Sort the words by their frequency of occurrence and use these frequencies as encodings in your model, arguing that more frequent words are more important for classification.

Generate a one-hot encoding for each word, ensuring that each word is represented as a unique vector in a high-dimensional space, to maintain distinctiveness between words.

Assign a unique numerical identifier to each word from 1 to 100,000, treating each word as an independent category without considering semantic relationships.

Utilize pre-trained word embeddings to represent each word, capturing semantic similarities and differences between words based on their usage in a large corpus of text.

Combine the approaches of one-hot encoding for less frequent words and pre-trained word embeddings for more frequent words, to balance between computational efficiency and semantic richness.