Classifies the movie reviews as positive or negative using LSTM Networks

MACHINE LEARNING / NLP / AMAZON SAGEMAKER: This an exemplary implementation of Web Application predicting if provided movie review is POSITIVE or NEGATIVE. This application uses Machine Learning model trained and deployed on Amazon SageMaker environment.

In this implementation, i have done sentiment analysis of Movies reviews from imdb dataset with LSTM using Keras API of Tensorflow.

Real time application of Sentiment Analysis on Movie Reviews. A Machine Learning Flask App hosted on Heroku and created on Google Colab. https://swetakesurnlp-playground.herokuapp.com

Analysing Snappfood comments with Bidirectional LSTM (BiLSTM)

LSTM Model based Twitter Text Emotions Analysis

Natural Language Processing Course- MOOC

Sentiment Analysis of Indonesian Negative Comments on Social Media Using Long Short-Term Memory (LSTM)

Vietnamese Student Feedback Sentiment Analysis

Sentiment Analysis of Books and Its impact on children using Deep Learning

Assignment of Introduction to Artificial Intelligence

The objective of this task is to detect hate speech in tweets.

An LSTM model is trained in this project to identify the sentiment of a given review. Flask is used along with gunicorn to develop a web application that gives the sentiment of the user review. Docker and Kubernetes are used in the Google Cloud Platform to deploy and scale the model.

REST API para exponer modelo de red reuronal recurrente (LSTM) para analisis de sentimientos usando base de datos de twitter

Text-Shield-Offensive-Text-Detection

Sentiment classification: From ML to DL to very DL models

the model for weather