Abstract: Techniques are presented for producing demographics, in an automated fashion, from a search result of computer-accessible content. While the demographics can be determined for a research product that has been produced by any technique, they are particularly useful when applied to an automated frame-based search approach. Frame-based search engines are presented for technology profiling, healthcare-related search and brand research. Determination of a demographic proceeds at two levels: member and population. At the member level, a demographic characteristic can be determined applicable with either total or partial certainty. Each value assigned by a demographic, to a population member, has a confidence level associated with it and the assignments can be represented by a Confidence Distribution. Summarization of a demographic, at the population level, depends upon whether the certainty assignments, at the member level, are total or partial. Declarant Demographics are presented.

Abstract: Analysis is enabled, of a corpus of statements (such as those from social media), according to each statement's expression of sentiment about some kind of object. Object-specific corpuses are identified, where each object-specific corpus contains statements that refer to a same object. For each statement of an object-specific corpus, the polarity and intensity of sentiment expressed is determined. Net polarity and intensity measures are determined for each object-specific corpus and utilized to graph the corpus in a two-dimensional space. The area of the graphical symbol, representative of an object-specific corpus, can be proportional to the number (absolute or relative) of statements of the object-specific corpus. Brands can be compared, with each brand represented by an object-specific corpus. A single brand can have shown, relative to a temporal dimension, the net polarity, net intensity, or volume of its statements. Net polarity is shown to have a strong correlation with survey-based techniques.

Abstract: To the standard operations of an inverted index database, a new “To” operator is added. The “To” operator treats the standard single-level linear collection of records as being organized into localized clusters. Techniques for hierarchical clusters are presented. During indexing, hierarchical clusters are serialized according to a uniform visitation procedure. Serialization produces bit maps, one for each hierarchical level, that preserve the hierarchical level of each record and its location in the serialization sequence. The “To” operator accepts a list of records, each at a same hierarchical level in a cluster, and a specification of a hierarchical level that all the input records should be converted into. The “To” operator outputs a list of records, representing a conversion of the input records to the specified new level. When searching a Corpus-of-Interest for an Object-of-Interest, techniques are presented for greatly improving the process by which Exclude Terms are identified.

Abstract: Differences are identified, at the lexical unit and/or phrase level, between time-varying corpora. A corpus for a time period of interest is compared with a reference corpus. N-grams are generated for both the corpus of interest and reference corpus. Numbers of occurrences are counted. An average number of occurrences, for each n-gram of the reference corpus, is determined. A difference value, between number of occurrences in corpus of interest and average number of occurrences, is determined. Each difference value is normalized. N-grams can be selected for display, or for further processing, on the basis of the normalized difference value. Further processing can include selecting a sample period. A plurality of reference corpora are produced, where a begin time, for each sub-corpus of the plurality of reference corpora, differs, from a begin time for the corpus of interest, by an integer multiple of the sample period. Word Cloud visualization is shown.

Abstract: Given a search result, the set of authors-identifiers appearing in it can be determined by use of a hash function, and an array-type data structure called an audience fingerprint (AF). The AF has as many storage locations as the hash function has possible output values. The number of possible output values is chosen to be large enough, with respect to the maximum number of unique authors expected in any one search result, to create a very high probability of a unique output value for each unique author-identifier that is hashed. At the AF location, addressed with a hash value, is stored an indicator that the author-identifier is present. The indicator can be a single bit, simplifying set operations on AFs. When not in working memory, an AF can be stored as a compacted sparse array. The actual author-identifiers present can be determined, from an AF, with an inverse hash function.

Abstract: Differences are identified, at the lexical unit and/or phrase level, between time-varying corpora. A corpus for a time period of interest is compared with a reference corpus. N-grams are generated for both the corpus of interest and reference corpus. Numbers of occurrences are counted. An average number of occurrences, for each n-gram of the reference corpus, is determined. A difference value, between number of occurrences in corpus of interest and average number of occurrences, is determined. Each difference value is normalized. N-grams can be selected for display, or for further processing, on the basis of the normalized difference value. Further processing can include selecting a sample period. A plurality of reference corpora are produced, where a begin time, for each sub-corpus of the plurality of reference corpora, differs, from a begin time for the corpus of interest, by an integer multiple of the sample period. Word Cloud visualization is shown.

Abstract: To the standard inverted index database, a new “To” operator is added. The “To” operator treats the standard single-level linear collection of records as being organized into localized clusters. Techniques for hierarchical clusters are presented. During indexing, hierarchical clusters are serialized according to a uniform visitation procedure. Serialization produces bit maps, one for each hierarchical level, that preserve the hierarchical level of each record and its location in the serialization sequence. Also presented are techniques, when searching for an Object-of-Interest, for greatly improving the process by which Exclude Terms are identified. Exclude Terms are particularly useful when the lexical units, representing an Object-of-Interest, are ambiguous. When in the mode of searching for Exclude Terms, the Object-of-Interest can match anywhere in a snippet, rather than just in the focus sentence.

Abstract: Techniques are presented for producing demographics, in an automated fashion, from a search result of computer-accessible content. While the demographics can be determined for a research product that has been produced by any technique, they are particularly useful when applied to an automated frame-based search approach. Frame-based search engines are presented for technology profiling, healthcare-related search and brand research. Determination of a demographic proceeds at two levels: member and population. At the member level, a demographic characteristic can be determined applicable with either total or partial certainty. Each value assigned by a demographic, to a population member, has a confidence level associated with it and the assignments can be represented by a Confidence Distribution. Summarization of a demographic, at the population level, depends upon whether the certainty assignments, at the member level, are total or partial. Declarant Demographics are presented.

Abstract: Analysis is enabled, of a corpus of statements (such as those from social media), according to each statement's expression of sentiment about some kind of object. Object-specific corpuses are identified, where each object-specific corpus contains statements that refer to a same object. For each statement of an object-specific corpus, the polarity and intensity of sentiment expressed is determined. Net polarity and intensity measures are determined for each object-specific corpus and utilized to graph the corpus in a two-dimensional space. The area of the graphical symbol, representative of an object-specific corpus, can be proportional to the number (absolute or relative) of statements of the object-specific corpus. Brands can be compared, with each brand represented by an object-specific corpus. A single brand can have shown, relative to a temporal dimension, the net polarity, net intensity, or volume of its statements. Net polarity is shown to have a strong correlation with survey-based techniques.