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: A frame represents a concept with a set of roles and a set of linguistic rules. If a linguistic rule is satisfied, by a unit of natural language discourse (UNLD), the frame is invoked and a frame instance produced. A frame instance specifies how the UNLD, with particular values drawn from the UNLD, fulfills the roles of the frame. A frame-based search, of target content, can produce a search result comprised of records and corresponding frame instances. The values of such frame instances can be presented to the user as a role-value oriented search result. Multiple values of a role-value oriented search result, sufficiently similar in meaning, can be merged. Merged values can be represented, in a role-value oriented search result, by a single value. Selection of a value, of a role-value oriented search result, can cause the records, for which the value occurs in the corresponding instance, to be displayed to the user.

Abstract: Pinnacle concepts are not amenable to detection by the use of keywords. A unit of natural language discourse (UNLD) “refers” to a pinnacle concept “C” when that UNLD uses linguistic expressions in such a way that “C” is regarded as expressed, used or invoked by an ordinary reader of “L.” A reference can have a “reference level” value that is proportional to: the “strength” with which the pinnacle concept is referenced, the probability that a pinnacle concept is referenced or both strength and probability. Pinnacle concepts can be divided into Quantifiers and non-Quantifiers. A Quantifier can modify the reference level assigned to a non-Quantifier. A concept “C,” that is determined to be referenced by a UNLD “x,” after application of its Quantifiers, is said to be asserted by “x.” Concept-based classification is the identification of whether a pinnacle concept “C” is asserted by a UNLD. Concept-based classification can be used for concept-based search.

Abstract: Searching computer-accessible content can be described as the utilization of an automated process to determine occurrences of where a sought-for concept is referred to in natural language discourse. Concept-based search refers to the reliable identification, within computer-accessible content that represents natural language discourse, of instances in which a particular pinnacle concept “C” is referenced. References to pinnacle concepts are not amenable to detection by the use of keywords. For each pinnacle concept “C,” whose reference is to be determined, a set of linguistic features can be compiled that is referred to herein as a “concept feature set.” In general, it is desirable for a concept feature set to be “complete.” A definition of completeness is presented. Concept-based search can be used in conjunction with keyword-based search.