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 ...
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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.
Venus Mohammadi; Mohsen Hosseinzadeh; Mehdi Hosseinzadeh Hosseinzadeh
Abstract
v Recommender systems utilization has proven sales enhancement in most e-commerce platforms. This system objected to provide more options, comfort and flexibility to user which could make him interested, as well as providing better chance for companies to increase sells in their products and services. ...
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v Recommender systems utilization has proven sales enhancement in most e-commerce platforms. This system objected to provide more options, comfort and flexibility to user which could make him interested, as well as providing better chance for companies to increase sells in their products and services. Flourishing popularity of web site has originated intrigue for recommendation systems. By penetrating in infinite fields, recommendation systems give deceptive suggestion on services compatible with user precedence. Integrating recommender systems by data management techniques to can targeted such issues and quality of suggestions will be improved considerably. Recent research reveals an idea of utilizing social network data to refine weakness points of traditional recommender system and improve prediction accuracy and efficiency. In this paper we represent views of recommender systems based on Twitter social network data by usage of variety interfaces, content analysis Methods, computational linguistics techniques and MALLET topic modeling algorithm. By deep exploration of objects, methodologies and available data sources, this paper will helps interested people to develop travel recommendation systems and facilitates future research by achieved direction.
Seyyed Jalaladdin Hosseini Dehshiri; Mojtaba Aghaei; Mohammad’Taghi Taghavifard
Abstract
Recommender systems utilization has proven sales enhancement in most e-commerce platforms. This system objected to provide more options, comfort and flexibility to user which could make him interested, as well as providing better chance for companies to increase sells in their products and services. ...
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Recommender systems utilization has proven sales enhancement in most e-commerce platforms. This system objected to provide more options, comfort and flexibility to user which could make him interested, as well as providing better chance for companies to increase sells in their products and services. Flourishing popularity of web site has originated intrigue for recommendation systems. By penetrating in infinite fields, recommendation systems give deceptive suggestion on services compatible with user precedence. Integrating recommender systems by data management techniques to can targeted such issues and quality of suggestions will be improved considerably. Recent research reveals an idea of utilizing social network data to refine weakness points of traditional recommender system and improve prediction accuracy and efficiency. In this paper we represent views of recommender systems based on Twitter social network data by usage of variety interfaces, content analysis Methods, computational linguistics techniques and MALLET topic modeling algorithm. By deep exploration of objects, methodologies and available data sources, this paper will helps interested people to develop travel recommendation systems and facilitates future research by achieved direction.
