Audience-based optimization of communication media

Abstract

Introduced here are communication optimization platforms configured to improve comprehension, persuasion, or clarity of communications. Initially, a communication optimization platform can acquire input sample(s) that are associated with a source audience. The communication optimization platform can then create a linguistic profile for the source audience by examining the content of the input sample(s). Additionally or alternatively, the communication optimization platform may produce a psychographic profile that specifies various characteristics of the source audience, such as personality, opinions, attitudes, interests, etc. The communication optimization platform can then generate, based on the linguistic profile and/or the psychographic profile, affinity language for communicating with a target audience. By incorporating the affinity language into communications, the communication optimization platform can increase appeal to the target audience.

What is claimed is: 
     
         1 . A method implemented by a computer program executing on a computing device for improving comprehension, persuasion, or clarity of written communications, the method comprising:
 acquiring multiple input samples corresponding to a source audience from at least one source;   for each input sample of the multiple input samples, identifying a linguistic feature by performing one or more natural language processing techniques;   producing a linguistic model based on the multiple linguistic features identified for the multiple input samples;   generating affinity language based on the linguistic model;   incorporating the affinity language into a written communication; and   presenting the written communication on a media channel.   
     
     
         2 . A method implemented by a computer program executing on a computing device, the method comprising:
 acquiring multiple input samples that are associated with a source audience,
 wherein each input sample of the multiple input samples includes text that is representative of one or more words written or spoken by a corresponding member of the source audience; 
   identifying a linguistic feature for each input sample of the multiple input samples by performing natural language processing of the corresponding text, so as to identify multiple linguistic features;   producing a linguistic model for the source audience based on the multiple linguistic features identified for the multiple input samples;   generating, based on the linguistic model, affinity language for communicating with an individual that is determined to share a characteristic in common with the source audience; and   causing the affinity language to be presented on an interface for review by the individual.   
     
     
         3 . The method of  claim 2 , wherein said acquiring comprises:
 interfacing with at least one source from which the multiple input samples are acquired.   
     
     
         4 . The method of  claim 3 , wherein the at least one source includes
 (i) a network-accessible database,   (ii) computer programs executing on computing devices that are associated with members of the source audience, or   (iii) Internet cookies executing on computing devices that are associated with members of the source audience.   
     
     
         5 . The method of  claim 2 , wherein the individual is a member of the source audience. 
     
     
         6 . The method of  claim 2 , wherein each linguistic feature of the multiple linguistic features is indicative of syntax, grammar, or terminology of the text included in the corresponding input sample of the multiple input samples. 
     
     
         7 . The method of  claim 2 , further comprising:
 determining whether the affinity language was incorporated into a written communication by the individual; and   in response to a determination that the affinity language was not incorporated into the written communication by the individual,
 causing additional language generated based on the linguistic model to be presented on the interface for review by the individual. 
   
     
     
         8 . A method implemented by a computer program executing on a computing device for improving comprehension, persuasion, or clarity of communications, the method comprising:
 acquiring multiple input samples corresponding to a source audience;   for each input sample of the multiple input samples, identifying a linguistic feature by performing one or more natural language processing techniques;   producing a linguistic model based on the multiple linguistic features identified for the multiple input samples;   producing a psychographic model based on the multiple linguistic features identified for the multiple input samples;   generating affinity language based on the linguistic model and the psychographic model;   incorporating the affinity language into a communication; and   causing the communication to be presented on a media channel to a target audience.   
     
     
         9 . The method of  claim 8 , wherein the source audience and the target audience at least partially overlap. 
     
     
         10 . The method of  claim 8 , wherein said acquiring comprises extracting or downloading each input sample of the multiple input samples from an Internet-based resource accessible to the source audience that is representative of a collection of students. 
     
     
         11 . The method of  claim 10 , wherein the affinity language is representative of feedback provided to the target audience that is representative of at least one student. 
     
     
         12 . A non-transitory computer-readable medium with instructions stored thereon that, when executed by a processor, cause the processor to perform operations comprising:
 acquiring content corresponding to a source audience;   examining the content to identify at least one linguistic feature by performing a natural language processing operation;   producing, based on the at least one linguistic feature,
 (i) a linguistic model that specifies a linguistic characteristic of the source audience, and 
 (ii) a psychographic model that specifies a psychographic characteristic of the source audience; 
   generating affinity language based on the linguistic model and the psychographic model;   incorporating at least some of the affinity language into a communication to be presented to a target audience that shares a characteristic in common with the source audience;   determining that a presence of the affinity language in the communication is likely to increase a likelihood of eliciting a particular response from the target audience upon presentation of the communication; and   causing presentation of the communication intended for the target audience.   
     
     
         13 . The non-transitory computer-readable medium of  claim 12 , wherein the linguistic characteristic is representative of a language pattern of the source audience. 
     
     
         14 . The non-transitory computer-readable medium of  claim 13 , wherein the psychographic characteristic is representative of a psychographic attribute that is determined to correlate to the language pattern of the source audience. 
     
     
         15 . The non-transitory computer-readable medium of  claim 14 , wherein the psychographic attribute is indicative of a level of extraversion, emotional stability, agreeableness, conscientiousness, or openness. 
     
     
         16 . The non-transitory computer-readable medium of  claim 14 , wherein the psychographic attribute is determined to correlate to the language pattern of the source audience through the use of a rule set that maps different language patterns to different psychographic attributes. 
     
     
         17 . A non-transitory computer-readable medium with instructions stored thereon that, when executed by a processor, cause the processor to perform operations comprising:
 acquiring content corresponding to a source audience;   examining the content to identify at least one linguistic feature by performing a natural language processing operation;   producing, based on the at least one linguistic feature,
 (i) a linguistic model that specifies a linguistic characteristic of the source audience, and 
 (ii) a psychographic model that specifies a psychographic characteristic of the source audience; 
   generating affinity language based on the linguistic model and the psychographic model;   incorporating at least some of the affinity language into a communication to be presented to a target audience that shares a characteristic in common with the source audience;   causing presentation of the communication intended for the target audience; and   determining a response from the target audience based on presentation of the communication.   
     
     
         18 . A method for suggesting written content for inclusion by an author in a written communication, the method comprising:
 acquiring multiple samples of past written content that correspond to a past interval of time;   acquiring contextual information related to the multiple samples of past written content;   creating a language model based on the multiple samples of past written content and the contextual information;   analyzing the multiple samples of past written content using one or more natural language processing techniques, so as to identify at least one linguistic feature of the multiple samples of past written content;   producing affinity language based on (i) the language model or (ii) the at least one linguistic feature; and   suggesting, based on the affinity language, written content to the author in real time by presenting the written content for review by the author as the author writes on a media channel.   
     
     
         19 . The method of  claim 18 , wherein the multiple samples of past written content are prepared by the author. 
     
     
         20 . A method for producing a communication for a target individual, the method comprising:
 acquiring multiple communication samples, at least one sample being from a source individual and at least one sample being from the target individual for whom the communication is to be produced;   acquiring contextual information related to the multiple communication samples;   creating a language model based on the multiple communication samples and the contextual information;   analyzing the multiple communication samples and the contextual information using one or more natural language processing techniques;   producing affinity language based on an outcome of said analyzing;   incorporating at least some of the affinity language into a communication to be presented to the target individual; and   presenting the communication on a media channel to the target individual.   
     
     
         21 . The method of  claim 20 , further comprising:
 identifying the target individual for whom the communication is to be produced, based on a determination that the source individual and the target individual share a characteristic in common.   
     
     
         22 . The method of  claim 21 , wherein the characteristic is age, gender, socioeconomic status, or geographical location.