Virtual Reality Technology: Analysis based on text and opinion mining

MBE, 19(8): 7856–7885. DOI: 10.3934/mbe.2022367 Received: 16 January 2022 Revised: 24 March 2022 Accepted: 02 May 2022 Published: 26 May 2022 http://www.aimspress.com/journal/MBE Research article Virtual Reality Technology: Analysis based on text and opinion mining Pedro R. Palos Sánchez 1, José A. Folgado-Fernández 2,* and Mario Alberto Rojas Sánchez3 1 Department Financial Economy and Operations Management, University of Seville, Spain 2 Department of Financial Economics and Accounting, Universidad de Extremadura, Spain 3 Department of Business Administration, Technological Institute of Costa Rica, San José, Costa Rica * Correspondence: Email: [email protected]. Abstract: The purpose of this research is to highlight the importance of periodically analyzing the data obtained from the technological sources used by customers, such as user comments on social networks and videos, using qualitative data analysis software. This research analyzes user sentiments, words, and opinions about virtual reality (VR) videos on YouTube in order to explore user reactions to such videos, as well as to establish whether this technology contributes to the sustainability of natural environments. User-generated data can provide important information for decision making about future policies of companies that produce video content. The results of our analysis of 12 videos revealed that users predominantly perceived these videos positively. This conclusion was supported by the findings of an opinion and text analysis, which identified positive reviews for videos and channels with many followers and large numbers of visits. The features such as the quality of the video and the accessibility of technology were appreciated by the viewers, whereas videos that are 100% VR and require special glasses to view them do not have as many visits. However, VR was seen to be a product which viewers were interested in and, according to Google, there are an increasing number of searches and sales of VR glasses in holiday seasons. Emotions of wonder and joy are more evident than emotions of anger or frustration, so positive feelings can be seen to be predominant. Keywords: virtual reality; sustainable tourism; sentiment analysis; text mining; LDA; MaxQDA 1. Introduction Constant market changes and the need for timely information force companies to use 7857 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. appropriate differentiation and growth strategies that fit the needs of customers. The disruptive aspects of information and communication technologies must be taken into account in order to improve the adaptability of companies to unexpected events such as pandemics. It is also essential that companies consider improving traditional market research processes by using both intentional user-generated content (UGC) from sources such as social media, as well as unintentional user-generated content (UGB) like images and information from geolocation applications, etc. This study analyzes comments on videos on virtual reality (VR) technology and its applications in tourism and other fields. This study analyzes user feedback on VR videos. These comments are an important source of information about user emotions and feelings which can be written [1–3] or conveyed with symbols such as emojis [4]. Comments allow the data to be analyzed to identify a range of emotions that are expressed with language that is not always expected. It is important to use technology in the process of analyzing feelings expressed with online comments, such as content on social networks, and analyzing and optimizing resources with technology that incorporates collaborative economies [5]. Recent studies explore research conducted on virtual reality in the context of tourism, concluding that “VR has been applied in planning, managing, promoting, educating, and creating or transforming tourist experiences” [6], (p.17). [7] analyze the dimensions of online customer engagement and associated concepts in online customer reviews, finding a high impact of cognitive processing of engagement, dimension and hedonic experience on the customer review effort, highlighting the importance of companies listening to feedback and aligning with these expectations and turning them into competitive advantages. The adoption of virtual reality technologies promotes economic growth by creating new market opportunities, can shorten the product innovation cycle, increase product quality, reduce time to market, and consider consumer expectations in a joint process of continuous improvement [8]. The use of mobile technologies in stores has led to new services, where VR applications are part of technologies that have led to what is known as electronic commerce [9]. These practices improve the user's shopping experience, even making it possible to dispense with staff at points of sale. Marketing despite not being a new concept, there is a phenomenon that we are interested in knowing, the feedback from the customer of the product or service offered, even if it is tacit, we seek to address this gap through the use of technological tools that allow one to improve decision making and improve the B2C relationship, through the analysis of text and sentiment obtained from comments on social networks [10]. Moro et al. 2019 [11] believe that there is a lack of research adopting Big Data/machine learning approaches based on secondary data. Specifically, we have selected from a list 12 virtual reality videos from the social network YouTube, which allows users to upload or comment on the content of the same, through the use of technological tools allows us to a systematic analysis of the information provided. An important aspect that becomes a limitation to cover in detail important aspects of feedback, mainly the one obtained from videos and making it difficult to turn it into competitive advantages, are the differences between the real environment and the online environment, the latter lacks some factors such as temperature, smell, texture, or people [12]. These stimuli of the senses have marked the progress of VR from its beginnings to the present day, factors that can become stimuli for reality scenarios with the evolution of technology. 7858 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. We expect to find implicit or explicit factors in user comments, reviews that can be classified as positive or negative opinions about VR technology, infer which features of videos influence favorable or unfavorable customer perceptions, visualizing suggestions for companies that generate VR content can create or change the focus of company strategies [13]. Knowing the user's feelings about the product allows the changes that increasingly demanding technological consumers expect to be quickly incorporated while respecting sustainability, which is a great challenge facing the world [14]. Technology platforms help reduce the number of resources, seek to reduce the environmental impact, and are considered a driver of sustainability [15]. An increasing number of users prefer green or sustainable products [16], leading to the emergence of green consumers that incorporate all dimensions of sustainability into their experiences [17]. The present study attempts to: (i) highlight spontaneous verbal reactions to videos conveying positive or negative emotions, as well as the language used; (ii) understand which attributes of virtual reality are most effective in terms of VR video user satisfaction; (iii) identify emotions in the messages generated by users after watching VR videos; and (iv) check in the comments to the videos if it is evident that virtual reality can be a substitute for a real tourist experience. To address these objectives, we used text mining using R software, we consider it the best way to analyze the content of more than 14,000 comments expressed by users for the set of selected videos, and we also performed an opinion mining and trend analysis, in search of timely information for the objectives set. This technique allows us to process a large amount of unstructured text, by categorizing and grouping text and sentiment analysis (attitudes, opinions, and general emotions of customers about a product or service), among others [18]. These analyses are performed considering reviews and comments published online [19]. This study addresses the following research question: RQ 1: What are the feelings and opinions of the viewers of VR nature videos about using virtual reality? RQ 2: What are the perceived components of VR in video feedback? RQ3. What is the trend in virtual reality in relation to real life experiences? RQ4. What are the predominant emotions? RQ 5. What are the topics around which the comments are grouped? This article aims to contribute to the academic literature with research work on the role of virtual reality in a better understanding of the life of nature. Virtual reality videos on a public social network like Youtube can help users feel virtual experiences in virtual reality technology such as 4K, 360, VR180 and others. Furthermore, this VR technology would preserve a national park and would not disturb wild animal life, and therefore would award deterioration of natural spaces. This article is organized as follows. The theoretical basis is presented in Section 2. Section 3 describes the methodology used in this study. Section 4 presents the results, which are discussed in Section 5. The conclusions are set out in Section 6. 2. Materials and methods 2.1. Virtual Reality VR is a technology that allows for an immersive experience of two different realities, through the use of a helmet with stereoscopic screens [20]; achieving the user to interact with a virtual 7859 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. environment through the multiple devices available, ranging from a keyboard, to suits, which seek to deceive our brain to stimulate the senses and perceive as real that environment. For the purposes of this research, VR is defined as "A computer-simulated environment where one interacts within the environment similar to how one would in the real world" [21]. This technology brings with it a history of success and applications in multiple fields such as e-Commerce [22]; in education [23]; and many others. What makes it particularly interesting in our view is its ability to lead us to a parallel world through devices that are frequently enhanced [24]; and to the extent that more senses are stimulated, a greater sense of immersion is achieved through multisensory experience [25]; achieving the sensation of interacting in that virtual environment. The origin of virtual reality technologies dates back mainly to the work of Morton Heilig and Ivan Sutherland from 1960 [26]; although we find important data that precede it, such as the creation of the stereoscope by Sir Charles Wheatstone in the year 1838, this invention consisted of a device that presents a double image that our brain mixes and visualizes as one. The term Virtual Reality emerged in the 1980s, coined by Jaron Lanier who used the expression "virtual reality", it consisted of a head mounted display (HMD), data suit, and gloves attached to a computer. The use of images generated through this medium has been commonly used, which increasingly enables the emergence of new and improved VR devices that are affordable for the user [27,28]. In 2014 virtual reality is growing, after the birth of Oculus, prototypes of virtual reality glasses start to be developed by large companies, and by 2016 new and improved devices were launched in the market, such as: Samsung Gear VR, in addition, important brands announce their devices with a wide amount of options to be used in different fields such as medicine [29]; optimizing complex products in process design [23]; military training [30]; and tourism, seeking to understand how to effectively market tourist destinations [31]; among others. 2.2. Data Mining The term data mining refers to a set of techniques and technologies that make it possible to explore large databases, automatically or semi-automatically. Its objective is to find repetitive patterns that explain the behaviour of these data. These techniques are used in many scientific and technological fields, such as the use of comprehensive classification and evaluation systems with lexicographic genres, such as WiPO Pearl [32], which is a complex terminology tool that supports ontological tools for searching terms in addition to traditional linguistic search methods. Other research, software as MaxQDA, data in text form is processed, it allows qualitative data analysis, providing a lexical approach with the evaluation of metrics for opinion extraction, being able to organize and categorize any type of unstructured data, search for information, test theories and create illustrations [33]. Previous studies have used the software to download and analyze the dataset, converting unstructured text into codes, memos, categories that allow text mining and opinion mining, for example, [34–37]. We have focused our interest on the context of tourism, seeking to understand consumer behavior, through reviews and recommendations in our case comments on VR videos, especially if we want to influence the purchase decision of a destination, which is related to any positive or negative statement made by potential, actual, or former customers about a product / service or company, and is available through the Internet [38]. Several publications applied context analysis and data mining in different researches, such as 7860 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. online feedback and satisfaction in hotels [39,40], startups [41], retail [42,43]. Other articles demonstrate the role of social networks in the development of recruitment actions that favor user interface design and conversions in mobile applications [44], especially in thought apps from the tourism sector [45]. Feedback is a way to interact with the company in the improvement or co-creation of products; it is an unstructured language that must be transformed into knowledge by management, technologies create value through new customer experiences and through interactions between customers [10]. Social networks allow companies to communicate with their potential customers, hence the importance of creating social content considering creativity, emotion, and passion, attributes that engage users of social networks [33]. People can share their experience and information with other users; this can affect purchase intention [46]. Online comments expose the emotional states and thoughts of consumers when they think about the company or the brand (product) [47]. Recent studies have used videos from the social network YouTube, [48] study whether virtual reality affects behavioral intention, for this they use a K-Pop video on YouTube, they highlight that virtual reality videos of celebrities on YouTube influence users' engagement processes, and in addition, virtual reality videos on YouTube have the potential to connect with celebrities (immersion and telepresence). [49] study virtual reality experiences in nature, such as watching wolves on YouTube, investigate aspects such as presence, emotions, and attitudes in immersive and non-immersive environments, find that immersive technology could induce interest in a test-related nature experience, and the findings suggest that 360◦ videos using immersive technology provide experiences with positive affective learning outcomes. [1] used the titles of videos uploaded to YouTube, since, on social networks, information about the law to force children to have mandatory vaccinations for school attendance, this fosters a fierce discussion between pro-vaccination and anti-vaccination people. Sentiment analysis shows that the intense vaccination campaign also promoted by physicians pushed sentiment to shift polarity from a negative to a positive opinion. [50] use YouTube content for analysis of cannabis vaping videos and [51] use YouTube content to explore trends in nursing start-ups, using text mining in one of their stages, finding that to foster nursing start-ups, business skills such as finance and budgeting need to be strengthened and active political support needs to be established. Social networks help users establish friendly relationships and informal communication, as well as instantly and easily share their thoughts, feelings, and opinions [52]. Through user comments on products, spam, or news dissemination, online opinions have a high potential for information analysis on a topic or product of interest [53]. There is evidence that the user-generated customer reviews are more trusted than those posted by the seller [54]. Consequently, users' opinions have been studied using sentiment analysis (SA) and text mining (TM), both of which are popular methods to discover the valence of written texts [55,56]. The amount of information available online is immense and it is difficult to obtain valuable information without a relevant technique that analyzes patterns [57]. In this respect, both SA and TM are very useful. TM searches for automatic data patterns while processing the language used; it also enables evaluating the degree of association, keywords, keyword phrases, concept hierarchy, semantic networks, classification rules or prediction [58]. In its turn, SA can help identify and classify emotions in the analyzed texts as positive, negative, or neutral [55]. The TM approach involves a process of organizing, editing, and analyzing large amounts of unstructured text information to discover associations between features, provide specific information 7861 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. for users who need it, and to find and extract knowledge from the data [58]. In essence, TM consists of extracting data from documents to obtain useful information from those documents [59]. The data are then converted to a document using the matrix (DTA) where each column represents a term, each row represents a document, and each cell shows the frequency of a given term in the analyzed document [60]. In an analysis of social network data using TM, [61] found three main groups and explored the sentiments for each group. The TM approach was also used effectively to analyze unstructured online reviews [62], production output, as well as chemical, biological and patent information [63]. The SA approach is used to explore emotions in written texts. Typically, SA is based on one of the following two approaches: (1) automatic sentiment classification using an algorithm or (2) lexicon-based classification using dictionaries for sentiment classification in text [64]. These approaches are used to describe the attitudes, experiences and opinions of users about a product, and these insights generate valuable information for companies in terms of identifying potential buyers [65]. Using the SA approach by monitoring social networks or online discussion forums can change the way companies measure consumer opinions [58]. For example, [66] used SA to measure sentiments expressed in reviews of online content. Similarly, [67] used SA to better understand public attitudes towards autonomous vehicles. 3. Methodology We followed the procedure previously developed by [68] and [69] to study user-generated content in the tourism industry. The data was collected from YouTube videos that are freely available to the public. Our specific focus was on videos with informative VR topics related to tourism, sustainability and high-quality VR content, as these videos attract a large number of viewers from different countries or regions, and the results were expected to have high external validity [70]. To find the videos, the following keywords were used: virtual reality tour, VR travel, sustainable tourism, VR 360, VR experiences, virtual vacation, virtual tourism, sustainability, changing planet, VR nature. Relevant videos are selected according to the number of views, topic of interest, and number of comments. The final data set consisted of a total of 12 videos. Selection criteria are summarized in Table 1. The videos included in the analysis are shown in Table 2. For this research was important in the first term topic of interest. Virtual reality videos with different focus on nature, wild animals, special protect species, natural monuments or planets were discarded because they did not provide real life experiences. However, the number of views and comments was very important because the applied method requires a sample. The results were better with a lot of comments. The number of views was a quality of the criteria related to popularity. The length of video is a critical question to avoid abandonment of video pages. Finally, users prefer short videos, and this criterion is a condition for obtaining successful visits. Video data mining was performed between November 2020 and March 2021. To ensure that the comments analyzed were related to the information contained in the videos, rather than the thoughts and feedback of the users to the conversation threads, we used two software programs MaxQDA 2020 and R.TeMiS. The collected data set of comments consisted of 5857 comments posted between March 2014 and March 2021. The comments were processed using MaxQDA 2020 software [71], which performs qualitative data analysis and derives quantitative information through a series of metrics [72]. To this end, the categories form codes. These codes and categories facilitate the evaluation of texts, videos, audio files, and images in the opinion-mining or sentiment analysis 7862 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. process. The main codes for the virtual reality category were as follows: nature, environment, tourism, heritage, accessibility, age, education, awareness, simulation, physical disability, love, glass, quality, experience. The categories obtained were opinion on videos, language, sustainability, ecology, among several others. Table 1. Search criteria for video selection. Search strings Filters Length of the video VR Technology Sort by Channel “virtual reality nature” OR “virtual reality tourism nature” OR “virtual reality sustainability nature” < 4 mins. 5.7K OR 4K OR 360º OR VR180 OR 3D Relevance AND Views > 50000 followers From the searches, we classified the results into the following types of videos (see Tables 2–3). Table 2. Video nature VR classification. Main channels Nature Classification Subject VR Technology Followers Comments Balu Niagara falls, forest wildlife or birds lagoon VR180, 3D 464,000 207 Airpano VR Salto Angel, Venezuela 360º 302,000 2,758 ECOVR Dive great barrier reef 5.7 K for oculus quest 42,100 27 National Geographic Animals at risk of extinction: Tigers, Lions, Elephants, Orangutans, Forest: Giant sequoias… Waterfalls: Victoria Extreme places: Antarctica 360º 17,2 millions 5,876 Animal Planet Elephants 4K, 360º 4,99 millions 86 At home in Wild Spaces Giant forest sequoia 360º 130,292 169 BBC Hearth Tourism Nature: forest in the Scottish highlands, baby turtles or bears brave VR180 9,25 millions 498 Qantas Hamilton Island 360º 102,000 731 Table 3. Environmental classification of videos. Main channels Environmental Classification Subject VR Technology Followers Comments We the curious Astronomy: Tour of six real exoplanets 4K, 360º 90,000 5,221 David Addis Chernobyl VR VR180 6,052 27 7863 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. As a second evaluation criterion, text mining was used. To the end, online comments were preprocessed by removing nonrelevant words such as "the" or "and", numbering, punctuation, white spaces, HTML tags, as well as by converting words to lowercase and using the root for words with the same meaning, but with different suffixes and prefixes to avoid noise in the dataset [73,56]. Since we sought to establish patterns from an unstructured collection of comments on videos after the debugging process, this analysis included not only natural language searches with the support of Boolean operators, but also information behind text, keywords and phrases [74]; our purpose was to find useful information for decision making that is not explicit and has minimal human intervention or interference [75]. For text mining analysis, we used a free software program called R-Text Mining Solution (R.TeMiS) [76,77]. This package R makes it possible to create text sets, analyze and manipulate them [78]. Additionally, the R-Code libraries 'syuzhet' [79,80] for emotion analysis and 'sentimentr' [81,82] for sentiment analysis were used. The classification of topics was supported with 'LDA' [83]. Through, the use of algorithms and statistical techniques, this approach enables obtaining the first data to later verify and evaluate the results [84]. Table 4 shows the videos selected based on our search processes. An important aspect to consider when analyzing VR video feedback is consumer behavior in terms of how they view the videos, which is difficult to clearly identify in the information provided by users. There are many options on the market, both for beginner and expert consumers, the differences go beyond price, although the higher the cost of the device, the better the immersion and interaction with VR. Many high-end virtual reality glasses use a six-degrees-of-freedom (DoF) tracking system, allowing you to move freely, crouch, jump, dodge objects, walk in different directions, interact with things in the virtual world even with both hands, offering a better experience. They are models with important features to highlight, such as sound quality, isolation from the real world, and better resolution, allowing a sharper and more realistic image, ability to know the scale of the room and hand tracking, allowing the location in that space which makes the images are seen immediately and the fluidity of movements in the parallel virtual world is achieved in a natural and realistic way, the major manufacturers in the market offer us many options and constant innovation, as an example, two high-end models are Oculus Quest 2, HTC Vive Pro. Table 5 shows general data by video type on Youtube. 7864 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. Table 4. Videos selected for the study according to VR type on YouTube. Name VR Type Date Visits Comments Like Dislike 5.7K, 180º VR video set VR180 Virtual Dive Great Barrier Reef Underwater for Oculus Quest 5.7K, 180 06/03/2020 68,126 27 918 44 5.7K, 360º VR video set Virtual Nature Meditation for Oculus Quest 5.7K, 360 20/05/2017 6,061,118 1635 87,716 4,922 4K, 360º VR video set Surrounded by White Lions - 4K 360° VR 4K, 360 26/11/2018 11,776,037 1,921 63,548 8,449 Travel Without Leaving Home 4K, 360 08/07/2018 513,008 1,026 9,530 374 Take a Virtual Reality tour of six REAL exoplanets 4K, 360 22/09/2017 12,832,154 4,938 184,514 7,486 360°, Salto Ángel, Venezuela. Video aéreo en 8K 4K, 360 10/04/2017 145,000,000 2758 134,888 7,608 Visit Hamilton Island Virtual Reality with Qantas 4K, 360 29/08/2015 6,076,458 731 17,778 2,076 4K, 180º VR video set The Amazing Niagara Falls in VR180! - 3D Virtual Reality Experience VR18 0 18/07/2019 598,563 117 4.632 413 Peaceful Lake Views in VR180 - Relaxing 3D Virtual Reality Experience with Calming Music VR18 0 28/08/2019 185,044 50 1,276 159 Beautiful Forest Wildlife in Deer & Elk - Relaxing 3D Virtual Reality Experience VR18 0 24/08/2019 520,449 90 3,422 413 ES VR 180 virtual reality tourism nature Unwind In A Forest In The Scottish Highlands VR18 0 12/12/2019 104,364 82 1,607 130 Virtual tour of the reptile zoo!! All my reptiles!! VR18 0 28/07/2019 128,195 1,075 5,578 229 Total 183,863,516 14,450 515,407 32,303 7871 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. Source: Google Trends Figure 9. Searches for the term 'Virtual reality' in blue and 'Virtual reality headset' in red, all over the world (August 2016-2021). In the gaming industry, PlayStation VR has an advantage thanks to its lower cost, compared to market options such as the Oculus Rift system, games on social networks, computers, consoles or mobile devices. use devices that seem futuristic, due to the various means of interaction (special glasses, motion sensors, virtual headsets). It is a growing market that involves electronic sports, even with the opportunity to compete and interact in championships with prizes that exceed $100 million and whose public is anywhere in the world, hence the importance of offering the latest applications, and the generational change of consoles. And the recent launches such as Sony's PS5 and Microsoft's Xbox Series X, without leaving aside the advances of the last years of the main industries such as Samsung, Apple, GoPro, Nintendo Co. Ltd., HTC, among others. The VR in Gaming and AR in Gaming market is present in five major regions, Europe, North America, Latin America, Asia Pacific, and the Middle East and Africa. In some of these regions, with the arrival of winter in December, people plan their vacations to travel to warmer destinations, or take advantage of the time at home for relaxation and entertainment, with VR videos and games being an excellent alternative. In Figure 10, we see the historical searches for virtual reality and PlayStation, one of the main consoles in this industry. Source: Google Trends Figure 10. Searches for the term 'Virtual reality' in blue and 'PlayStation VR' in red, throughout the world (August 2016-2021). 7872 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. 4.3. Text Mining Analysis Using textual analysis [76], we can perform operations typical of lexical statistics (e.g., measurement of occurrences and cooccurrences of terms) and textual data analysis (e.g., hierarchical ascending classification and factor analysis of correspondences). With TA, we can also construct a matrix of documents (rows) and terms (columns) so that the frequencies (occurrences) of each term in each document are observed in the cells [86]. We performed the classification of the corpus documents as follows. We used the text of the "comments" field of the analyzed YouTube videos, thus using the text of each comment and statistically analyzing the remaining fields, such as "channel name", "number of likes", "number of answers", "superior level" and "date". The superior level field allowed us to establish whether each comment was a first-level comment or a reply. The multivariate techniques used to classify the research topics were hierarchical clustering and correspondence analysis. Cluster analysis is a multivariate technique that aims to group elements (or variables) to achieve maximum homogeneity in each group and the greatest difference between groups [87]. The dendrogram is the graphic representation used to interpret the results of the cluster analysis. The steps followed to compile the video comments using MaxQDA are shown in Figure 11. Figure 11. Steps of the Data Collection Process. 4.3.1. Field analysis of comments The complete corpus downloaded from YouTube included a total of 5,857 comments/documents Step 1. Download the comments from URL youtube and convert to csv format for processing. Step 2. Manually analyse words. If they are terms divided at the end of a line, remove the comment. If they are hyphenated words, leave the hyphen in. Step 3. In order to focus the study on the main research topic of the comments analysed, manually remove words in other language. Step 4. Convert all terms to lowercase so there are no differences between the same terms. Step 5. Remove punctuation marks. Step 6. Delete digits. Step 7. Remove stopwords which are commonly used in a language but that do not provide information in the text analysis. Some examples in English are: was, to, were, that, then, the, a, of, an, and, among others. 7873 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. for the 12 selected videos. Table 6 shows the complete corpus, while Figure 12 shows the distribution channels. Table 6. Complete corpus. Non-/sparse entries Sparsity Maximum term length 31684/30752708 100% 94 Figure 12. Channel distribution. 4.3.2 Hierarchical clustering Hierarchical clustering was performed on the data set. Hierarchical ascending clustering, also called agglomerative or cumulative hierarchical clustering, was performed according to Ward's minimum variation method, which uses the chi-square distance [77], a procedure that joins, at each step, the two clusters for which there is the smallest increase in the total value of the sum of the squares of the differences within each cluster from each individual to the centroid of the cluster [88]. Terms appearing in more than 99% of cases were eliminated, and two dimensions were identified. We then eliminated 1,603 comments that did not meet this requirement. The bar graph obtained using hierarchical clustering is shown in Figure 13. Of the total number of documents, 6 groupings were created according to the following parameters (see Table 7). Table 7 shows the validation statistics for the maximum number of clusters, a step after the detection of the most frequent terms, where we obtained 6 clusters applying Ward's minimum variance method with the chi-square distance [89]. Table 7 shows the most interesting groups from the point of view of the number of comments and the quality of the results. The clusters in Figure 13 represent the opinions, feelings, and emotions of the most frequent users in the comments of the analyzed videos. Channels 360G MALDIVES NBC_News NEW_ZEALAND SENIOR_TRAV Take_VR VR_GetReady VR_Tourism VRI WOAV WORLD_1 WST 7874 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. Figure 13. Clusters of the total set of comments. Table 7. Clusters Summary. Topic Comments Frequency Mean 1 723 16.48% Vitality 2 883 20.12% Perceived enjoyment 3 763 17.39% Spirituality 4 627 14.29% Virtual reality industry 5 672 15.31% Representation of reality 6 720 16.41% Planets and Earth (space) total 4,388 100.00% The results obtained for cluster 1 have been interpreted as vitality: loving things in life. Cluster 2 represents perceived enjoyment, due to the association of words such as 'video' or 'planet' with 'cool', 'amazing', 'use', or 'watch'. Cluster 3 represents 'Spirituality' (“video',”god'''amazing', 'love', 'mean', 'hell', etc.). Cluster 4 was associated with the virtual reality industry (“video”, “work”,”world”, “see”, “make”…). Ir is a more professional cluster. Cluster 5 obtained high beta values with the following words: 'look', 'think', 'real', 'watch', 'something', etc. and represented reality. Finally, Cluster 6 was closely related to 'planet' and 'earth' and was understood as 'planets and earth: the space.' The most interesting clusters with related terms are shown in detail in Figure 13 and Table 8. 7875 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. Table 8. Cluster means. Cluster 1 2 3 4 5 6 Love, life, will, thing, live, universe Video, Planet, Cool, Use, Watch, Video, God, Amazing, Love, Good, Make, Mean Video, work, world, see, move Look, think, real, something, reality, wow Planet, video, Earth, look, watch Figure 14 shows the word cloud. The words 'video', 'amazing', 'watch', 'see', 'wow', 'reality', 'cool', 'think', 'space', or 'water' appear in more size due to the high frequency. Figure 14. Word cloud. Figure 15 represents the analysis of emotions from comments. This figure shows 8 emotions. The main emotions detected were trust, fear, and anticipation. However, the 'surprise' obtained less value. Users felt these emotions when they wrote these comments on Youtube, and the number of positive and negative comments (polarity) was very similar: positive (50.62%) and negative (49.37%). 7876 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. Figure 15. Analysis of associated emotions (β). 5. Discussion On the basis of the results, we can now formulate answers to the research questions addressed in the present study. Concerning our first research question (“What are the reactions of VR video viewers to nature, as manifested through users’ feelings and opinions associated with virtual reality?”), we found that video viewers most experienced trust, fear, anticipation, and sadness. In addition, the results of the cluster analysis technique allowed to verify the thematic differentiation when structuring the set of terms according to the 6 clusters previously obtained in the hierarchical classification. As the bar graph reveals, the dispersion of the clusters was scarce, since the 6 clusters represent percentages of comments ranging from 14.29%–20.12%. Which indicates that they are sufficiently symmetrical. It should be noted that the topics identified based on words with higher beta coefficients are consistent. Cluster 6 alone represents one of the VR videos that has the most comments (Take a Virtual Reality tour of six REAL exoplanets). This also explains the appearance of the word wáter in cluster 2 and 4, given that the most commented video is '360°, Angel Falls, Venezuela. Aerial video in 8K.' This means that, for VR video viewers, it is necessary to have a greater production of contents with this technology of representation of reality. However, viewers considered that this representation technology was the best they had ever seen. Other clusters incorporated comments with emotions such as surprise (wow), love, gratitude (thanks), and amazement. In addition, there were also comments that expressed doubts about VR technology, as some videos that were indexed VR in YouTube 360º videos were VR 180. With regard to our RQ1, check in the comments to the videos if it is evident that virtual reality can be a substitute for a real tourist experience. A significant number of comments (e.g., those in Cluster 5 that agglutinated more than 15.31% of all comments) indicated that the VR experience was perceived by the viewers as very real and authentic. In fact, many real tourism experiences were 7877 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. rated with similar emotions (e.g., amazing, awesome, nice, or lovely). There is no evidence of contrasts of VR destinations with real visits of users, perhaps they have not made these trips for various reasons or limitations. In relation with RQ 2 (What are the perceived components of VR in video feedback?), evidence was found indicating that VR Experience, Simulation, Video Quality, Tourism, Accessibility and Education are the perceived components. With regards on RQ3 (What is the trend in virtual reality in relation to real life experiences?), it was found that search engine searches focus on launch dates of technological products, VR videos or video games. In addition, a continuing trend can be observed in the interest in related terms on Wikipedia. The results obtained are in line with other similar research works on Youtube [90,91]. The predominant emotions were experienced trust, fear, anticipation, and sadness. Trust' shows that VR users trust the degree to which these videos represent reality (β = 0.185). Likewise, 'anticipation' appears with high values ((β = 0.172) which means that users have a positive outlook on the future and believe that many protected species and natural spaces will continue to be preserved for future generations. Also, the emotion 'joy' (β = 0.168) was high, indicating the degree of enjoyment and satisfaction of the VR. Also, 'fear' appears among the group of emotions with the highest values (β = 0.135). This is caused by many comments that harshly criticize the fact that the making of these videos prevents the normal life of the animals filmed and that they have even been kept in captivity in order to make the videos. The rest of the emotions such as 'sadness' (β = 0.980), 'anger' (β = 0.950), 'disgust' (β = 0.790) and 'surprise' (β = 0.690) are less emotionally intense in the comments studied. Question RQ4 is therefore answered. With reference to RQ5 (What are the topics around which the comments are grouped?), Figure 13 explains the results. Cluster 6 contains values (β > 0.100) for 'planet' and is therefore very definitive, followed by clusters 2 (Perceived Enjoyment) and 5 (Representation of Reality) with high values (β > 0.05). Other clusters such as 1 represent 'Vitality' or 'how users love things in life', cluster 3 'spirituality' and 'Virtual reality industry'. 6. Conclusions 6.1. Theoretical Contributions This research work applies text mining techniques to analyze messages generated by users after watching virtual reality videos. As suggested by the minimal variability between clusters, the degree of differentiation in this new scientific field remains low. To date, 100% VR videos that require special glasses have not had many views. As suggested by a Google Trends graph (Figure 6), virtual reality goggles are a holiday gift product. A similar trend was found based on Wikipedia searches of users (Figure 7). We also found that the total inertia of the axes, factors, or dimensions is low in all of them, i.e. the contribution of each of the dimensions is so low that the interpretation of each of the two dimensions is complicated. On the other hand, our results also demonstrated that the channels of companies with many followers get a significant number of visits and that the variety of VR videos helps to get more visits (see the case of Balu, National Geographic, BBC Hearth, etc., as shown in Table 2). These results support recent research on the feasibility of applying VR in national parks as a way to make tourists 7878 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. more eco-friendly [92]. Sentiment analysis is an important approach used to analyze massive amounts of data produced by users of social networks. Consumers are increasingly expressing their opinions about different products and services. In this context, YouTube reviews and comments are an excellent way to understand which attributes of virtual reality are the most effective in terms of user satisfaction. Fortunately, these reviews generate a large volume of data recorded in digital form for subsequent analysis. Although short, these comments are spontaneous verbal reactions to videos and convey genuine users’ emotions. Videos' likes and dislikes can also be informative. Lexical statistics and textual data analysis are the basis for the development of the so-called knowledge systems, i.e. expert systems resulting from knowledge engineering, widely used in text mining. The results of our sentiment analysis of the comments of VR video viewers revealed that most users have a positive opinion of this technology and a great interest in learning more about it. The viewers’ emotions included admiration, joy, gratitude, as well as anger, frustration when there are accessibility problems to VR and even stress and fear in very realistic immersive content that highlights their phobias or helps them overcome them. Importantly, our results also suggested that, through simulation of natural environments, VR videos can be used as a means to promote the protection of national parks and raise users’ awareness about environmental problems. 6.2. Managerial Implications VR video content must be adjusted to the progress of the leading companies in the market, such as Oculus and HTC, among others, due to constant change, innovation and greater immersion and interaction, are features that are improved to incorporate our senses more and more. Even though we cannot generalize the results, there is a knowledge that must be taken full advantage of by decision makers, we refer to the comments that are an unstructured language where their position towards the product is reflected, but also their emotions, way of expressing themselves, and expectations of improvement that must be treated with greater care. Despite the fact that VR is at a more advanced stage than other similar technologies, a potential market for VR video consumption devices is evident, in addition, few comments refer to the availability of equipment and accessories to enjoy VR technology, on the contrary, many access through their computer, without the use of accessories such as headphones or glasses, and some comments refer to the difference in costs and purchasing power from one country to another. Another viewing option is the low-end glasses, which use the smartphone and its resources such as accelerometer, compass, screen division for stereoscopic vision, limiting the advantages of this technology to the characteristics of the display device and the phone. What does not go unnoticed is the quality of the VR videos that the user has visualized, it is evident the positive feelings towards the technology that can be used with the available resources, highlighting attributes such as immersion, telepresence, interaction, are the most effective in terms of user satisfaction. Although these feelings and emotions are present in the different formats, comments regarding resolution and quality are more common in 4K, VR360 formats, therefore, advertising and promotion efforts considering the quality and acceptance of this format results in a good opportunity for marketing strategies. The sharpness and fluidity of the movements allowed by 5K, VR360 and very possibly the new formats, allow in-depth tours of tourist or commercial destinations; this is useful as a means of visual 7879 Mathematical Biosciences and Engineering Volume 19, Issue 8, 7856–7885. information, brand positioning, loyalty, promotion, training, and development, among many options, but the way in which the videos are displayed reflects that the efforts that some manufacturers have made to provide low-cost devices, which are capable by themselves of providing a VR experience, without using the options available on your smartphone, should be continued. 6.3. Limitations and future research It is expected that people with interest and experience with virtual reality nature, ecotourism and sustainability are the people who provide comments on VR videos, but we do not know the consumer profile of VR video content, so it is impossible to assess the representativeness of the sample or the generalizability of the findings. Important limitations of using SA to analyze video comments include the need for data cleaning and the insufficient development of theoretical frameworks and recommendations on how to get the most out of these tools. As demonstrated in the present study, there are few high-quality VR videos. One of the reasons behind this trend is the low popularity of visualization devices and the scarcity of specialized channels. Future research may include some experimental control and modality manipulation (e.g., 360 VR vs. conventional video vs. still photos) so that they can test the impact of the VR format on viewer experience and future intentions and behavior. In the lab, an experiment can be conducted with a control group viewing the VR videos on their computer or whatever device they have and another group with access to high-end devices to compare the results that can be obtained. Conflict of interest The authors declare that there is no conflict of interests regarding the publication of this paper. References 1. A. Porreca, F. Scozzari, M. Di Nicola, Using text mining and sentiment analysis to analyze YouTube Italian videos concerning vaccination, BMC Public Health, 20 (2020), 1–9. https://doi.org/10.1186/s12889-020-8342-4 2. Z. Li, R. Li, G. 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