mattered most.