How can I implement word embedding using Keras for semantic text analysis

Question

With the help of proper code can you tell me How can I implement word embedding using Keras for semantic text analysis?

Answer 1

Use tf.keras.layers.Embedding to convert words into dense vector representations for semantic text analysis.

Here is the code snippet you can refer to:

In the above code, we are using the following approaches:

• Embedding Layer (Embedding): Transforms words into dense, learnable vector representations.
• Semantic Understanding: Captures word relationships in vector space.
• GlobalAveragePooling1D: Compresses sequence representations efficiently.
• LSTM for Contextual Learning: Extracts sequential patterns in text.
• Pre-trained Embeddings (Optional): Can replace with GloVe or Word2Vec for better performance.

Hence, Keras' Embedding layer enables effective semantic text analysis by converting words into meaningful vector representations for NLP models.