Inspiration
In daily conversations—messages, comments, social media posts—people often misunderstand the emotional tone. Positive messages may sound neutral, and neutral messages may sound rude. This inspired me to build a simple, smart tool that instantly shows the emotion behind any text. I wanted something that everyone can use and understand in seconds.
What it does
Smart Text Sentiment Analyzer allows users to enter any text and immediately see whether the emotion is Positive, Negative, or Neutral. It analyzes the sentence using natural language processing and returns both the sentiment label and an emoji. This helps users clearly understand the emotional meaning behind messages.
How we built it
I built the app using Python and Streamlit for the user interface. For sentiment detection, I used TextBlob/VADER from the NLTK library.
Steps involved:
Designing a simple and clean UI in Streamlit
Writing Python functions to process user text
Applying sentiment analysis model to classify emotions
Connecting everything in a single Streamlit app
Testing multiple sentences to ensure accurate results
Challenges we ran into
Getting the correct sentiment for short or sarcastic sentences
Choosing the right NLP model for accurate outputs
Making the UI simple enough for anyone to use
Understanding how to deploy and host the app for Devpost
Managing everything as a first-time hackathon participant
Accomplishments that we're proud of
Built a working AI tool as a beginner
Created a clean, minimal interface that anyone can understand
Achieved accurate sentiment classification for most test cases
Successfully completed and submitted my first hackathon project
Learned how to use Streamlit to build a real interactive web app
What we learned
Basics of sentiment analysis and how NLP models work
How to build and deploy a Streamlit application
How to organize a project for a hackathon
How to write clean, beginner-friendly code
How to structure and present a project for Devpost
What's next for Smart Text Sentiment Analyzer
Adding support for multiple languages
Including more emotion categories like joy, anger, fear, surprise
Creating a Chrome extension for quick text analysis
Adding a history panel to store past sentences and sentiments
Building a mobile version for Android/iOS
Improving model accuracy using advanced NLP models
Log in or sign up for Devpost to join the conversation.