Analysis of Social Value of TV Dramas Based on Audience Comments

2020 ◽  
pp. 69-88
Author(s):  
Xinye Liu ◽  
Xiaotong Zhang ◽  
Tao Wang ◽  
Kun Cheng ◽  
Shangbing Jiao ◽  
...  
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Design Methodology ◽  
Algorithm Design ◽  
Real Life ◽  
Social Value ◽  
Random Forest Algorithm ◽  
Two Dimension ◽  
The Social

This chapter analyzes the social value of the TV drama Entrepreneurial Age through the mining of the audience's comments, so as to provide reference for the TV drama producers in topic selection, casting, and script design. Design/methodology/approach: The research is based on a three-step approach including data crawling, two-dimension data tags, and the random forest algorithm design. Findings: This chapter finds that there are three factors related to demand of TV drama:1) the appearance and acting skill of actors; 2) the closeness between TV plays and real life; 3) whether the topic of TV plays has high attention. Value: Based on the big data of audience comments, this chapter explores the factors that influence the number of TV plays. It provides an important reference for TV drama producers on how to design the plot of TV drama, how to choose actors, and how to create topics.

scholarly journals A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment

2017 ◽  
Vol 28 (4) ◽  
pp. 919-933 ◽  
Author(s):  
Jianguo Chen ◽  
Kenli Li ◽  
Zhuo Tang ◽  
Kashif Bilal ◽  
Shui Yu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Random Forest ◽  
Random Forest Algorithm ◽  
Computing Environment ◽  
Cloud Computing Environment

scholarly journals Enhanced Tree Based Real Time Intrusion Detection System in Big Data

2016 ◽  
Vol 15 (3) ◽  
pp. 6563-6569
Author(s):  
S.J.SATHISH AARON JOSEPH ◽  
R. BALASUBRAMANIAN
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Training Data ◽  
Random Forest Algorithm ◽  
Digital Form ◽  
Hadoop Cluster

Intrusion detection is one of the major necessities of the current networked environment, where every information is available in its corresponding digital form. This paper presents an enhanced tree based approach that can be used to perform intrusion detection faster and with better accuracy. The training data is subject to the random forest algorithm. This algorithm is a combination of tree predictors, and each tree depends upon the random vector generated. Spark based implementations of the Random Forest algorithm is used in a Hadoop cluster on datasets with varied imbalance to obtain the results. It has been observed that the classifier provided results in real time with an accuracy >90%, hence is more appropriate for online intrusion detection.

scholarly journals Hamilton's Kirikiriroa Gully: Engaging with waterway social life from the ground up

2021 ◽  
Author(s):  
◽  
May Jan MacIntyre
Keyword(s):  
Social Life ◽  
Design Methodology ◽  
Urban Expansion ◽  
Social Value ◽  
Limited Resources ◽  
Top Down ◽  
The Social ◽  
Social Potential ◽  
Large Project ◽  
Suburban Development

<p>The social value of waterways and gullies in new suburban development is something that is often overlooked and given limited resources to be developed. They have the potential to be intense centres of neighbourhoods and provide much needed social relief in the age of rapid urban expansion. This thesis explores the social potential of Kirikiriroa gully in Hamilton where suburban development has occurred at an alarming rate. The research extends the traditional top down masterplan design methodology used for large project sites by investigating the reverse of this, a study of life on the ground to inform the design.  Using on-site analytical and design methods, the design attempts to reveal the workings of the landscape in a way that a masterplan cannot. Key to this was the identification of three important social experiential typologies within the gully system. The understanding and documentation of the relations and forces that produced these types facilitated adjustments to strategically identified sites, with the intention of intensifying the relevant social ecology of the gully at that site. This intensification is intended to influence the wider neighbourhoods and the gully system more broadly.</p>

Decomposing the value of department store shopping into utilitarian, hedonic and social dimensions

2006 ◽  
Vol 34 (1) ◽  
pp. 6-24 ◽  
Author(s):  
Timo Rintamäki ◽  
Antti Kanto ◽  
Hannu Kuusela ◽  
Mark T. Spence
Keyword(s):  
Customer Value ◽  
Design Methodology ◽  
Department Store ◽  
Social Value ◽  
Social Dimension ◽  
Social Dimensions ◽  
Content Type ◽  
Retail Markets ◽  
Hedonic Value ◽  
The Social

PurposeThe purpose of this paper is to decompose total customer value as perceived by department store shoppers into utilitarian, hedonic and social dimensions, and empirically test this conceptualization in a Finnish department store shopping context.Design/methodology/approachData were collected by a questionnaire administered over three days at a department store that generates the second largest turnover in Finland. A total of 364 shoppers completed the questionnaire.FindingsEmpirical evidence supports our tripartite conceptualization of total customer value. In particular, social value is an independent construct. Further, social value varies by day‐of‐week, with a significant increase on Saturday (versus weekdays) when the store is more crowded, whereas no such differences in utilitarian and hedonic values were detected.Originality/valueThe principal contribution is a tripartite conceptualization of total customer value that incorporates utilitarian, social and hedonic value dimensions in a department store shopping context. Individually these dimensions are all well rooted in streams of consumer behavior literature, albeit mostly at the product or brand, not the store, level. Increasing our understanding of these softer aspects of shopping, particularly the social dimension, is important because they represent possible differentiating factors in the highly competitive and often commoditized retail markets.

scholarly journals Application of machine learning algorithms in MBR simulation under big data platform

2020 ◽  
Vol 15 (4) ◽  
pp. 1238-1247
Author(s):  
Weiwei Li ◽  
Chunqing Li ◽  
Tao Wang
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Membrane Fouling ◽  
Sewage Treatment ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
Membrane Flux ◽  
Data Platform

Abstract Membrane bioreactors (MBRs) are a sewage treatment process that combines membrane separation with bioreactor technology. It has great advantages in sewage treatment. Membrane fouling hinders MBR process development, however. Studies have shown that the degree of membrane fouling can be judged using the membrane flux rate. In this study, principal component analysis was used to extract the main factors affecting membrane fouling, then the random forest algorithm on the Hadoop big data platform was used to establish an MBR membrane flux prediction model, which was tested. In order to verify the model's effectiveness, BP neural network and SVM support vector machine models were established using the same experimental data. The experimental results from the different models were compared, and the results showed that the random forest algorithm gave the best MBR membrane flux predictions.

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