Web api built on flask for keras-based sentiment analysis using Word Embedding, RNN and CNN

This project is an implementation of sentiment analysis of IMDB movie reviews using a Recurrent Neural Network (RNN) model. The goal of the project is to classify movie reviews as positive or negative based on the text content of the review. The project is built using Python, Flask web framework, and TensorFlow Keras API.

A sentiment analysis model based on the Keras IMDB dataset using bidirectional LSTMs. Deployed using REST framework on django

YouTube & IMDB Sentiment Analysis Web App Tech Stack: Python, Flask, Keras, TensorFlow, HTML/CSS, JavaScript, Google YouTube API Built a web app to perform sentiment analysis on user comments from YouTube videos and IMDB reviews using a trained deep learning model (LSTM).

This repo hosts a web app that predicts stock trends using machine learning and tweet sentiment analysis. It combines historical data with real-time tweet sentiment for accurate insights. Features include trend forecasting and visualizations using Python, WordPress Flask, JS, HTML/CSS, Twitter API, Keras, Tensorflow, and scikit-learn

This is a web app which can be used to *analyze users' sentiments across Twitter hashtags*. Its created using React and Django and uses an LSTM model trained on the [Kaggle Sentiment140 dataset](https://www.kaggle.com/kazanova/sentiment140) and served as a REST API to the ReactJS frontend. The server pulls tweets using **tweepy** and performs inference using Keras. It also pulls data from the **Wikipedia API** based the hashtag chosen to display a short description. As part of the analysis, I also added few examples of the tweets and their predicted sentiments. A kernel for another sentiment classification using a CNN + 1D pooling can be found [here](https://www.kaggle.com/thatawkwardguy/twitter-sentiment-classification-using-cnns)