Abstract:
A method for extracting keywords affecting the emotional change of the public from the blog comprises: an emotion index calculating step of calculating an emotion index using an emotional vocabulary dictionary based on vocabularies included in each comment in the blog; an emotional change section extracting step of extracting an emotional change section of the blog using the calculated emotion index; and a keyword extracting step of collecting comments included in the emotional change section and extracting main keywords included in the emotional change section from the collected comments. Therefore, when keywords affecting the emotional change of the public are extracted, the method helps to infer reasons associated with the emotional change of the public in a specific company or social issue to easily identify the needs of the public, thereby creating economic benefits later.
Abstract:
A method for calculating program consumption intensity and a program recommendation method using the same are disclosed. The method for calculating program consumption intensity comprises: a program classification step for dividing a series program (P) produced into a total M (M is a natural number greater than or equal to two) series into N (N is a natural number of 2 to M inclusive) stages and granting a weighting to each stage; a watching confirmation step for confirming m (m is a natural number less than or equal to M) series information which the user has watched in the series program; a weighting calculation step for determining whether each series information confirmed in the watching confirmation step is in any one of the N stages and obtaining each weighting (W(P)); and a computation step for computing the consumption intensity (Cscr(P)) of the user for the program using a consumption intensity equation, wherein the consumption intensity equation is defined as Cscr(P)=∑W(P)×N(P), ∑W(P) is defined as the sum of weightings for each of m series, and N(P) is defined as m/M. Thus, the program consumption intensity of the user can be calculated, thereby recommending an appropriate program. [Reference numerals] (AA) Start; (BB) End; (S110) Program classification step; (S120) Watching confirmation step; (S130) Weighting calculation step; (S140) Computation step
Abstract:
PURPOSE: A system for learning a price bargaining strategy between a user and an IVA(Intelligent Virtual Agent) and a learning method thereof are provided to apply a form of a real market by considering an emotion of a user seeing a price and stocks of the IVA. CONSTITUTION: A module(24) for learning a learning information pattern learns current information once a price decision between an IVA and a user of user terminal is completed. The current information includes a current emotional state about a transaction between the user and the IVA, a current emotional state of the IVA, and stock information. A weighted value measurement module(25) applies a weighted value according to an emotional pattern between the IVA and the user terminal. [Reference numerals] (11) User emotion determining module; (12) User price determining module; (21) IVA emotion determining module; (22) IVA price determining module; (23) Stock determining module; (24) Module(24) for learning a learning information pattern; (25) Weighted value measurement module; (26) Transaction bargaining module; (30) Database