The present invention discloses a method and a system for filtering goods review information. The method comprises: acquiring a plurality of predetermined advertisement spam samples, each advertisement spam sample comprising a review text and a user identification; establishing an advertisement spam user identification library comprising the user identifications of the plurality of advertisement spam samples; and acquiring a new review comprising a user identification and a review text, and determining the new review as an advertisement spam review if the user identification of the new review is included in the advertisement spam user identification library. An advertisement spam review is identified according to a user identification that publishes the review in the present invention. A new method is provided in the technical field of identifying an advertisement spam review for solving the problem that messy spam reviews are difficult to identify.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for filtering goods review information, comprising: acquiring a plurality of predetermined advertisement spam samples, each advertisement spam sample comprising a review text and a user identification; establishing an advertisement spam user identification library comprising user identifications of the plurality of advertisement spam samples; and acquiring a new review comprising a user identification and a review text, and determining the new review as an advertisement spam review if the user identification of the new review is included in the advertisement spam user identification library. 2. The method for filtering goods review information according to claim 1, further comprising: establishing a collection of advertisement spam reviews comprising the review texts of the plurality of advertisement spam samples, training a text classifier using the collection of advertisement spam reviews as a training set for the text classifier, and performing, by the text classifier, classification on an input review text to determine it as an advertisement spam review text or a non-advertisement spam review text; after acquiring the new review, if the user identification of the new review is not included in the advertisement spam user identification library, performing, by the text classifier, classification on the review text of the new review: if the text classifier classifies the review text of the new review as an advertisement spam review text, determining the new review as an advertisement spam review, adding the user identification of the new review to the advertisement spam user identification library, adding the review text of the new review to the collection of advertisement spam reviews, and retraining the text classifier. 3. The method for filtering goods review information according to claim 2, further comprising: extracting advertisement keywords from the collection of advertisement spam reviews to compile an advertisement dictionary; if the text classifier classifies the new review as a non-advertisement spam review text, filtering the review text of the new review with the advertisement dictionary; if the review text of the new review includes a number of advertisement keywords from the advertisement dictionary that is greater than or equal to a predetermined advertisement threshold, determining the new review as an advertisement spam review, and adding the user identification of the new review to the advertisement spam user identification library, adding the review text of the new review to the collection of advertisement spam reviews, and retraining the text classifier. 4. The method for filtering goods review information according to claim 3, further comprising: if the review text of the new review includes a number of advertisement keywords from the advertisement dictionary that is less than the predetermined advertisement threshold, performing spam review text analysis on the review text: if a result of the spam review text analysis on the review text is a spam review text, determining the new review as an advertisement spam review, adding the user identification of the new review to the advertisement spam user identification library, adding the review text of the new review to the collection of advertisement spam reviews, and retraining the text classifier. 5. The method for filtering goods review information according to claim 4, wherein the step of performing spam review text analysis comprises: calculating a single-character ratio of the review text after word segmentation, and determining the review text as a spam review text if the single-character ratio is greater than or equal to a predetermined ratio threshold. 6. A system for filtering goods review information, comprising: a sample acquiring module for acquiring a plurality of predetermined advertisement spam samples, each advertisement spam sample comprising a review text and a user identification; an identification library establishing module for establishing an advertisement spam user identification library comprising user identifications of the plurality of advertisement spam samples; and a new review determining module for acquiring a new review comprising a user identification and a review text, and determining the new review as an advertisement spam review if the user identification of the new review is included in the advertisement spam user identification library. 7. The system for filtering goods review information according to claim 6, further comprising: establishing a collection of advertisement spam reviews comprising the review texts of the plurality of advertisement spam samples, training a text classifier using the collection of advertisement spam reviews as a training set for the text classifier, and performing, by the text classifier, classification on an input review text to determine it as an advertisement spam review text or a non-advertisement spam review text; after acquiring the new review, if the user identification of the new review is not included in the advertisement spam user identification library, performing, by the text classifier, classification on the review text of the new review: if the text classifier classifies the review text of the new review as an advertisement spam review text, determining the new review as an advertisement spam review, adding the user identification of the new review to the advertisement spam user identification library, adding the review text of the new review to the collection of advertisement spam reviews, and retraining the text classifier. 8. The system for filtering goods review information according o claim 7, further comprising: extracting advertisement keywords from the collection of advertisement spam reviews to compile an advertisement dictionary; if the text classifier classifies the new review as a non-advertisement spam review text, filtering the review text of the new review with the advertisement dictionary; if the review text of the new review includes a number of advertisement keywords from the advertisement dictionary that is greater than or equal to a predetermined advertisement threshold, determining the new review as an advertisement spam review, and adding the user identification of the new review to the advertisement spam user identification library, adding the review text of the new review to the collection of advertisement spam reviews, and retraining the text classifier. 9. The system for filtering goods review information according to claim 8, further comprising: if the review text of the new review includes a number of advertisement keywords from the advertisement dictionary that is less than the predetermined advertisement threshold, performing spam review text analysis on the review text: if a result of the performing spam review text analysis on the review text is a spam review text, determining the new review as an advertisement spam review, adding the user identification of the new review to the advertisement spam user identification library, adding the review text of the new review to the collection of advertisement spam reviews, and retraining the text classifier. 10. The method for filtering goods review information according to claim 9, wherein the spam review text analysis comprises: calculating a single-character ratio of the review text after word segmentation, and determining the review text as a spam review text if the single-character ratio is greater than or equal to a predetermined ratio threshold.