Amplifying Participant Voices Through Text Mining

2020 ◽  
pp. 231-247
Author(s):  
Jonathan S. Lewis
Keyword(s):  
Text Mining ◽  
Thematic Analysis ◽  
Large Scale ◽  
Future Research ◽  
Text Data ◽  
Tremendous Amount ◽  
Mining Methods ◽  
Survey Responses ◽  
Research And Policy ◽  
Lengthy Discussion

Text mining presents an efficient, scalable method to separate signals and noise in large-scale text data, and therefore to effectively analyze open-ended survey responses as well as the tremendous amount of text that students, faculty, and staff produce through their interactions online. Traditional qualitative methods are impractical when working with these data, and text mining methods are consonant with current literature on thematic analysis. This chapter provides a tutorial for researchers new to this method, including a lengthy discussion of preprocessing tasks and knowledge extraction from both supervised and unsupervised activities, potential data sources, and the range of software (both proprietary and open-source) available to them. Examples are provided throughout the paper of text mining at work in two studies involving data collected from college students. Limitations of this method and implications for future research and policy are discussed.

scholarly journals Using Digital Technology to Address Confirmability and Scalability in Thematic Analysis of Participant-Provided Data

2020 ◽  
Author(s):  
Chung Joo Chung ◽  
J. Patrick Biddix ◽  
Han Woo Park
Keyword(s):  
Thematic Analysis ◽  
Large Scale ◽  
Qualitative Data ◽  
Large Data ◽  
Data Sources ◽  
Analysis Method ◽  
Latent Constructs ◽  
Word Analysis ◽  
Survey Responses ◽  
Conceptual Overview

This article presents a technique for analyzing large-scale qualitative data to address considerations for scalability and confirmability in thematic analysis of participant-provided data. A network approach provides a consistent means of coding that scales with the size of the dataset and is verifiable using standardized methods. This form of data analysis can be used with smaller data sources including interview transcripts as well as large data sources such as open-ended survey responses. A constructivist (inductive) approach is maintained and needed, however, to aid in interpretation of latent constructs. In this article, we provide both a conceptual overview of the co-word analysis method and a practical example.

An Analysis on Text Mining Techniques for Smart Literature Review

2021 ◽  
Vol 10 (2) ◽  
pp. 1284-1288
Keyword(s):  
Text Mining ◽  
Review Paper ◽  
Evaluation Criteria ◽  
Unstructured Data ◽  
Future Research ◽  
Literature Analysis ◽  
Research Papers ◽  
Text Data ◽  
Web Technologies ◽  
Analysis Process

With the development of web technologies, databases and social networks etc. a large amount of text data is generated each day. Mostof the data on the internet is in unstructured form. This unstructured data can provide valuable knowledge. For getting valuable knowledge from text data text mining techniques are used widely. As each day large amounts of research papers were published in journals and conferences. These research papers are very valuable for future research and investigations. These research papers act as a source for future innovations. Researchers write review papers to give updated knowledge about the specific field. But review papers used a limited number of papers and involved manually reading each paper. Due to the large volume of research papers published each day, it is not possible for the researchers to go through each paper to find the updated knowledge about their field of interest. To automate the literature analysis process different techniques of text mining were used. This paper provides a review of text mining techniques used in automatic literature analysis. We collected papers in which previous literature is used with text mining techniques to get valuable knowledge. This review paper presented an overview of text mining techniques, their evaluation criteria, their limitations and challenges for exploring literature to find research trends.

scholarly journals Aplikasi Text Mining untuk Klasterisasi Aduan Masyarakat Kota Semarang Menggunakan Algoritma K-means

2021 ◽  
Vol 18 (2) ◽  
pp. 215
Author(s):  
Dita Afida ◽  
Erika Devi Udayanti ◽  
Etika Kartikadarma
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Clustering Algorithm ◽  
City Government ◽  
Community Based ◽  
Text Data ◽  
Government Organizations ◽  
Text Document ◽  
Data Source ◽  
Mining Methods

<p>Social media is a service that is very supportive for government activities, especially in providing openness and community-based government. One form of its implementation is the Semarang City government through the Center for Community Complaints Management (P3M), whose task is to manage community complaints that enter one of the communication channels namely social media twitter. The number of public complaints that enter every day is very varied. This is certainly quite difficult for managers in categorizing complaints reports according to the relevant Local Government Organizations (OPD). This paper focuses on the problem of how to conduct clustering of community complaints. The data source comes from Twitter using the keyword "Laporhendi". Text document data from community complaint tweets was analyzed by text mining methods. A number of pre-processing of text data processing begins with the process of case folding, tokenizing, stemming, stopword removal and word robbering with tf-idf. In conducting cluster mapping, clustering algorithm will be used in dividing the complaint cluster, namely the k-means algorithm. Evaluation of cluster results is done by using purity to determine the accuracy of the results of grouping or clustering.</p>

Mining Text with the Prototype-Matching Method

2011 ◽  
pp. 328-340
Author(s):  
A. Durfee ◽  
A. Visa ◽  
H. Vanharanta ◽  
S. Schneberger ◽  
B. Back
Keyword(s):  
Text Mining ◽  
Future Research ◽  
Text Documents ◽  
Natural Language Text ◽  
Business Applications ◽  
Prototype Matching ◽  
Textual Data ◽  
Mining Methods ◽  
Vast Range ◽  
Language Text

Text documents are the most common means for exchanging formal knowledge among people. Text is a rich medium that can contain a vast range of information, but text can be difficult to decipher automatically. Many organizations have vast repositories of textual data but with few means of automatically mining that text. Text mining methods seek to use an understanding of natural language text to extract information relevant to user needs. This article evaluates a new text mining methodology: prototypematching for text clustering, developed by the authors’ research group. The methodology was applied to four applications: clustering documents based on their abstracts, analyzing financial data, distinguishing authorship, and evaluating multiple translation similarity. The results are discussed in terms of common business applications and possible future research.

scholarly journals Kawaii Culture in Japan: A Bibliometric Analysis and Text Mining Approach Based on Pop-Cultural Diplomacy and Transmission into Global Values

2020 ◽  
Vol 3 (4) ◽  
pp. 3990-4004
Author(s):  
Hiroko Oe ◽  
Max Weeks
Keyword(s):  
Text Mining ◽  
Bibliometric Analysis ◽  
Cultural Diplomacy ◽  
Pop Culture ◽  
Future Research ◽  
Research Topics ◽  
Text Data ◽  
Key Dimensions ◽  
Global Values ◽  
Cultural Experts

This research aims to develop a discussion framework for Kawaii cultural study based on a bibliometric analysis and text mining approach. First, a bibliometric analysis is conducted on literature pertaining to ‘Kawaii and Japanese pop culture’ extracted from the academic database; from this standpoint, the current research topics in the field of Kawaii study are discussed. Second, we aim to provide direction for future research by mining the text data disseminated by three special exhibitions launched by Japanese museums on the theme of ‘Japanese Kawaii culture’ and planned by Kawaii cultural experts and curators. From the results of these two studies, the present research develops a discussion framework containing key dimensions and factors for researchers in this field of study.

scholarly journals Text‑Mining in Streams of Textual Data Using Time Series Applied to Stock Market

2018 ◽  
Vol 66 (6) ◽  
pp. 1573-1580
Author(s):  
Pavel Netolický ◽  
Jonáš Petrovský ◽  
František Dařena
Keyword(s):  
Time Series ◽  
Text Mining ◽  
Classification Accuracy ◽  
Time Windows ◽  
Stock Index ◽  
Text Data ◽  
Textual Data ◽  
Mining Methods ◽  
Index Value ◽  
Value Changes

Each day, a lot of text data is generated. This data comes from various sources and may contain valuable information. In this article, we use text mining methods to discover if there is a connection between news articles and changes of the S&P 500 stock index. The index values and documents were divided into time windows according to the direction of the index value changes. We achieved a classification accuracy of 65–74 %.

scholarly journals Authentication and Authorization for Mobile IoT Devices Using Biofeatures: Recent Advances and Future Trends

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Mohamed Amine Ferrag ◽  
Leandros Maglaras ◽  
Abdelouahid Derhab
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Future Research ◽  
Future Trends ◽  
Different Types ◽  
Authentication And Authorization ◽  
Authentication Schemes ◽  
Mining Methods ◽  
Iot Devices ◽  
Biometric Based Authentication

Biofeatures are fast becoming a key tool to authenticate the IoT devices; in this sense, the purpose of this investigation is to summarise the factors that hinder biometrics models’ development and deployment on a large scale, including human physiological (e.g., face, eyes, fingerprints-palm, or electrocardiogram) and behavioral features (e.g., signature, voice, gait, or keystroke). The different machine learning and data mining methods used by authentication and authorization schemes for mobile IoT devices are provided. Threat models and countermeasures used by biometrics-based authentication schemes for mobile IoT devices are also presented. More specifically, we analyze the state of the art of the existing biometric-based authentication schemes for IoT devices. Based on the current taxonomy, we conclude our paper with different types of challenges for future research efforts in biometrics-based authentication schemes for IoT devices.

Change of Attitude, Technology and Practice: Identifying the Change for Increased Value Creation with Customer Co-creation

2017 ◽  
Vol 5 (1) ◽  
pp. 70-82
Author(s):  
Soumi Paul ◽  
Paola Peretti ◽  
Saroj Kumar Datta
Keyword(s):  
Social Media ◽  
New Product Development ◽  
Value Creation ◽  
Large Scale ◽  
Customer Orientation ◽  
Customer Relationships ◽  
Future Research ◽  
Management Competencies ◽  
Business Decisions ◽  
Prime Concern

Building customer relationships and customer equity is the prime concern in today’s business decisions. The emergence of internet, especially social media like Facebook and Twitter, changed traditional marketing thought to a great extent. The importance of customer orientation is reflected in the axiom, “The customer is the king”. A good number of organizations are engaging customers in their new product development activities via social media platforms. Co-creation, a new perspective in which customers are active co-creators of the products they buy and use, is currently challenging the traditional paradigm. The concept of co-creation involving the customer’s knowledge, creativity and judgment to generate value is considered not only an upcoming trend that introduces new products or services but also fitting their need and increasing value for money. Knowledge and innovation are inseparable. Knowledge management competencies and capacities are essential to any organization that aspires to be distinguished and innovative. The present work is an attempt to identify the change in value creation procedure along with one area of business, where co-creation can return significant dividends. It is on extending the brand or brand category through brand extension or line extension. This article, through an in depth literature review analysis, identifies the changes in every perspective of this paradigm shift and it presents a conceptual model of company-customer-brand-based co-creation activity via social media. The main objective is offering an agenda for future research of this emerging trend and ensuring the way to move from theory to practice. The paper acts as a proposal; it allows the organization to go for this change in a large scale and obtain early feedback on the idea presented. 

Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

2020 ◽  
Vol 13 (7) ◽  
pp. 969-979
Author(s):  
Xu Pei-Zhen ◽  
Lu Yong-Geng ◽  
Cao Xi-Min
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Induction Generator ◽  
Future Research ◽  
Stable Operation ◽  
Subsynchronous Oscillation ◽  
Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

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