Product or Service Review Summarization Using Attributes

• US 20100169317A1
• Filed: 12/31/2008
• Published: 07/01/2010
• Est. Priority Date: 12/31/2008
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Yaman, Sibel, Wang, Ye-Yi
• Application Number:
  US12/346,903
• Publication Number:
  US 20100169317A1
• Time in Patent Office: Days
• US Class Current: 707/736
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 30/00 Commerce

Abstract

Described is a technology in which product or service reviews are automatically processed to form a summary for each single product or service. Snippets from the reviews are extracted and classified into sentiment classes (e.g., as positive or negative) based on their wording. Attributes are assigned to the reviews, e.g., based on term frequency concepts, as nouns, which may be paired with adjectives and/or verbs. The summary of the reviews belonging to a single product or service is generated based on the automatically computed attributes and the classification of review snippets into attribute and sentiment classes. For example, the summary may indicate how many reviews were positive (the sentiment class), along with text corresponding to the most similar snippet based on its similarity to the attributes (the attribute class).

Claims

1. In a computing environment, a method comprising, processing review data corresponding to a product or service, including automatically obtaining an inventory of attributes for a product or service category, obtaining review snippets from the review data, classifying the review snippets into classification data, assigning attributes to the review snippets, and generating a summary of the review data based on the classification data and the assigned attributes.

2. The method of claim 1 wherein automatically obtaining the inventory comprises representing the review snippets using part of speech tagging, and extracting candidate attributes including name, adjective pairs with part of speech-based patterns.

3. The method of claim 2 further comprising, pruning the candidate attributes based on frequency.

4. The method of claim 2 further comprising, representing a candidate attribute based upon distributions of the adjectives that co-occur with the attribute.

5. The method of claim 4 further comprising, clustering attribute names based upon distributions of the adjectives.

6. The method of claim 1 wherein classifying the review snippets comprises performing sentiment classification for review snippets based on an overall score associated with the review snippets.

7. The method of claim 1 wherein assigning the attributes to a review snippet comprises applying a TF-IDF weighted vector space model.

8. The method of claim 1 further comprising, representing a cluster of at least one attribute with a TF-IDF weighted vector of terms therein, including attribute names, and co-occurring adjectives.

9. The method of claim 8 wherein representing the cluster further comprises representing at least one co-occurring verb.

10. The method of claim 1 wherein generating the summary comprises selecting a representative snippet based on confidence scores from classification and attribute assignment.

11. The method of claim 1 further comprising, processing evaluation metrics indicative of fidelity of a summary.

12. In a computing environment, a system comprising, a classification mechanism that classifies snippets of reviews into sentiment scores for each snippet, an attribute assignment mechanism that assigns attributes to each snippet, and a summary generation mechanism that outputs a summary based on the sentiment score and assigned attributes for a snippet.

13. The system of claim 12 wherein the classification mechanism comprises a maximum entropy model.

14. The system of claim 12 wherein the attribute assignment mechanism comprises a term-frequency, inverse document frequency model that compares snippet vectors against attribute vectors to determine similarity.

15. The system of claim 12 wherein the summary generation mechanism outputs information corresponding to sentiment classification and text based upon a representative snippet.

16. One or more computer-readable media having computer-executable instructions, which when executed perform steps, comprising:

summarizing a set of reviews, including determining a set of attributes corresponding to review data, determining similarity between review data and the set of attributes, and providing a summary based upon the similarity.

17. The one or more computer-readable media of claim 16 having computer-executable instructions comprising, classifying reviews into classification sentiment data, and wherein providing the summary further comprises, outputting information based upon the classification sentiment data.

18. The one or more computer-readable media of claim 16 wherein determining the set of attributes comprises using part of speech tagging to extract candidate attributes, and pruning the candidate attributes based on frequency.

19. The one or more computer-readable media of claim 16 wherein the candidate attribute includes at least one adjective that co-occurs with the attribute, or at least one verb that co-occurs with the attribute, or both at least one adjective and at least one verb that co-occur with the attribute.

20. The one or more computer-readable media of claim 16 having computer-executable instructions comprising, clustering at least some of the attributes based upon distributions of co-occurring adjectives.