Build Movie Review Classification with BERT and Tensorflow

Explore how words become numbers through word embeddings and vectors, enabling text and image data processing with neural networks. Learn why representing data as arrays of numbers powers deep learning.

Explore word2vec, representing words as vectors through continuous bag-of-words and skip-gram models. Train neural networks to predict missing words from context, learning word embeddings and weights.

One hot encoding represents each word as a binary vector with a position. Note sparse vectors have a dimension equal to the vocabulary size, with the vocabulary under 500 words.

Explore how GloVe creates dense word vectors from a co-occurrence matrix with an unsupervised objective and probabilistic weighting. Apply vector arithmetic and nearest-neighbor queries to sentiment analysis, including movie reviews.

Explain how a model's performance is evaluated using accuracy, recall, precision, and the area under the curve of the operating characteristics curve, with a detailed confusion matrix and threshold effects.

Access the movie review classification project code in a Jupyter notebook using BERT and TensorFlow. Download the notebook, upload it to Google Colab, and run it on gpu for free.

Set up a Google Colab runtime with GPU, install TensorFlow 2.4, clone the denser flow model garden with pre trained models, and install prerequisites to explore the Quora dataset.

Download the IMDB movie review dataset, mount drive in Colab, load the data into a dataframe, and create text and target columns for balanced sentiment classification.

Split data into train, validation, and test sets with 90 percent training and 10 percent remainder, then convert text and target columns to tensors for movie review classification.

Download a pretrained model and set up binary classification with a 128 token input, padding, and batch size 32; use about 90,000 samples for fine-tuning.

Explore creating features with the BERT preprocessor to generate input IDs, attention masks, and segment IDs, then classify sentiment with a TensorFlow-based BERT model.

Create the model from three inputs—token ideas, input mask ideas, and input type ideas—using the Battalia and Kiraz model to generate a binary output.

Train the model across four epochs on the prepared training and validation data, updating weights to minimize loss and achieve strong accuracy.

Use the history object from model.fit to plot training and validation loss and accuracy across epochs, identify overfitting when validation loss rises, and consider tuning hyperparameters to improve validation performance.

Test the movie review classifier on real world data by evaluating a sample IMDb review set and predicting sentiment with a 0.5 threshold.

Explore next steps after building a movie review classifier with BERT and TensorFlow, tune learning rate and epochs, and deploy a Flask app to load the model and display sentiment.