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embedding-layer-keras

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TwitterSentimentAnalysis

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.

  • Updated Sep 13, 2020
  • Jupyter Notebook

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…

  • Updated Sep 27, 2018
  • Jupyter Notebook

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.

  • Updated Jul 14, 2023
  • Jupyter Notebook

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