This repository contains details and key points of research papers related to NLP and Deep Learning

NLP-FinHeadlines-MoodTracker is a NLP project utilising sentiment analysis on financial news headlines. It employs a combination of CNN and LSTM layers to predict sentiment (positive, negative, neutral). The model incorporates an embedding layer, 1D convolution, max pooling, bidirectional LSTM, dropout, and dense layer for sentiment classification.

Contains courses in specializations of coursera on deep learning

Sentiment analysis is analysis of the sentence that gives the opinion of the sentence. In this project, it will be implemented a model which inputs a sentence and gives the appropriate emoji to be used with sentence.

Use of word embeddings to classify sentiments of sentences and automatically attach emojis

Using Random Forest , Bi Direction LSTM and Tensorflow Transfer Learning to do a text classification project. Compare model differences between tokenization and word embedding.

A study of the use of Long Short Term Memory (LSTM) for the sentiment classification of movie reviews on the well-known IMDb website.

Sentiment classification of an imbalanced data set with text data using sklearn and keras (ML and DL)

Sentiment analysis for Twitter's tweet (in Indonesia language) was built with 3 models to get a comparison in determining which model gives the best results for predicting a tweet to have a positive or negative meaning.

Use this repository to effectively use word embeddings for your next NLP Project!

Using the IMDB data found in Keras here a few algorithms built with Keras. The source code is from Francois Chollet's book Deep learning with Python. The aim is to predict whether a review is positive or negative just by analyzing the text. Both self-created as well as pre-trained (GloVe) word embeddings are used. Finally there's a LSTM model an…