Amazon Technologies, Inc.

Kasturi Bhattacharjee

Rashmi Gangadharaiah

Deepak Seetharam Nadig

Sharon Shapira

Ankit Kapoor

Tony Chun Tung Ng

Sunil Mallya Kasaragod

Yahor Pushkin

Rishita Rajal Anubhai

Abhinav Goyal

Srikanth Doss Kadarundalagi Raghura

Smaranda Muresan

Senthil Chock Chidambaram

Federico Torreti

Siddharth Chaitanyakumar Varia

Senthil C Chidambaram

Systems and methods are used to detect underlying themes from a collection of documents at an aggregated level. A representative set of documents may be selected from a cluster of documents, with the representative set of documents corresponding to a general theme of the cluster. Candidate theme phrases may then be extracted from the documents and used to generate document embeddings and candidate phrase embeddings, which may be ranked, such as with a diversity-based ranking approach. Certain candidates may be selected from the ranking. Each of the documents forming the representative set may then be concatenated and a query embedding may be generated and ranked against the candidate phrases. In this manner, a collection of phrases associated with both the general underlying theme of the cluster, along with granular topics associated with that theme, may be identified.

Theme detection within a corpus of information

A global segmenting and analysis service of a provider network may receive documents (e.g., posts, product reviews) from different applications. The service may analyze the documents to identify target entities and sentiment. The service may generate different levels of sentiment data and store data into a segmented database. For example, the service may store within-document level sentiment, document-level sentiment, and multi-document level sentiment for a target entity. The service may also update the entity taxonomy automatically or with only a small number of sample documents. The client may query the service for the segmented sentiment data.

Global segmenting and sentiment analysis based on granular opinion detection

The present disclosure generally relates to a feedback processing service that can receive customer input, as customer feedback corresponds to a service context. The feedback processing service aggregates semantically similar feedback as a cluster. Then, the feedback processing service can prioritize each of the clusters by ranking each of the clusters.

Feedback prioritization system

Growing community of inventors

Sunnyvale, CA, United States of America

Kasturi Bhattacharjee