{"id":103198,"date":"2026-03-07T17:02:46","date_gmt":"2026-03-07T11:32:46","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=103198"},"modified":"2026-04-02T10:40:39","modified_gmt":"2026-04-02T05:10:39","slug":"creating-a-sentiment-analysis-app","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/creating-a-sentiment-analysis-app\/","title":{"rendered":"Creating a Sentiment Analysis App with Hugging Face and Streamlit"},"content":{"rendered":"\n

The sentiment analysis app is a tool that reads text and determines whether it is positive or negative<\/strong>. It is an effective way to recognize feelings in messages, reviews, or comments, <\/strong>and finding the means to do so is no longer complicated.<\/p>\n\n\n\n

Now we are going to use Hugging Face for smart text analysis with AI and Streamlit to write a simple interactive application. Step by step, you will know how to transform the code into a live Sentiment Analysis App<\/strong>, which can analyze any text in real-time.<\/p>\n\n\n\n

Quick Answer:<\/em><\/strong><\/p>\n\n\n\n

Quick Answer: A Sentiment Analysis app<\/strong> detects if the text is positive<\/strong>, negative<\/strong>, or neutral<\/strong>. Streamlit<\/strong> helps build a simple, interactive app, while Hugging Face<\/strong> provides pre-trained AI models<\/strong> to analyze the text easily.<\/p>\n\n\n\n

Understand Sentiment Analysis App with Streamlit and Hugging Face<\/strong><\/h2>\n\n\n\n
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Sentiment Analysis App: Definition<\/strong><\/h3>\n\n\n\n

A sentiment analysis app is an application that evaluates the sentiment (whether a sentence or review is positive or negative) by reading and analyzing text.<\/p>\n\n\n\n

For example<\/strong>, when a person types something like “This product is amazing”<\/em><\/strong>, the application will indicate that as positive<\/strong> sentiment, and when the person types something like “I hate this”<\/em><\/strong>, the application will indicate that as negative <\/strong>sentiment.<\/p>\n\n\n\n

How Streamlit and Hugging Face Help Build a Sentiment Analysis App<\/strong><\/h3>\n\n\n\n

Streamlit<\/strong><\/h4>\n\n\n\n

Streamlit<\/strong><\/a> is a minimal framework that lets you create a web app with your Python code in a very short time. You never have to learn web design or web programming, such as HTML or CSS. You can make buttons, text boxes, and make results appear on a web page with just a few lines.<\/p>\n\n\n\n

Hugging Face<\/strong><\/h4>\n\n\n\n

Hugging Face<\/strong><\/a> is a platform that provides pretrained AI models, particularly for text interpretation. It assists you with sentiment analysis, translation, and text generation without requiring you to build everything from scratch. You just have to copy their models, and you can do it in a few steps.<\/p>\n\n\n\n

Brief Overview: How to develop it?<\/strong><\/h4>\n\n\n\n

To create this app, you stage Hugging Face<\/a><\/strong> to obtain a pre-trained sentiment analysis model<\/strong> that understands content, and then use Streamlit<\/a><\/strong> to create an interactive webpage<\/strong> where individuals can type a sentence.<\/p>\n\n\n\n

Once the user types the text, the application forwards it to the Hugging Face model and receives the answer (positive or negative), and displays it on the screen.<\/p>\n\n\n\n

<\/p>\n\n\n\n

\n \ud83d\udca1 Did You Know?<\/strong>

\n \n The first sentiment analysis<\/strong> system was created in 2000<\/strong> by AT&T Labs<\/strong>, paving the way for AI tools<\/strong> like Hugging Face<\/strong>.\n <\/span>\n<\/div>\n\n\n\n

<\/p>\n\n\n\n

Prepare Your Setup for Developing the Sentiment Analysis Application<\/strong><\/h2>\n\n\n\n
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Follow these steps sequentially to set up your environment for developing the app:<\/p>\n\n\n\n

1. Streamlit Installation<\/strong><\/h3>\n\n\n\n

To install Streamlit via the command line and run your first app, carefully follow the official guide here: Streamlit Installation<\/em><\/strong><\/a><\/p>\n\n\n\n

2. Connect the Hugging Face model to your Streamlit app<\/strong><\/h3>\n\n\n\n

In your terminal, make sure your virtual environment is activated \u2014 you should see something like this:<\/p>\n\n\n\n

(.venv) PS E:\\Python Project><\/code><\/pre>\n\n\n\n
This indicates that you are inside your project\u2019s virtual environment<\/strong><\/a>. Then, run the following command to install the Hugging Face Transformers library along with PyTorch<\/strong>:<\/p>\n\n\n\n
pip install transformers torch<\/code><\/pre>\n\n\n\n
Wait until the installation completes fully before moving to the next step. This ensures your app can access the pre-trained sentiment analysis models.<\/p>\n\n\n\n
3. Importing Lottie Animations (locally as JSON files)<\/strong><\/h3>\n\n\n\n
To show animated icons in your app, you can use Lottie animations<\/strong> saved as JSON files. First, install the required packages by running:<\/p>\n\n\n\n
pip install streamlit-lottie requests<\/strong><\/p>\n\n\n\n