scholarly journals The disruptometer: an artificial intelligence algorithm for market insights

2019 ◽  
Vol 8 (2) ◽  
pp. 727-734
Author(s):  
Mimi Aminah binti Wan Nordin ◽  
Dmitry Vedenyapin ◽  
Muhammad Fahreza Alghifari ◽  
Teddy Surya Gunawan
Keyword(s):  
Customer Value ◽  
Market Research ◽  
Venture Capitalist ◽  
Value Proposition ◽  
Problem Space ◽  
Social Media Data ◽  
Portfolio Company ◽  
Different Types ◽  
Artificial Intelligence Algorithm ◽  
Media Data

Social media data mining is rapidly developing to be a mainstream tool for marketing insights in today’s world, due to the abundance of data and often freely accessed information. In this paper, we propose a framework for market research purposes called the Disruptometer. The algorithm uses keywords to provide different types of market insights from data crawling. The preliminary algorithm data-mines information from Twitter and outputs 2 parameters-Product-to-Market Fit and Disruption Quotient, which is obtained from a brand’s customer value proposition, problem space, and incumbent space. The algorithm has been tested with a venture capitalist portfolio company and market research firm to show high correlated results. Out of 4 brand use cases, 3 obtained identical results with the analysts ‘studies.

scholarly journals Perceiving Residents’ Festival Activities Based on Social Media Data: A Case Study in Beijing, China

2021 ◽  
Vol 10 (7) ◽  
pp. 474
Author(s):  
Bingqing Wang ◽  
Bin Meng ◽  
Juan Wang ◽  
Siyu Chen ◽  
Jian Liu
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Topic Model ◽  
Central Area ◽  
Classification Model ◽  
Social Media Data ◽  
Ring Road ◽  
Different Types ◽  
Spatial Differences ◽  
Media Data

Social media data contains real-time expressed information, including text and geographical location. As a new data source for crowd behavior research in the era of big data, it can reflect some aspects of the behavior of residents. In this study, a text classification model based on the BERT and Transformers framework was constructed, which was used to classify and extract more than 210,000 residents’ festival activities based on the 1.13 million Sina Weibo (Chinese “Twitter”) data collected from Beijing in 2019 data. On this basis, word frequency statistics, part-of-speech analysis, topic model, sentiment analysis and other methods were used to perceive different types of festival activities and quantitatively analyze the spatial differences of different types of festivals. The results show that traditional culture significantly influences residents’ festivals, reflecting residents’ motivation to participate in festivals and how residents participate in festivals and express their emotions. There are apparent spatial differences among residents in participating in festival activities. The main festival activities are distributed in the central area within the Fifth Ring Road in Beijing. In contrast, expressing feelings during the festival is mainly distributed outside the Fifth Ring Road in Beijing. The research integrates natural language processing technology, topic model analysis, spatial statistical analysis, and other technologies. It can also broaden the application field of social media data, especially text data, which provides a new research paradigm for studying residents’ festival activities and adds residents’ perception of the festival. The research results provide a basis for the design and management of the Chinese festival system.

scholarly journals Sentimental analysis on social media data using R programming

2018 ◽  
Vol 7 (2.31) ◽  
pp. 80 ◽  
Author(s):  
Mandava Geetha Bhargava ◽  
Duvvada Rajeswara Rao
Keyword(s):  
Social Media ◽  
Research Field ◽  
Virtual Currency ◽  
Ongoing Research ◽  
Social Media Data ◽  
Digital Currency ◽  
Alternative Currency ◽  
Different Types ◽  
R Programming ◽  
Media Data

Sentimental Analysis is an ongoing research field in Text Mining Arena to determine the situation of market on particular entity such as Product, Services...Etc. and it can be called as computational treatment of reviews, subjectivity and sentiment of text. Cryptocurrency can be explained as a type of digital estate and devised to mechanize as a form of trade and exchanges that uses cryptography as an encryption technique to secure the transactions and acts as decentralized controlled transaction which is opposed to centralized transactions. Cryptocurrency are a type of virtual currency, digital currency and alternative currency, On basis of categorical, there are different architecture and security protocols which are used in the cryptocurrencies to secure transactions, the different types of cryptocurrency are available in the market such as Bitcoin, Litecoin, and Namecoin…etc. This paper focuses on survey on different types of sentimental analysis methods and main contribution of this paper include sentimental analysis of  social media data on different types of cryptocurrencies on basis of categorical and different terms of cryptocurrency such as Cryptocurrency, virtual currency, digital currency and discussed on trends of crypto currency in present market.  

Brand value proposition for bank customers in India

2017 ◽  
Vol 35 (1) ◽  
pp. 24-44 ◽  
Author(s):  
Y.L.R. Moorthi ◽  
Bijuna C. Mohan
Keyword(s):  
Customer Value ◽  
Design Methodology ◽  
Foreign Banks ◽  
Value Proposition ◽  
Content Type ◽  
Public Sector Banks ◽  
Target Segment ◽  
Different Types ◽  
The Relationship ◽  
Practical Implications

Purpose The purpose of this paper is to relate the customer value proposition offered by a bank with its structure of ownership. Design/methodology/approach The study adopted a combination of exploratory and descriptive approaches. The attitudes and opinions of bank customers were gauged through a survey. Based on literature, a pool of items was identified to measure the construct of value proposition. It was hypothesized that different types of banks in India are chosen for different benefits offered by them. The relationship between value proposition and its constituent variables functional, emotional and self-expressive benefits was analyzed using multiple regression. Findings Results prove that while self-expressive benefits drive the choice of foreign banks (FBs), functional benefits are important for all types of banks. Research limitations/implications The research intends to study only the perceptions of customers having an account in Indian public sector banks, private sector banks or FBs. Practical implications The study helps to relate the type of bank (public, private or foreign) a customer chooses, with the value proposition it offers. Using this study, banks can configure the value proposition that is appropriate for their target segment. Originality/value The paper examines the value proposition offered by the three different types of banks (public, private and foreign) empirically. It links bank choice of the customer to the benefit assortment offered by different types of banks.

scholarly journals Social Media Data Misuse

2021 ◽  
Author(s):  
Tariq Soussan ◽  
Marcello Trovati
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Modern Method ◽  
High Tech ◽  
Social Media Data ◽  
Ethical Concerns ◽  
Different Types ◽  
Social Media Platforms ◽  
Innovative Enterprise ◽  
Media Data

The present high-tech landscape has allowed institutes to undergo digital transformation in addition to the storing of exceptional bulks of information from several resources, such as mobile phones, debit cards, GPS, transactions, online logs, and e-records. With the growth of technology, big data has grown to be a huge resource for several corporations that helped in encouraging enhanced strategies and innovative enterprise prospects. This advancement has also offered the expansion of linkable data resources. One of the famous data sources is social media platforms. Ideas and different types of content are being posted by thousands of people via social networking sites. These sites have provided a modern method for operating companies efficiently. However, some studies showed that social media platforms can be a source for misinformation at which some users tend to misuse social media data. In this work, the ethical concerns and conduct in online communities has been reviewed in order to see how social media data from different platforms has been misused, and to highlight some of the ways to avoid the misuse of social media data.

Social Media Data Misuse

2021 ◽  
Author(s):  
Tariq Soussan ◽  
Marcello Trovati
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Modern Method ◽  
High Tech ◽  
Social Media Data ◽  
Ethical Concerns ◽  
Different Types ◽  
Social Media Platforms ◽  
Innovative Enterprise ◽  
Media Data

The present high-tech landscape has allowed institutes to undergo digital transformation in addition to the storing of exceptional bulks of information from several resources, such as mobile phones, debit cards, GPS, transactions, online logs, and e-records. With the growth of technology, big data has grown to be a huge resource for several corporations that helped in encouraging enhanced strategies and innovative enterprise prospects. This advancement has also offered the expansion of linkable data resources. One of the famous data sources is social media platforms. Ideas and different types of content are being posted by thousands of people via social networking sites. These sites have provided a modern method for operating companies efficiently. However, some studies showed that social media platforms can be a source for misinformation at which some users tend to misuse social media data. In this work, the ethical concerns and conduct in online communities has been reviewed in order to see how social media data from different platforms has been misused, and to highlight some of the ways to avoid the misuse of social media data.

scholarly journals Applied Webscraping in Market Research

2016 ◽  
Author(s):  
Markus Herrmann ◽  
Laura Hoyden
Keyword(s):  
Social Media ◽  
Market Research ◽  
Data Science ◽  
Open Data ◽  
The Internet ◽  
Social Media Data ◽  
Content Classification ◽  
Legal Restrictions ◽  
Website Content ◽  
Media Data

Modern Webscraping tools and APIs facilitate the extraction of information from the Internet significantly, especially if the data is not offered for download in a structured format. In this abstract we outline, that Webscraping, as a common practice to load, prepare and statistically analyze specific structured or unstructured data from the Internet, has become an essential application in Marketing and Data Science. Furthermore, we emphasize the importance of Open Data and social media data as a scraping target and illustrate examples of Open Data and social media data integration, Sentiment Analysis and website content classification as a utilization of Webscraping in a Market Research environment. While we argue that Webscraping of internet data is an enabler and driver of product innovation in Market Research it should also be noted that there are some legal restrictions involved.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

Sign in / Sign up

Export Citation Format

Share Document