# US 2013/0159348 A1 · Computer-Implemented Systems and Methods for Taxonomy Development > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2013/0159348 A1 - Document ID: US20130159348A1 - Status: Published application - Kind: A1 - Application number: 13/327,949 - Filed: 2011-12-16 - Published: 2013-06-20 - Earliest claimed: 2011-12-16 - Assignees: SAS Institute Inc. - Inventors: Bruce Monroe Mills; John Courtney Haws; John Clare Brocklebank; Thomas Robert Lehman - Web page: https://patentgrep.com/patent/US20130159348A1 ## CPC - G06F 16/36: Creation of semantic tools, e.g. ontology or thesauri - G06F 16/35: Clustering; Classification - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Systems and methods are provided for generating a set of classifiers. A location is determined for each instance of a topic term in a collection of documents. One or more topic term phrases are identified, and one or more sentiment terms within each topic term phrase. Candidate classifiers are identified by parsing words in the one or more topic term phrases, and a colocation matrix is generated. A seed row of the colocation associated with a particular attribute is identified, and distance metrics are determined by comparing each row of the colocation matrix to the seed row. A set of classifiers are generated for the particular attribute, where classifiers in the set of classifiers are selected using the distance metrics. ## Claims ### Claim 1 A computer-implemented method for generating a set of classifiers, comprising: determining, using one or more data processors, one or more locations of instances of a topic term in a collection of documents; identifying, using the one or more data processors, one or more topic term phrases by parsing words in the collection of documents, wherein a topic term phrase includes one or more words that appear within a topic threshold distance of a topic term; identifying, using the one or more data processors, one or more sentiment terms within a topic term phrase; identifying, using the one or more data processors, one or more candidate classifiers by parsing words in the one or more topic term phrases, wherein a candidate classifier is a word that appears within a sentiment threshold distance of a sentiment term; generating, using the one or more data processors, a colocation matrix including a plurality of rows, wherein a candidate classifier is associated with a row, and wherein the colocation matrix is generated using the locations of the candidate classifiers as they appear within the collection of documents; identifying, using the one or more data processors, a seed row, wherein the seed row is selected from among the plurality of rows, and wherein the seed row is associated with a particular attribute; determining, using the one or more data processors, distance metrics by comparing rows of the colocation matrix to the seed row; and generating, using the one or more data processors, a set of classifiers for the particular attribute, wherein classifiers in the set of classifiers are selected using the distance metrics. ### Claim 2 (depends on 1) The method of claim 1, further comprising parsing the collection of documents to identify locations of words within the corpus of documents. ### Claim 3 (depends on 2) The method of claim 2, wherein stemmed versions of the words and the locations of the words are stored in one or more data structures. ### Claim 4 (depends on 3) The method of claim 3, wherein parts of speech are associated with the words and stored in the one or more data structures. ### Claim 5 (depends on 3) The method of claim 3, wherein noun groups are identified in the corpus of documents, wherein individual noun records of the noun groups are removed from the one or more data structures and replaced by a noun group record. ### Claim 6 (depends on 1) The method of claim 1, wherein the sentiment terms are stored in a data structure stored in a computer-readable memory. ### Claim 7 (depends on 1) The method of claim 1, wherein the candidate classifiers are limited to nouns, verbs, or adjectives. ### Claim 8 (depends on 1) The method of claim 1, further comprising determining appearance counts of each of the candidate classifiers in the corpus of documents, wherein the colocation matrix is limited to a top N candidate classifiers having highest appearance counts. ### Claim 9 (depends on 1) The method of claim 1, wherein the colocation matrix identifies a frequency that a particular candidate classifier appears within a colocation threshold of each other candidate classifier in the colocation matrix. ### Claim 10 (depends on 9) The method of claim 9, wherein the colocation threshold is equal to the topic threshold. ### Claim 11 (depends on 1) The method of claim 1, wherein a distance metric for a particular candidate classifier is determined based on a Euclidean distance calculation using values from the candidate classifier row in the colocation matrix and corresponding values in the seed row in the colocation matrix. ### Claim 12 (depends on 1) The method of claim 1, wherein candidate classifiers having associated distance metrics within a distance threshold are selected for inclusion in the set of classifiers for the particular attribute. ### Claim 13 (depends on 1) The method of claim 1, wherein the corpus of documents includes one or more of: websites, books, magazines, and social media postings. ### Claim 14 (depends on 1) The method of claim 1, wherein the corpus of documents includes more than 1000 documents. ### Claim 15 (depends on 1) The method of claim 1, wherein the set of classifiers are provided to a downstream computer for processing. ### Claim 16 (depends on 1) The method of claim 1, wherein the corpus of documents comprises text entered into a computer by a person using an input interface device. ### Claim 17 (depends on 1) The method of claim 1, wherein the topic term is a company name, and wherein the particular attribute is an attribute of the company. ### Claim 18 (depends on 17) The method of claim 17, wherein the particular attribute is a customer service attribute, a product attribute, a product mix attribute, a product price attribute, or a company culture attribute. ### Claim 19 (depends on 1) The method of claim 1, wherein synonyms of selected classifiers are included in the set of classifiers. ### Claim 20 (depends on 1) The method of claim 1, wherein the colocation matrix is stored in one or more data structures store in a computer-readable medium. ### Claim 21 (depends on 1) The method of claim 1, wherein the one or more sentiment terms are identified by comparing words in topic term phrases to words in a sentiment term list. ### Claim 22 (depends on 21) The method of claim 21, wherein the sentiment term list contains words that connote human feeling. ### Claim 23 (depends on 21) The method of claim 21, wherein the sentiment term list includes the terms: like, love, hate, and dislike. ### Claim 24 A computer-implemented system for generating a set of classifiers, comprising: one or more data processors; a computer-readable medium encoded with instructions for commanding the one or more data processors to execute steps including: determining one or more locations of instances of a topic term in a collection of documents; identifying one or more topic term phrases by parsing words in the collection of documents, wherein a topic term phrase includes one or more words that appear within a topic threshold distance of a topic term; identifying one or more sentiment terms within a topic term phrase; identifying one or more candidate classifiers by parsing words in the one or more topic term phrases, wherein a candidate classifier is a word that appears within a sentiment threshold distance of a sentiment term; generating a colocation matrix including a plurality of rows, wherein a candidate classifier is associated with a row, and wherein the colocation matrix is generated using the locations of the candidate classifiers as they appear within the collection of documents; identifying a seed row, wherein the seed row is selected from among the plurality of rows, and wherein the seed row is associated with a particular attribute; determining distance metrics by comparing rows of the colocation matrix to the seed row; and generating a set of classifiers for the particular attribute, wherein classifiers in the set of classifiers are selected using the distance metrics. ### Claim 25 A computer-readable medium encoded with instructions for commanding one or more data processors to execute method for generating a set of classifiers, the method comprising: determining one or more locations of instances of a topic term in a collection of documents; identifying one or more topic term phrases by parsing words in the collection of documents, wherein a topic term phrase includes one or more words that appear within a topic threshold distance of a topic term; identifying one or more sentiment terms within a topic term phrase; identifying one or more candidate classifiers by parsing words in the one or more topic term phrases, wherein a candidate classifier is a word that appears within a sentiment threshold distance of a sentiment term; generating a colocation matrix including a plurality of rows, wherein a candidate classifier is associated with a row, and wherein the colocation matrix is generated using the locations of the candidate classifiers as they appear within the collection of documents; identifying a seed row, wherein the seed row is selected from among the plurality of rows, and wherein the seed row is associated with a particular attribute; determining distance metrics by comparing rows of the colocation matrix to the seed row; and generating a set of classifiers for the particular attribute, wherein classifiers in the set of classifiers are selected using the distance metrics.