Sentiment Analysis with LSTM and Keras in Python

Explore sentiment analysis algorithms, from rule-based and negation-aware rules to machine learning classifiers and deep learning with LSTM and RNN, including hybrid approaches that fuse rules with data-driven models.

Explore the sentiment analysis workflow, from gathering unstructured data and cleaning to pre-processing and vectorizing text with bag-of-words or one-hot encoding, then selecting and training a classifier and making predictions.

Outline a sentiment analysis workflow using the IMDB movie reviews dataset, with preprocessing, embedding, and an LSTM model in Keras for Python; train, evaluate, and save the model for predictions.

Load the imdb sentiment dataset with keras, then pre-process by limiting vocabulary to a max word count and truncating sequences to prepare data for an lstm model.

Decode IMDB movie reviews by mapping word indices back to words, using a start-of-sequence token and unknown tokens, and apply padding or truncation to fixed-length sequences for decoding.

Learn to build a sentiment analysis pipeline with custom data by loading CSV, creating a word index with a tokenizer, embedding and converting reviews to sequences for modeling.

Define a sentiment analysis model in python using keras, with word embeddings of dimension 200 and a 20,000-word vocabulary, an lstm layer to capture word order, and a sigmoid output.

Explore word representations from one-hot encoding to word embeddings, build a vocabulary, and create vectors of size 10000 to capture semantic similarity for sequence-based sentiment tasks.

Master one-hot representation of words in Keras by converting words to integers, configuring a 10000-word vocabulary, and encoding documents as one-hot vectors.

Implement a Keras embedding layer for sentiment analysis, turning words into a trainable embedding matrix and feeding it to subsequent layers, with sequence padding and fixed input length.

Explore how the LSTM layer in Keras uses embedding to shape fixed-length input sequences, define batch size, and produce outputs via a dense layer for sentiment analysis.

Train a sentiment model with an embedding layer and a 64-unit LSTM in Keras Python to classify reviews as positive or negative using a sigmoid output.

Evaluate the trained sentiment analysis model on the test data to measure accuracy across twenty five thousand attributes, and preview predictions on new data in the next lesson.

Learn how to prepare a new review for prediction by tokenizing text, mapping words to indices, handling unknown words, and feeding fixed-length sequences into a trained model to predict sentiment.

Explore how a corrected prediction workflow handles tokenization, lowercase normalization, and word-index lookup in sentiment analysis using an LSTM model in Keras, highlighting vocabulary limits and accuracy.

Scale the sentiment analysis model by adding multiple lstm layers with an embedding and dense layer, enabling return_sequences to feed deeper layers and produce a final sigmoid output.

Explore sentiment analysis with an lstm and Keras in Python, building an embedding layer, an lstm, and a dense layer for binary classification with sigmoid activation on movie reviews.