Data science, intelligence and future analysis
Fariba Karimi; ameneh khadivar; Fatemeh Abbasi
Abstract
In recent years, the rapid growth of virtual space has made people devote more of their time in virtual space, especially to social networks, which can be attributed to the remarkable features of virtual space; including increasing the speed of information exchange, easy and free access to information ...
Read More
In recent years, the rapid growth of virtual space has made people devote more of their time in virtual space, especially to social networks, which can be attributed to the remarkable features of virtual space; including increasing the speed of information exchange, easy and free access to information and variety of knowledge topics. In this regard, the opinions recorded by users in virtual networks have grown day by day and have become very important, and extracting the opinions and feelings of users' opinions for more informed decision-making is of great help to businesses, on the other hand, virtual reality technology in the past few decades It has undergone technical changes and improved immersion and the feeling of remote presence; This technology is used in various fields such as education, tourism, health, sports, entertainment, architecture and construction, etc. The increasing progress of virtual reality technology has caused many businesses to operate in this field, but due to changes Continuous market and the need for timely information, companies should use differentiation and growth strategies, in this regard, they need to ask users' opinions and in line with that, try to grow and improve their business, considering that Users' comments are textual, and reading and summarizing them is time-consuming and difficult. Based on this, the aim of the current research was to categorize comments related to virtual reality technology using machine learning methods and a dictionary-based approach. Therefore, about one million tweets in the field of virtual reality technology were collected by the web crawler, and after data preprocessing, 480,432 samples remained in the data, then Dirichlet's hidden allocation topic modeling was implemented on the data. This modeling separated different topics by examining the distribution of words in tweets; The tweets whose distribution of words were similar were placed into a topic and the number of topics with the highest coherence score was selected, the number of topics 9 had higher coherence and the data were grouped into 9 topics, so once again the Dirichlet hidden allocation modeling was set to 9. The topic was done, with this the tweets were grouped into 9 different topics. To evaluate the model, considering that we had a probability distribution, the confusion criterion was used, the value of which was -9.44, and the coherence score was used for the degree of semantic similarity between words and the distinction between subjects, and the result was 0.47. The lower the confusion criterion and the higher the coherence score, the more efficient the model is. With the help of keyword weights obtained by Dirichlet hidden allocation modeling and examining at least 5 different tweets from each topic, 9 topics related to virtual reality technology were identified: "New Technology", "Creation and Make", "Technological Business", "Education", "Virtual Games", "Progress", "Gadget", "Metaverse", and "Indiegame", the topics were analyzed with the help of several graphs. We found that the number of neutral comments on topics such as "New Technology" and "Metaverse" is more than positive and negative comments, which indicates the lack of sufficient information or the lack of use of these technologies, and it is necessary for businesses in this field, to try more in this regard, in the same way, if we observe the graph of "Virtual Games" and "Technological Business", we can see that it changes almost with the same ratio in different years, in the sense that this The two graphs are related, in fact, businesses should keep in mind that the factors affecting these two issues are the same, but users pay more attention to the issue of "Virtual Games", as a result, if the creators of "Technological Business" Focus specifically on "Virtual Games", they will grow more due to the more attention of users, also the creators of games should consider that "Virtual Games" are a topic of more attention than "Indiegame". Is. In the subjects of "Education" and "Gadget", users lost their attention to these subjects in the field of virtual reality over time, in fact they showed their attention to other subjects, so it is better for businesses that operate in this field to take measures To advertise and attract users or change their user area if there is no growth.
Introduction
Constant changes in the market and the need for timely information force companies to use differentiation and growth strategies appropriate to the needs of customers. (Sánchez, Folgado-Fernández, & Sánchez, 2022). Companies can check and analyze their customers' opinions through microblogging sites (Facebook, Twitter, etc.) and finally improve the desired products or services (Ahmad, Aftab, Bashir, & Hameed, 2018). Today, users express their opinions and feelings and review products in online social networks. Therefore, user comments and the analysis of these comments have become a valuable resource for businesses (Kim et al., 2015; Loureiro et al., 2019).
Virtual reality and augmented reality have undergone technical developments in the past few decades and have improved immersion and the feeling of remote presence. Several examples of applications of such techniques can be found in stores, the tourism industry, hotels, restaurants, etc. (Loureiro, Guerreiro, & Ali, 2020). Due to the constant changes in the market and the need for timely information, companies should use differentiation and growth strategies, nowadays, due to the rapid evolution of the Internet, instead of collecting their opinions through time-consuming and expensive methods such as questionnaires and interviews, etc., they express in the context of social networks, which is very useful for businesses in their development, and they can measure the feelings of customers towards products and services, and understand the needs of users, and finally make appropriate and appropriate decisions in the direction of adopt growth, but in order to use the produced content correctly, text mining and sentiment analysis techniques should be used, which has not been researched in Iran so far. Analysis of users' opinions and feelings about virtual reality technology can help businesses that operate in the field of metaverse, virtual game production, virtual education, virtual tourism, etc., to make better decisions and plans.
Literature Review
Social media generates a large amount of real-time social signals that can provide new insights into human behavior and emotions. People around the world are constantly engaged with social media. (Al-Samarraie, Sarsam, & Alzahrani, 2023).
On the other hand, the amount of data is increasing day by day. Almost all institutions, organizations and business industries store their data electronically. A huge amount of text is circulating on the Internet in the form of digital libraries, repositories, and other textual information such as blogs, social media networks, and emails (Sagayam, Srinivasan, & Roshni, 2012).
Topic modeling is one of the most powerful techniques in text mining for data mining, discovering hidden data and finding relationships between data and textual documents (Jelodar et al., 2017).
The technological advances of the last century have confronted societies with new realities that have indisputably improved daily life, making it more convenient and interesting. In recent decades, technology using virtual reality and wearable devices have had a significant impact in the fields of education, tourism, health, sports, entertainment, architecture and construction, etc. (Kosti et al., 2023).
Virtual reality is a technology that allows a user to interact with a computer-simulated environment, whether that environment is a simulation of the real world or an imaginary one. With virtual reality, we can experience the most frightening and overwhelming situations with safe play and a learning perspective (Mandal, 2013). Most people are curious about the possibilities and future of new technologies, considering the various applications it is supposed to offer such as virtual meetings, learning environments and many others, however, there are also concerns about potential negative effects. because real world signals can be transmitted in the virtual world. In this regard, people express their feelings in different social networks (Bhattacharyya et al., 2023).
Methodology
According to the main goal of the research, which is to classify comments related to virtual reality technology using machine learning methods and a dictionary-based approach, therefore, about one million tweets in the field of virtual reality technology were collected by the web crawler and After data preprocessing, 480,432 samples remained in the data, then Dirichlet hidden allocation thematic modeling was implemented on the data. By examining the distribution of words in tweets, this modeling tries to separate different topics by detecting the distribution of words; The tweets whose distribution of words are similar were put into a topic, and the number of topics with the highest score was selected, the number of topics 9 has higher coherence, and the data was grouped into 9 topics, so once again, Dirichlet hidden allocation modeling was applied 9 topics were done, whereby the tweets were grouped into 9 different topics. Considering that we have a probability distribution, the confusion criterion was used to evaluate the model. The lower the confusion criterion and the higher the coherence score, the more efficient the model is. With the help of keyword weights obtained by Dirichlet hidden allocation modeling and examining at least 5 different tweets from each topic, 9 topics related to virtual reality technology were identified: "New Technologies", "Creation and Make", "Technological Business", "Education", "Virtual Games", "Progress", "Gadget", "Metaverse" and "Indiegame" were named.
Discussion and Conclusion
In this research, by examining topics in different years, we observed that the topic of "Progress" was the most popular topic among users from 2017 to the end of 2021, in early 2022, this topic gave way to "Metaverse", currently "Metaverse" is one of the most popular topics being discussed by users. Businesses in the field of virtual reality should strive for the attractiveness of "Metaverse" and attract users. Likewise, if we observe the "Virtual Games" and "Technological Business" graphs, we can see that they change with almost the same ratio in different years, meaning that these graphs are related to each other, in fact, business and keep in mind that the factors affecting these two issues are the same, but in the case of "Virtual Games" it has more effects, and if "Technological Businesses" specifically focus on virtual games, they will grow more due to the greater attention of users. had Similarly, "Indiegame" which have had a series of changes but in recent years have had a declining trend and then no change, now the creators of these games should check, and in general "Virtual Games" are a more interesting topic than "Indiegame". In the subjects of "Education" and "Gadget" it has been decreasing since the beginning of 2017, which shows that users lost their attention to these subjects in the field of virtual reality over time, in fact to other topics showed their attention, so it is better for businesses that are active in this field to take measures to advertise and attract users, or change their user field if there is no growth.
Keywords: Data Mining, Text Mining, Virtual Reality Technology, Topic Modeling, Latent Dirichlet Allocation.
Data, information and knowledge management in the field of smart business
Mohsen Shafiei Nikabadi; Roya Esmaeilzadeh; Mina Abfroush
Abstract
The business model is an important factor in the competitive advantage of companies، and companies need to recreate their business model by changing the business environment due to changes in technology and communication. The current research aims to design a dynamic model based on text mining and soft ...
Read More
The business model is an important factor in the competitive advantage of companies، and companies need to recreate their business model by changing the business environment due to changes in technology and communication. The current research aims to design a dynamic model based on text mining and soft methods to determine the most important key factors of electronic business models. This research is based on the text mining method and using the system dynamics modeling approach. In order to extract the key factors، the text mining of 779 articles of the last ten years from the world's authoritative databases has been examined. After examining the experts and selecting 17 key factors from among the extracted factors، in order to investigate the causal relationships between the key factors، the DEMATEL technique was used and the DEMATEL matrix was completed by the experts، and finally، the dynamic model of the research was drawn using Vensim software. The most influential causal factor is "Internet of Things" followed by "blockchain and cloud processing"، and the most impressionable disabling factor is "provided value in the business". Also، the most influential factor on all factors was "nature of the media" and the most impressionable factor among the set of factors was "type of used technology".IntroductionThe business model is an important factor in the competitive advantage of companies، and companies need to recreate their business model by changing the business environment due to changes in technology and communication. The current research aims to design a dynamic model based on text mining and soft methods to determine the most important key factors of electronic business models. This research is based on the text mining method and using the system dynamics modeling approach.In the current research، using dynamic modeling، the key factors of electronic business models have been determined with text mining and other soft methods. Examining the causal relationships between the key factors of e-business models and determining the effect coefficients of each factor on other factors and finally determining the causal/effectual nature of the factors and prioritizing them based on the degree of influence and effectiveness can Consider the innovative aspect of research.2.Research Question(s)The main question of this research is what are the most important key factors of electronic business models and how do they interact? Literature ReviewThe business model can be considered as a type of architecture for the product، service and information flow، which includes a description of different business agents، their role in this، potential advantages for each of these agents and their sources of income (Roweley، 2002).Electronic business models are a description of work processes that are used in virtual or electronic environments such as the World Wide Web (Botto، 2003). These models are a description of the roles and relationships between customers، consumers، partners and suppliers، which seeks to determine and identify the main flows of products، information and money، and to identify major benefits for shareholders and business participants، and by using It works from the Internet to conduct interactions and create value for customers and other stakeholders (Currie، 2004).According to the literature review، it can be seen that different researchers have presented models in different spatial domains، but no research has been seen that can identify، classify and analyze all the components in different models and identify their interactions.MethodologyIn order to extract the key factors، the text mining of 779 articles of the last ten years from the world's authoritative databases has been examined. After examining the experts and selecting 17 key factors from among the extracted factors، in order to investigate the causal relationships between the key factors، the DEMATEL technique was used and the DEMATEL matrix was completed by the experts، and finally، the dynamic model of the research was drawn using Vensim software. In this research، to collect articles، integrate and clean the data، we tried to use the reliable global databases of Wiley، Taylor and Francis، Springer، Oxford، Inderscience، IGI Global، Emerald، and Elsevier.In this research، in the first step of collecting articles، merging and cleaning data for articles of the last ten years from the reliable global databases of Wiley، Taylor and Francis، Springer، Oxford، Inscience، IGI Global، Emerald، and Elsevier. Is. At this stage، the following 4 key phrases were searched;"e-business model"، "e-commerce model"، "electronic business model"، "electronic commerce model"In the second step of the research، extraction of frequent words was done in the web portal Voint. Voint Portal is an online program used for text analysis.In the third step of the research، pre-processing، normalization and clustering of frequent words and clustering evaluation were done by Rapidminer software and its output is the classification of data with different topics.In the fourth step، the key words of each cluster were extracted using the experts' opinion، and finally، the key variables of electronic business models were extracted.In the fifth step، a researcher-made questionnaire was created based on the Dimtel technique and among experts in the field of e-business (people with more than ten years of working and executive experience in the field of e-commerce and business and the development of information technology tools، in active companies in this field with master's education and above) was distributed in order to identify the causal relationships between the variables extracted in the previous step.In the sixth step، it is time to present a dynamic model of the studied factors. The dynamic modeling process used in the current research consists of two stages: "modeling cause and effect loops" and "dynamic modeling".ResultsFirst part: text mining and clustering.In the first stage of research (text mining)، the results of pre-processing، selection and selection of indicators by experts show 17 factors of "type of business and trade"، "type of value provided in business"، "Type of offered product"، "Type of customer and its features"، "Type of technology used"، "Type of market"، "Online social networks"، "Business platform and website"، "Source and Sourcing"، "Innovation in Business"، "Processes and Knowledge Management in Business"، "Nature of Supply Chain"، "Dimensions of Internet of Things"، "Blockchain and Cloud Processing"، "Competitive environment"، "Information security and privacy"، "The nature of media"، are key factors of electronic business models.The second part: combining techniques to design a dynamic model.In the first part of the second stage of the research (Dimtel technique)، the causal model of the factors، the degree of influence and the coefficients of the influence of each factor on other factors have been studied، which is used as the basis for the design of the dynamic model of the research.In the second part of the second stage of the research (system dynamics)، based on the results of the first stage and then Dimtel، the dynamic model of the key factors of the electronic business model has been designed using Vansim software.ConclusionThe most influential causal factor is "Internet of Things" followed by "blockchain and cloud processing"، and the most impressionable disabling factor is "provided value in the business". Also، the most influential factor on all factors was "nature of the media" and the most impressionable factor among the set of factors was "type of used technology ". As mentioned، the factors of "Internet of Things" and "Blockchain/Cloud Processing" are the most important causal factors. Considering the importance of Internet of Things and artificial intelligence and blockchain، which are the main driving forces in the future technology revolution، it is suggested that companies pay attention to these technologies in order to earn quick and lasting income. Also، in the prioritization based on the effect of one factor on the set of factors، "the nature of the media" is in the first place، which is a sign of the important need of business activists for the media.Keywords: E-business model، Text mining، DEMATEL، Voyant، Vensim، Dynamic modeling.
