Google Inc.

Kerry L Hannan

Jeffrey C Reynar

Ryan T McDonald

Tyler J Neylon

Sasha Blair-Goldensohn

Michael Lloyd Wells

Christopher Rohrs

Junli Xian

A method, a system and a computer program product for ranking reviewable entities based on sentiment expressed about the entities. A plurality of review texts are identified wherein each review text references an entity. A plurality of sentiment scores associated with the plurality of review texts are generated, wherein each sentiment score for a review text indicates a sentiment directed to the entity referenced by the review text. A plurality of ranking scores for the plurality of entities are generated wherein each ranking score is based at least in part on one or more sentiment scores associated with one or more review texts referencing the entity. A plurality of search results associated with the plurality of entities are displayed based at least in part on the ranking scores.

Sentiment detection as a ranking signal for reviewable entities

The present disclosure describes systems and methods for presenting document-specific snippets for documents in site-specific search results. One example method according to the present disclosure includes determining a set of documents that are responsive to a search query, the set of documents including (i) a first document and (ii) a second document that is referenced by the first document, identifying a set of representative keywords associated with the second document, identifying a snippet that (i) is associated with the second document, and (ii) includes one or more terms from the set of representative keywords, and providing a search results page that identifies the first document, the second document, and the snippet.

Methods and systems for presenting document-specific snippets

Disclosed herein is a method, a system and a computer product for generating a snippet for an entity, wherein each snippet comprises a plurality of sentiments about the entity. One or more textual reviews associated with the entity is selected. A plurality of sentiment phrases are identified based on the one or more textual reviews, wherein each sentiment phrase comprises a sentiment about the entity. One or more sentiment phrases from the plurality of sentiment phrases are selected to generate a snippet.

Phrase based snippet generation

A domain-specific sentiment classifier that can be used to score the polarity and magnitude of sentiment expressed by domain-specific documents is created. A domain-independent sentiment lexicon is established and a classifier uses the lexicon to score sentiment of domain-specific documents. Sets of high-sentiment documents having positive and negative polarities are identified. The n-grams within the high-sentiment documents are filtered to remove extremely common n-grams. The filtered n-grams are saved as a domain-specific sentiment lexicon and are used as features in a model. The model is trained using a set of training documents which may be manually or automatically labeled as to their overall sentiment to produce sentiment scores for the n-grams in the domain-specific sentiment lexicon. This lexicon is used by the domain-specific sentiment classifier.

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Brooklyn, NY, United States of America

Kerry L Hannan