Sentiment Analysis with RNNs in Keras: How Is a Final Sentiment Label Generated in the Prediction Phase?

What Does the model.predict() Function Output in a Keras Sentiment Analysis Model?

Understand the final output of the prediction phase in a Keras sentiment analysis project. Learn how the trained model processes a new, unseen movie review and outputs a definitive sentiment label—either “positive” or “negative”—classifying the emotional tone of the text.​

Question

What is the output of the prediction phase in this sentiment analysis project?

A. The number of hidden layers used in the model
B. A generated movie review
C. A plot of accuracy over epochs
D. A sentiment label (positive or negative) for a review

Answer

D. A sentiment label (positive or negative) for a review

Explanation

Predictions classify reviews as positive or negative. The ultimate purpose of the trained sentiment analysis model is to take a new piece of text as input and assign a classification label to it.​

The prediction phase is the culmination of the entire machine learning workflow, where the trained model is used to make judgments on new, unseen data. In this sentiment analysis project, the process is as follows:​

1. A new movie review (a string of text) is provided to the model.
2. This text undergoes the same preprocessing steps used for the training data (tokenization, integer encoding, padding) to convert it into a numerical sequence that the model can understand.
3. This numerical sequence is fed into the trained LSTM model.
4. The model performs a forward pass, processing the sequence and outputting a raw prediction. In the case of a binary classification with a sigmoid activation function in the final layer, this output is a single value between 0 and 1.
5. This value represents the model’s predicted probability of the review being “positive.” A threshold (typically 0.5) is applied to this probability to make a final decision. If the output is greater than 0.5, the model assigns the label “positive”; otherwise, it assigns the label “negative.”

Therefore, the final, human-interpretable output of the prediction phase is the categorical sentiment label derived from the model’s raw numerical prediction.​

A. The number of hidden layers used in the model (Incorrect): The number of layers is a fixed architectural hyperparameter of the model and is not an output of the prediction process.

B. A generated movie review (Incorrect): This model is designed for classification (discriminative task), not for generating new text (generative task). A different type of architecture, like a language model, would be required to generate reviews.

C. A plot of accuracy over epochs (Incorrect): Accuracy and loss plots are artifacts of the training and validation phases, used to monitor and diagnose the model’s learning process. They are not an output of the prediction phase, which is concerned with applying the trained model to new data.

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