Amazon Technologies, Inc.

Logan Luyet Dillard

Eric B Fox

Russell Dicker

Scott Allen Mongrain

Brandon William Porter

Siddharth Sriram

Terrence R Nightingale

Olga I Kuznetsova

Jason Roselander

Jared L Richardson

James G Robinson

Rahul H Bhagat

Grant Michael Emery

Adam Callahan Sanders

Brian Cameros

Benjamin Thomas Cohen

Yongwen Liang

Eli Glen Rosofsky

Balamurugan Anandan

Michael David Quinn

Technologies are described herein for extracting comments from customer reviews for a media item or a collection of media items in order to provide salient comments to promote the media item or the collection of media items. Comments contained in the customer reviews are associated with topics and individual comments are assigned to one or more of the topics. Sentiment indicators corresponding to the comments indicate a majority sentiment that is associated with the topics. Using the majority sentiment, quotes are extracted from the comments to be displayed with an offer to sell at the media item or the collection of media items.

Extracting quotes from customer reviews of collections of items

Disclosed are various embodiments for extracting an excerpt from a representative review of an item, such as an item available for purchase in an electronic commerce system. Attributes or categories used in reviews of an item may be identified and ranked according to consumer preference. Upon ranking the categories, an excerpt may be extracted from a review corresponding to a ranked one of the attributes or categories. The excerpt may be identified and extracted if a number of reviews for an item exceeds a threshold quantity as it may be impractical for a user to read every review written about the item.

Automatic review excerpt extraction

Technologies are described herein for extracting quotes from customer reviews for a collection of items in order to provide salient comments for a particular item and/or upsell the collection. Individual sentences contained the customer reviews regarding the collection of items are parsed into a collection of sentences. A list of topics is generated from the collection of sentences, and each sentence in is assigned to one or more of the topics. A number of the topics from the list of topics are identified as related to the particular item, and quotes are extracted from the sentences assigned to the identified topics to be displayed as customer reviews of the particular item. Similarly, a number of topics in the list of topics are identified as related to a customer satisfaction with the collection of items, and quotes are extracted from the sentences assigned to the identified topics to be displayed in conjunction with an offer to upsell the collection of items.

Disclosed are various embodiments for defining subcategories of items to be used in merchandising. The subcategories may be defined on the basis of item data and/or sales data for the items. Based on a distribution of the items in accordance with one or more of the item and/or sales data, implicit groups or subcategories can be identified and selected for merchandising purposes.

Automatic product groupings for merchandising

Disclosed are various embodiments for tracking user behavior relative to a network page and identifying user interest in various content items of the network page according to the user behavior. A network page that includes multiple content items is rendered for display in a client. A user action is obtained relative to the network page. A user behavior report is sent to one or more servers. The user behavior report indicates the user action, a timestamp associated with the user action, and one or more of the content items that are associated with the user action.

Tracking user behavior relative to a network page

Technologies are described herein for extracting quotes for an item from customer reviews. Individual sentences or phrases contained in customer reviews regarding an item or category of items are parsed into a collection of sentences. A list of topics is generated from the collection of sentences, and each sentence or phrases is assigned a topic from the list of topics. The most relevant topics from the list of topics are identified for a particular item, and one or more sentences or phrases from customer reviews for the particular item are selected for each of the most relevant topics that best represent the sentences associated with the item and assigned to that topic, according to content and sentiment.

Extracting quotes from customer reviews

The present technology may identify item category affinities by identifying a plurality of classifications of an item. An accuracy of the plurality of classifications relative to one another for the item may be identified. A category affinity of the item may be determined based on the accuracy of the plurality of classifications relative to one another.

Video classification

Users are enabled to provide structured ratings for various attributes of items or other such content in an electronic environment. Users are able to rate existing attributes associated with an item, or new attributes that the users want to associate with the item. In addition to allowing users to provide a rating for each attribute, users can be prompted to include information relating to these attributes in reviews for the respective item(s). Attributes can be automatically applied to various items using a process that determines aspects of items that are indicative of each attribute being relevant, and automatically applies the attributes to items having at least some of those or similar aspects. Various models and algorithms are described for providing such functionality.

Methods and system of associating reviewable attributes with items

Disclosed are various embodiments for extracting a representative review excerpt from a plurality of reviews associated with an item available for purchase in an electronic commerce system. A review extractor identifies a lexicon appearing in the reviews and associates each of the reviews with at least one category where the categories are based on the identified common lexicon. The review extractor then ranks the categories according to a ranking of attributes specified by consumer preferences. Upon ranking the categories, the review extractor then extracts a representative review excerpt based on a correlation between the number of reviews associated with each category and the ranking of that category as indicated by the consumer preferences.

Disclosed are various embodiments for defining subcategories of product items to be used in merchandising. The subcategories may be defined on the basis of product data and/or sales data for the product items. Based on a distribution of the product items in accordance with one or more of the product and/or sales data, implicit groups or subcategories can be identified and selected for merchandising purposes.

Technologies are described herein for extracting quotes from customer reviews regarding collections of items. An identifier of an individual item is received and customer reviews regarding a collection of items containing the individual item are retrieved. A collection of sentences is parsed from the retrieved customer reviews, and sentences that discuss the individual item are identified. A quote is extracted from the identified sentences and displayed to a customer in conjunction with information regarding the individual item. Similarly, individual items belonging to a collection of items are identified, and customer reviews regarding the individual items are retrieved. A collection of sentences are parsed from the retrieved customer reviews, and those sentences that discuss topics relevant to the collection of items are identified. A number of quotes are extracted from the identified sentences and displayed to a customer in conjunction with information regarding the collection of items.

Extracting quotes from customer reviews regarding collections of items

Technologies are described herein for classifying sentences or phrases as expressing positive or negative sentiment based on machine learning from training data comprising sentences manually labeled as to sentiment. A list of terms is generated from the manually labeled sentences and sentiment scores are determined for the terms in the list of terms based on the manually labeled sentences. A collection of sentences or phrases may then be classified as to sentiment utilizing one or more logistic regression classifiers trained on the sentiment scores determined for the terms in the list of terms. The classified collection of sentences may be further analyzed to determine an overall majority sentiment regarding a topic discussed in the sentences and/or to extract specific sentences or phrases expressing a particular sentiment for display to a customer.

Determining sentiment of sentences from customer reviews

Disclosed are various embodiments for selecting a subset of categories of product items to be used in merchandising. The subset of categories may be selected on a basis of a measured level of interest in the product items. Based on the subset of subcategories that have been selected, merchandising presentations may be automatically formulated and presented to a customer.

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Seattle, WA, United States of America

Logan Luyet Dillard