Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The top rated products in a product category are identified by computing a score for each product in the category. The score is computed by calculating a time decay factor for each product category. The time decay factor is utilized to decay the ratings of each product as a function of time. A relevance value is also computed for each product. The relevance value is utilized to modify the ratings for each product in view of the recency of the ratings. The score may be computed for each product using the ratings for the product, the computed time decay factor, and/or the relevance value. The computed scores are utilized to rank the products in the product category.

Abstract: 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.