scholarly journals Accurate Digital Marketing Communication Based on Intelligent Data Analysis

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
ZhuoJun Li
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Bayesian Optimization ◽  
Digital Marketing ◽  
Marketing Communication ◽  
Intelligent Algorithm ◽  
The Social ◽  
Optimization Parameters ◽  
Technological Means

In digital marketing, the core advantages of scientific and technological means such as artificial intelligence and big data analysis gradually appear and pay attention to them. This paper studies the accuracy of digital marketing and proposes an intelligent algorithm based on data analysis, which improves the effect of marketing communication. Through the combination of intelligent algorithms and big data analysis, the data are convincing. Through the comparison and improvement of intelligent algorithm logistic regression and XGBoost, this paper puts forward an improved algorithm of XGBoost based on Bayesian optimization parameters, which can improve the efficiency of digital marketing communication and enhance the social influence of digital marketing.

Genre as Ideological Impulse: Reflections on Big Data and African Cultural Production

2017 ◽  
Vol 4 (3) ◽  
pp. 409-423
Author(s):  
Matthew H. Brown
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Cultural Production ◽  
Big Data Analysis ◽  
Focus Attention ◽  
Social Nature ◽  
The Social

This article draws from “big-data” analysis of Netflix’s usage, which suggests that what spectators tend to like about films is inherently generic. Moreover, the process of liking serves as a metaphor, over and above the process of taking pleasure, for the ways in which spectators make texts meaningful rather than deriving meaning from them. The article then discusses some examples of African cultural production in order to focus attention on the category of analysis at stake in theorizing genre—a discussion that helps to distinguish genre’s thematic ontology from its material, formal, and stylistic features. Finally, at the intersection of spectator agency and theme, genre appears to be an “ideological impulse,” a way of relating to and encoding experience that begins with people and that they distribute over texts. This way of understanding genre, the article argues, may help scholars write more productively about the social nature of the concept.

scholarly journals Social Emotional Opinion Decision with Newly Coined Words and Emoticon Polarity of Social Networks Services

2019 ◽  
Vol 11 (8) ◽  
pp. 165 ◽  
Author(s):  
Jin Sol Yang ◽  
Myung-Sook Ko ◽  
Kwang Sik Chung
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Threshold Value ◽  
Big Data Analysis ◽  
Network Services ◽  
Social Big Data ◽  
The Social ◽  
Social Network Services ◽  
The Government ◽  
Social Opinions

Nowadays, based on mobile devices and internet, social network services (SNS) are common trends to everyone. Social opinions as public opinions are very important to the government, company, and a person. Analysis and decision of social polarity of SNS about social happenings, political issues and government policies, or commercial products is very critical to the government, company, and a person. Newly coined words and emoticons on SNS are created every day. Specifically, emoticons are made and sold by a person or companies. Newly coined words are mostly made and used by various kinds of communities. The SNS big data mainly consist of normal text with newly coined words and emoticons so that newly coined words and emoticons analysis is very important to understand the social and public opinions. Social big data is informally made and unstructured, and on social network services, many kinds of newly coined words and various emoticons are made anonymously and unintentionally by people and companies. In the analysis of social data, newly coined words and emoticons limit the guarantee the accuracy of analysis. The newly coined words implicitly contain the social opinions and trends of people. The emotional states of people significantly are expressed by emoticons. Although the newly coined words and emoticons are an important part of the social opinion analysis, they are excluded from the emotional dictionary and social big data analysis. In this research, newly coined words and emoticons are extracted from the raw Twitter’s twit messages and analyzed and included in a pre-built dictionary with the polarity and weight of the newly coined words and emoticons. The polarity and weight are calculated for emotional classification. The proposed emotional classification algorithm calculates the weight of polarity (positive or negative) and results in total polarity weight of social opinion. If the total polarity weight of social opinion is more than the pre-fixed threshold value, the twit message is decided as positive. If it is less than the pre-fixed threshold value, the twit message is decided as negative and the other values mean neutral opinion. The accuracy of the social big data analysis result is improved by quantifying and analyzing emoticons and newly coined words.

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