Unsupervised neural attention model for aspect extraction

A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for aspect extraction from raw data using an unsupervised neural attention model, the operations comprising:

• receiving a vocabulary, the vocabulary comprising text data that is provided as at least a portion of raw data, the raw data being provided in a computer-readable file;
• associating each word in the vocabulary with a feature vector;
• providing a sentence embedding for each sentence of the vocabulary based on a plurality of feature vectors to provide a plurality of sentence embeddings;
• providing a reconstructed sentence embedding for each sentence embedding based on a weighted parameter matrix to provide a plurality of reconstructed sentence embeddings;
• and
• training the unsupervised neural attention model based on the sentence embeddings and the reconstructed sentence embeddings to provide a trained neural attention model, the trained neural attention model being used to automatically determine aspects from the vocabulary.

A system, comprising:

• a computing device;
• and
• a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations for aspect extraction from raw data using an unsupervised neural attention model, the operations comprising:
• receiving a vocabulary, the vocabulary comprising text data that is provided as at least a portion of raw data, the raw data being provided in a computer-readable file;
• associating each word in the vocabulary with a feature vector;
• providing a sentence embedding for each sentence of the vocabulary based on a plurality of feature vectors to provide a plurality of sentence embeddings;
• providing a reconstructed sentence embedding for each sentence embedding based on a weighted parameter matrix to provide a plurality of reconstructed sentence embeddings;
• and
• training the unsupervised neural attention model based on the sentence embeddings and the reconstructed sentence embeddings to provide a trained neural attention model, the trained neural attention model being used to automatically determine aspects from the vocabulary.