Deep Learning model BERT of Neural Network has been implemented to find the sentiment using text & then hosted on web app.

Sentiment Analysis with BERT: BERT is a state-of-the-art deep learning model that utilizes a transformer architecture to understand & represent the contextual information of words in a text. It has achieved remarkable performance in various natural language processing tasks, including sentiment analysis. Sentiment analysis aims to determine the sentiment or emotional tone expressed in a given text. Mood has been categorised into 5 levels : 1)Excellent 2)Good 3)Fine 4)Bad 5)Extremely Bad

Model Implementation: To implement sentiment analysis using BERT, you would typically follow these steps:

Preprocess the text data: Tokenize the text into individual words or subwords, and convert them into numerical representations. Load the BERT model: Import the pre-trained BERT model, which has been trained on a large corpus of text data.

Tell sentiment: Use the fine-tuned BERT model to tell the sentiment of new text inputs. Hosting on a Web App: Once you have trained and saved your BERT sentiment analysis model, you can host it on a web app to provide a user interface for sentiment analysis. Here are the general steps involved:

Set up a web framework: Choose a web framework like Anvil. Create web app endpoints: Define the necessary endpoints in your web app for handling user requests and responses. Load the BERT model: Load the saved BERT model into your web app's backend. Process user inputs: Receive user inputs (text statements) from the web interface, preprocess them as required, and pass them to the BERT model for sentiment prediction. Display results: Return the predicted sentiment back to the user interface and display it to the user. Web App Link : https://wilted-left-quail.anvil.app/

LICENCE : https://github.com/TusharPaul01/Sentiment-Analysis-Hear-Mind-ML-Web-App/blob/main/LICENSE

LinkedIn ID : https://www.linkedin.com/in/tusharpaul2001/