Document representation for machine-learning document classification

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 providing weighted vector representations of documents, the operations comprising:

• receiving text data, the text data comprising a plurality of documents, each document comprising a plurality of words;
• processing the text data to provide a plurality of word-vectors, each word-vector being based on a respective word of the plurality of words;
• determining a plurality of similarity scores based on the plurality of word-vectors, each similarity score representing a degree of similarity between word-vectors;
• grouping words of the plurality of words into clusters based on the plurality of similarity scores, each cluster comprising two or more words of the plurality of words;
• and
• providing a document representation for each document in the plurality of documents, each feature in the feature vector comprising a cluster, each feature having a weight assigned thereto that represents a relative importance of a respective cluster to a respective document based on weights of constituent words in the cluster, each weight being determined based on a sum of frequency values of words in the respective cluster of the respective document and a sum of document frequency values of words in the respective cluster of the respective document across the plurality of documents.

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 unsupervised aspect extraction from raw data, the operations comprising:
• a receiving text data, the text data comprising a plurality of documents, each document comprising a plurality of words;
• processing the text data to provide a plurality of word-vectors, each word-vector being based on a respective word of the plurality of words;
• determining a plurality of similarity scores based on the plurality of word-vectors, each similarity score representing a degree of similarity between word-vectors;
• grouping words of the plurality of words into clusters based on the plurality of similarity scores, each cluster comprising two or more words of the plurality of words;
• and
• providing a document representation for each document in the plurality of documents, each feature in the feature vector comprising a cluster, each feature having a weight assigned thereto that represents a relative importance of a respective cluster to a respective document based on weights of constituent words in the cluster, each weight being determined based on a sum of frequency values of words in the respective cluster of the respective document and a sum of document frequency values of words in the respective cluster of the respective document across the plurality of documents.