Decoding Digital Consumer Feedback: Customer Intelligence Insights Through Unstructured Data Mining

2018 ◽  
pp. 113-120
Author(s):  
Supriya Tandon ◽  
Aswinraj Govindaraj
Keyword(s):  
Data Mining ◽  
Unstructured Data ◽  
Consumer Feedback ◽  
Customer Intelligence

Data Mining Techniques and Applications

2018 ◽  
Vol 9 (1) ◽  
pp. 78-97 ◽  
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Unstructured Data ◽  
The Internet ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Extant Literature ◽  
Business Environments ◽  
Data Volume ◽  
Complete Set

Data mining has been gaining attention with the complex business environments, as a rapid increase of data volume and the ubiquitous nature of data in this age of the internet and social media. Organizations are interested in making informed decisions with a complete set of data including structured and unstructured data that originate both internally and externally. Different data mining techniques have evolved over the last two decades. To solve a wide variety of business problems, different data mining techniques are developed. Practitioners and researchers in industry and academia continuously develop and experiment varieties of data mining techniques. This article provides an overview of data mining techniques that are widely used in different fields to discover knowledge and solve business problems. This article provides an update on data mining techniques based on extant literature as of 2018. That might help practitioners and researchers to have a holistic view of data mining techniques.

Privacy Preserving Data Mining on Unstructured Data

2017 ◽  
pp. 167-190
Author(s):  
Trupti Vishwambhar Kenekar ◽  
Ajay R. Dani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Structure Data ◽  
Data Privacy ◽  
Differential Privacy ◽  
Unstructured Data ◽  
Map Reduce ◽  
Individual Data ◽  
Data Set ◽  
Privacy Preserving Data Mining

As Big Data is group of structured, unstructured and semi-structure data collected from various sources, it is important to mine and provide privacy to individual data. Differential Privacy is one the best measure which provides strong privacy guarantee. The chapter proposed differentially private frequent item set mining using map reduce requires less time for privately mining large dataset. The chapter discussed problem of preserving data privacy, different challenges to preserving data privacy in big data environment, Data privacy techniques and their applications to unstructured data. The analyses of experimental results on structured and unstructured data set are also presented.

Data Mining

2011 ◽  
pp. 358-365
Author(s):  
Mark Last
Keyword(s):  
Data Mining ◽  
Homeland Security ◽  
Automatic Analysis ◽  
Unstructured Data ◽  
Computational Techniques ◽  
Cyber Warfare ◽  
Behavioral Patterns

Data mining is a growing collection of computational techniques for automatic analysis of structured, semi-structured, and unstructured data with the purpose of identifying important trends and previously unknown behavioral patterns. Data mining is widely recognized as the most important and central technology for homeland security in general and for cyber warfare in particular

Big Data Mining

2016 ◽  
pp. 53-59 ◽  
Author(s):  
N. G. Bhuvaneswari Amma
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Social Networking ◽  
Unstructured Data ◽  
Science And Engineering ◽  
Business World ◽  
Volume Velocity ◽  
Big Data Mining ◽  
Traditional Processing ◽  
Processing Techniques

Big data is a term used to describe very large amount of structured, semi-structured and unstructured data that is difficult to process using the traditional processing techniques. It is now expanding in all science and engineering domains. The key attributes of big data are volume, velocity, variety, validity, veracity, value, and visibility. In today's world, everyone is using social networking applications like Facebook, Twitter, YouTube, etc. These applications allow the users to create the contents for free of cost and it becomes huge volume of web data. These data are important in the competitive business world for making decisions. In this context, big data mining plays a major role which is different from the traditional data mining. The process of extracting useful information from large datasets or streams of data, due to its volume, velocity, variety, validity, veracity, value and visibility is termed as Big Data Mining.

Augmented Reality and Mobile Consumers

2020 ◽  
pp. 76-94
Author(s):  
Zehra Nur Canbolat ◽  
Fatih Pinarbasi
Keyword(s):  
Data Mining ◽  
Augmented Reality ◽  
Text Mining ◽  
Mobile Application ◽  
Unstructured Data ◽  
Consumer Evaluations ◽  
Theoretical Contribution ◽  
Mining Methods ◽  
The Uk ◽  
Product Perception

In this chapter, consumer perceptions of augmented reality mobile applications will be emphasized and the analysis will be carried out through the mobile application markets of two different countries. In the research, the top 20 applications were selected from the UK and USA mobile application markets and the last consumer evaluations regarding these applications were obtained. In accordance with the purpose of the research, text mining methods were used to evaluate the expressions of consumers, since data mining methodologies can contribute to a better understanding of unstructured data. In the research, top words, bigram, and trigram are used in consumer comments. Then sentiment analysis method is employed to determine the emotions in consumer comments. Authors conclude that both markets have positive polarities. While the study provides a theoretical contribution in terms of consumer evaluations and new product perception, it also contributes to the sector in terms of expressions and evaluations used by consumers.

Intelligent Bilingual Data Extraction and Rebuilding Using Data Mining for Big Data

2020 ◽  
Vol 17 (1) ◽  
pp. 513-518
Author(s):  
Shashi Pal Singh ◽  
Ajai Kumar ◽  
Rachna Awasthi ◽  
Neetu Yadav ◽  
Shikha Jain
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Parallel Processing ◽  
Data Extraction ◽  
Structured Data ◽  
Unstructured Data ◽  
File Format ◽  
Structure Form ◽  
Text Extraction ◽  
File Formats

In today’s World there exists various source of data in various formats (file formats), different structure, different types and etc. which is a hug collection of unstructured over the internet or social media. This gives rise to categorization of data as unstructured, semi structured and structured data. Data that exist in irregular manner without any particular schema are referred as unstructured data which is very difficult to process as it consists of irregularities and ambiguities. So, we are focused on Intelligent Processing Unit which converts unstructured big data into intelligent meaningful information. Intelligent text extraction is a technique that automatically identifies and extracts text from file format. The system consists of different stages which include the pre-processing, keyphase extraction techniques and transformation for the text extraction and retrieve structured data from unstructured data. The system consists multiple method/approach give better result. We are currently working in various file formats and converting the file format into DOCX which will come in the form of the un-structure Form, and then we will obtain that file in the structure form with the help of intelligent Pre-processing. The pre-process stages that triggers the unstructured data/corpus into structured data converting into meaning full. The Initial stage is the system remove the stop word, unwanted symbols noisy data and line spacing. The second stage is Data Extraction from various sources of file or types of files into proper format plain text. The then in third stage we transform the data or information from one format to another for the user to understand the data. The final step is rebuilding the file in its original format maintaining tag of the files. The large size files are divided into sub small size file to executed the parallel processing algorithms for fast processing of larger files and data. Parallel processing is a very important concept for text extraction and with its help; the big file breaks in a small file and improves the result. Extraction of data is done in Bilingual language, and represent the most relevant information contained in the document. Key-phase extraction is an important problem of data mining, Knowledge retrieval and natural speech processing. Keyword Extraction technique has been used to abstract keywords that exclusively recognize a document. Rebuilding is an important part of this project and we will use the entire concept in that file format and in the last, we need the same format which we have done in that file. This concept is being widely used but not much work of the work has been done in the area of developing many functionalities under one tool, so this makes us feel the requirement of such a tool which can easily and efficiently convert unstructured files into structured one.

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