Data mining analytics investigate Facebook Live stream users’ behaviors and business models: The evidence from Thailand

2022 ◽  
pp. 100478
Author(s):  
Shu-Hsien Liao ◽  
Retno Widowati ◽  
Pimchanok Puttong
Keyword(s):  
Data Mining ◽  
Business Models

scholarly journals The Appleization of finance: Charting incumbent finance’s embrace of FinTech

2018 ◽  
Vol 4 (2) ◽  
pp. 159-80
Author(s):  
Reijer Hendrikse ◽  
David Bassens ◽  
Michiel Van Meeteren
Keyword(s):  
Data Mining ◽  
Information And Communication Technologies ◽  
Business Model ◽  
Financial Services ◽  
Business Models ◽  
Communication Technologies ◽  
Data Driven ◽  
Financial Industry ◽  
Information And Communication ◽  
Financial Technology

The rise of financial technology (FinTech) engenders novel business models through integrating financial services and information and communication technologies (ICT). Digital currencies and payments, data mining, and other FinTech applications threaten to radically overhaul the financial sector. This article argues that, while we are becoming aware of how technology giants such as Apple Inc. are making inroads into financial services, we need to become more sensitive to how financial incumbents mimick ICT firms while aiming to neutralize the FinTech challenge. Practices from Silicon Valley are spilling over into ‘traditional’ finance through a process we dub Appleization. We illustrate how incumbents aim to remain indispensable amidst rapid digitization. Mimicking tech strategies, financial incumbents resort to transforming legacy ICT systems into integrated platforms, cultivating entrepreneurial ecosystems where startups are ‘free’ to compete whilst effectively being locked into the incumbent's orbit. We illustrate this by comparing Apple’s business features (locking-in developers, customers and state into a hybrid business model based on a synergy between hardware, software and data-driven platform components) with emerging practices in the financial industry. Our analogy suggests that the Appleization of finance might radically transform, yet not undercut the oligopolistic position of financial incumbents.

scholarly journals Identifying Startups Business Opportunities from UGC on Twitter Chatting: An Exploratory Analysis

2021 ◽  
Vol 16 (6) ◽  
pp. 1929-1944
Author(s):  
José Ramón Saura ◽  
Ana Reyes-Menéndez ◽  
Nelson deMatos ◽  
Marisol B. Correia
Keyword(s):  
Data Mining ◽  
Business Models ◽  
Latent Dirichlet Allocation ◽  
Business Ecosystem ◽  
Technology Industry ◽  
Exploratory Approach ◽  
Machine Learning Approach ◽  
Investment Opportunities ◽  
Using Data ◽  
Key Indicators

The startup business ecosystem in India has experienced exponential growth. The amount of investment in Indian startups in the last decade demonstrates the strong interest of the technology industry to these business models based on innovation. In this context, the present study aims to identify investment opportunities for investors in Indian startups by identifying key indicators that characterize the startup ecosystem in India. To this end, a three steps data mining method is developed using data mining techniques. First, a sentiment analysis (SA), a machine learning approach that classifies the topics into groups expressing feelings, is applied to a dataset. Next, we develop a Latent Dirichlet Allocation (LDA) model, a topic-modeling technique that divides the sample of n = 14.531 tweets from Twitter into topics, using user-generated content (UGC) as data. Finally, in order to identify the characteristics of each topic we apply textual analysis (TA) to identify key indicators. The originality of the present study lies in the methodological process used for data analysis. Our results also contribute to the literature on startups. The results demonstrate that the Indian startup ecosystem is influenced by areas such as fintech, innovation, crowdfunding, hardware, funds, competition, artificial intelligence, augmented reality and electronic commerce. Of note, in view of the exploratory approach of the present study, the results and implications should be taken as descriptive, rather than determining for future investments in the Indian startup ecosystem.

scholarly journals Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

2019 ◽  
Vol 11 (3) ◽  
pp. 917 ◽  
Author(s):  
Jose Ramon Saura ◽  
Pedro Palos-Sanchez ◽  
Antonio Grilo
Keyword(s):  
Data Mining ◽  
Sentiment Analysis ◽  
Business Models ◽  
Latent Dirichlet Allocation ◽  
New Technologies ◽  
Business Success ◽  
Key Factors ◽  
Text Data ◽  
Text Data Mining ◽  
The Creation

The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technology-based startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels’ requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator’s and startup’s geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

Post, Mine, and Be Disturbed: Social Media Data Mining

PsycCRITIQUES ◽  
2016 ◽  
Vol 61 (51) ◽  
Author(s):  
Daniel Keyes
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Media Data ◽  
Media Data
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