Building a neural network for classifying reviews is a complex yet crucial task in the realm of sentiment analysis. The process involves the construction of a multi-layered interconnected network of artificial neurons that can learn patterns and make predictions based on them.
Typically, it starts by collecting a large dataset of labeled reviews, which are then preprocessed to remove noise and standardize the input format. The subsequent step involves architecting the neural network, which may consist of multiple hidden layers with different activation functions to extract hierarchical features from the input reviews.
To improve performance, techniques like word embeddings and recurrent or convolutional layers can be employed to capture semantic relationships or local relevance, respectively.
Training such a neural network requires iterative optimization of parameters using methods like backpropagation and gradient descent to minimize the prediction error. Data augmentation, regularization techniques (e.g., dropout), and hyperparameter tuning are often applied to mitigate overfitting issues.
Evaluating model performance against test data is essential for assessing its effectiveness in accurately classifying movie reviews before deploying it for real-world applications such as sentiment analysis platforms or recommendation systems.
The link below will take you to the code for building such a neural network. I achieved 80+% accuracy on a test data sample, but it was not an easy task. There was a lot of work on data preprocessing and some complex NLP techniques were applied.