Crawler Tool Research Articles

Abstract 6202: Integrating public single-cell transcriptomics and patient profiles to guide clinical development

Abstract Background: Single-cell technologies provide invaluable insights into disease biology and drug development by revealing complex interactions among different cell types within patients. However, harnessing the potential of publicly available single-cell data remains challenging due to the lack of integrated data across diverse single cell platforms. Methods: To maximize the potential of single cell insights, we have created an AI/ML powered curation and data integration process within our Drug Intelligence Science (DIS®) platform. This automated process integrates single cell transcriptomic data from publicly available sources with our in-house generated datasets from ex vivo translational efforts and clinical programs. Relevant public datasets are identified and retrieved using an automated Python-based GEO dataset crawler tool, and standardized metadata are curated. A reference atlas of 31 immune and 4 non-immune cell types is generated from the database and is used to consistently label cells across datasets. Additionally, cell-level gene expression integration and normalization across datasets is achieved using generative modeling techniques. To facilitate multiuser visualization and interrogation of the curated and integrated datasets, we have developed Cellect, a customized version of CellxGene Gateway. Results: The current single-cell database comprises 15 million cells analyzed in >5,000 samples collected from >2,500 patients across 160 curated unique human studies, spanning oncology (56%), autoimmune disease (AID, 21%), and viral infection (15%), with the remaining being from reference healthy tissues (8%). We focused dataset curation on indications of interest for our internal clinical development efforts. Uniquely, data from approximately 300 patients who received standard of care (SOC) treatment including immune checkpoint inhibitor (IO) therapy were integrated to delineate mechanisms of drug response and resistance. The post-SOC resource was used to identify (1) treatment settings where these mechanisms are enriched and (2) markers co-expressed with targets of interest (e.g., PD1, CTLA4, OX40, TNFR2, etc.) in specific cell populations to inform combination strategies. Conclusions: We have presented an AI/ML guided approach to address the key challenge of integrating single-cell data across platforms and demonstrated that relevant disease biology is retained upon integration. We outline a path for deploying this solution at scale for bench and computational scientists to guide target as well as indication selection, as was done for our ongoing clinical programs, including our first-in-class TNFR2 agonist (HFB200301, NCT05238883) and second-generation OX40 agonist (HFB301001, NCT05229601). Citation Format: Jack Russella-Pollard, Joshua Whitener, Jordan Byck, Monika Manne, Hani Alostaz, Marianna Elia, Kamil Krukowski, Gabrielle Wong, Francisco Adrian, Liang Schweizer, Robert H. Andtbacka, Christos Hatzis. Integrating public single-cell transcriptomics and patient profiles to guide clinical development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6202.

Emotional Expression and Information Communication in English Texts Based on Artificial Intelligence Technology

Abstract This paper firstly researches English text emotion expression and information communication, classifies English text emotion expression and information communication according to the human emotion-value relationship, and summarizes the characteristics of English emotion expression and information communication. Secondly, using artificial intelligence technology, it is proposed to construct an analysis model for English text emotion and information communication using the BiLSTM neural network. To deal with the characteristics of English text quickly and efficiently, it is necessary to encode the emotional information of English text, and based on encoding, the BiLSTM neural network is applied to extract the emotional features of English text and solve the problem of the loss of emotional features through the loss function. Then, the crawler tool is used to obtain the dataset from the Chinese English module under the MOOC of Chinese universities, and the evaluation indexes are set according to the model’s performance, followed by the experimental analysis of the English text emotion expression and information conveyance. The results show that compared with the original CNN, LSTM, and T-LSTM, the BiLSTM-based neural network performs better in the task of text emotion expression and information conveyance, with the accuracy rate staying above 0.925, and the effect on the English dataset is a bit better than that on the Chinese dataset. This study aims to enhance English teaching and communication between Chinese and foreign cultures.

Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling.

Social networks have become one of the main channels for obtaining health information. However, they have also become a source of health-related misinformation, which seriously threatens the public's physical and mental health. Governance of health-related misinformation can be implemented through topic identification of rumors on social networks. However, little attention has been paid to studying the types and routes of dissemination of health rumors on the internet, especially rumors regarding health-related information in Chinese social media. This study aims to explore the types of health-related misinformation favored by WeChat public platform users and their prevalence trends and to analyze the modeling results of the text by using the Latent Dirichlet Allocation model. We used a web crawler tool to capture health rumor-dispelling articles on WeChat rumor-dispelling public accounts. We collected information from health-debunking articles posted between January 1, 2016, and August 31, 2022. Following word segmentation of the collected text, a document topic generation model called Latent Dirichlet Allocation was used to identify and generalize the most common topics. The proportion distribution of the themes was calculated, and the negative impact of various health rumors in different periods was analyzed. Additionally, the prevalence of health rumors was analyzed by the number of health rumors generated at each time point. We collected 9366 rumor-refuting articles from January 1, 2016, to August 31, 2022, from WeChat official accounts. Through topic modeling, we divided the health rumors into 8 topics, that is, rumors on prevention and treatment of infectious diseases (1284/9366, 13.71%), disease therapy and its effects (1037/9366, 11.07%), food safety (1243/9366, 13.27%), cancer and its causes (946/9366, 10.10%), regimen and disease (1540/9366, 16.44%), transmission (914/9366, 9.76%), healthy diet (1068/9366, 11.40%), and nutrition and health (1334/9366, 14.24%). Furthermore, we summarized the 8 topics under 4 themes, that is, public health, disease, diet and health, and spread of rumors. Our study shows that topic modeling can provide analysis and insights into health rumor governance. The rumor development trends showed that most rumors were on public health, disease, and diet and health problems. Governments still need to implement relevant and comprehensive rumor management strategies based on the rumors prevalent in their countries and formulate appropriate policies. Apart from regulating the content disseminated on social media platforms, the national quality of health education should also be improved. Governance of social networks should be clearly implemented, as these rapidly developed platforms come with privacy issues. Both disseminators and receivers of information should ensure a realistic attitude and disseminate health information correctly. In addition, we recommend that sentiment analysis-related studies be conducted to verify the impact of health rumor-related topics.

Analyzing Marketing Issues in China's Juice Market Using Visualization Tools

China's juice market has been slow in recent years, and the market share is gradually decreasing. Based on the importance and research value of online reviews on e-commerce platforms today, this paper uses the python crawler tool to crawl the top three 100% juice product reviews on the JD platform, extract new words and keywords, and analyze positive and negative emotions, and then visualize them. According to the research results, it is suggested that fruit juice enterprises carry out taste innovation; Optimize packaging design, materials, and specifications; and make suggestions such as timely promotion activities on holidays. It aims to put forward targeted tips for recovering China's juice market.

THE OVERSEAS DISSEMINATION AND RECEPTION OF THE TRANSLATION OF THE QUOTATIONS OF MAO ZEDONG: BASED ON AMAZON READER REVIEWS

For a long time, as the main object of foreign translation and dissemination of Chinese books, The Quotations of Mao Zedong has had a remarkable impact on China and the world. The comments and evaluations of foreign readers are an important aspect to study and measure the effectiveness of the work’s foreign translation and distribution. In this paper, with the help of data crawler tool and text mining tool ROST CM6, a quantitative and qualitative study on the reader reviews of the translation of The Quotations of Mao Zedong on Amazon platform is conducted. The trends of star ratings, emotional attitudes and number of reviews show that most readers have positive attitudes toward it, and give it positive evaluations. Readers’ motivation for reading is based not only on circulation but also on personal interests such as historical, philosophical or study needs; at the same time, its political ideology is another important reason. Although there are flaws in the quality of book design and the language quality of the translation, the overseas dissemination of the translation of The Quotations of Mao Zedong is on the rise and the overall acceptance is good.

Improving the Communication and Credibility of Government Media in Response to Public Health Emergencies: Analysis of Tweets From the WeChat Official Accounts of 10 Chinese Health Commissioners.

BackgroundA significant public health emergency has appeared worldwide since the beginning of 2020. The spread of negative information about COVID-19 on social media poses a challenge and threat to public health disposition and the credibility of government public opinion.ObjectiveThis study aimed to analyze the rules and characteristics of government media in disseminating information on public emergencies. In addition, find ways and means to improve government media's communication power and credibility.MethodBased on relevant theories and measures of information econometrics, 10 WeChat official accounts of the Chinese government were taken as examples. The Python crawler tool was used to collect data of 10 WeChat official accounts-related tweets. In addition, this study used various tools, such as ROST, UCINET, and SPSS, for statistical analysis and co-word analysis of the data.ResultFrom January 17 to March 31, 2020, 6,612 COVID-19-related tweets were published by 10 WeChat official accounts, which broadcast epidemic overview, epidemic prevention and control, science and disinformation, epidemic assistance, epidemic impact, and negative impact. By analyzing the posting time and content of the tweets, we found that changes in the number of articles posted by the WeChat and changes in content and the progress of the COVID-19 pandemic are nearly synchronized, and most tweets are published at 8:00 am. Furthermore, based on the analytics of high dissemination index and high-frequency words, we propose that there is a significant correlation between the strength of independence and the credibility of the WeChat official account.ConclusionThe three elements of WeChat communication (value, interest, and moving) and the degree of independent innovation of public numbers impact the communication power and credibility of government media. First, if the articles published by the WeChat official account are valuable, interesting, and moving, the communication power of the WeChat official account would get more powerful. Second, increased ability for independent innovation has a positive impact on enhancing the WeChat official account's credibility. Third, government media can improve its governance effects of public health emergencies by enhancing their communication power and credibility.

Impact of climate change on catchment nutrient dynamics: insights from around the world

This study is a meta-analysis of global articles on hydrological nutrient dynamics to determine trends and consensus on: (1) the effects of climate change-induced hydrological and temperature drivers on nutrient dynamics and how these effects vary along the catchment continuum from land to river to lake; (2) the convergence of climate change impacts with other anthropogenic pressures (agriculture, urbanization) in nutrient dynamics; and (3) regional variability in the effects of climate change on nutrient dynamics and water-quality impairment across different climate zones. An innovative web crawler tool was employed to help critically synthesize the information in the literature. The literature suggests that climate change will impact nutrient dynamics around the globe and exacerbate contemporary water-quality challenges. Nutrient leaching and overland flow transport are projected to increase globally, promoted by extreme precipitation. Seasonal variations in streamflow are expected to emulate changing precipitation patterns, but the specific local impacts of climate change on hydrology and nutrient dynamics will vary both seasonally and regionally. Plant activity may reduce some of this load in nonagricultural soils if the expected increase in plant uptake of nutrients prompted by increased temperatures can compensate for greater nitrogen (N) and phosphorus (P) mineralization, N deposition, and leaching rates. High-temperature forest and grass fires may help reduce mineralization and microbial turnover by altering N speciation via the pyrolysis of organic matter. In agricultural areas that are at higher risk of erosion, extreme precipitation will exacerbate existing water-quality issues, and greater plant nutrient uptake may lead to an increase in fertilizer use. Future urban expansion will amplify these effects. Higher ambient temperatures will promote harmful cyanobacterial blooms by enhancing thermal stratification, increasing nutrient load into streams and lakes from extreme precipitation events, decreasing summer flow and thus baseflow dilution capacity, and increasing water and nutrient residence times during increasingly frequent droughts. Land management decisions must consider the nuanced regional and seasonal changes identified in this review (realized and predicted). Such knowledge is critical to increasing international cooperation and accelerating action toward the United Nations’s global sustainability goals and the specific objectives of the Conference of Parties (COP) 26.

Using data mining technology to analyse the spatiotemporal public opinion of COVID-19 vaccine on social media

PurposeThe deployment of vaccines is the primary task in curbing the COVID-19 pandemic. The purpose of this paper is to understand the public’s opinions on vaccines and then design effective interventions to promote vaccination coverage.Design/methodology/approachThis paper proposes a research framework based on the spatiotemporal perspective to analyse the public opinion evolution towards COVID-19 vaccine in China. The framework first obtains data through crawler tools. Then, with the help of data mining technologies, such as emotion computing and topic extraction, the evolution characteristics of discussion volume, emotions and topics are explored from spatiotemporal perspectives.FindingsIn the temporal perspective, the public emotion declines in the later stage, but overall emotion performance is positive and stabilizing. This decline in emotion is mainly associated with ambiguous information about the COVID-19 vaccine. The research progress of vaccines and the schedule of vaccination have driven the evolution of public discussion topics. In the spatial perspective, the public emotion tends to be positive in 31 regions, whereas local emotion increases and decreases in different stages. The dissemination of distinctive information and the local epidemic prevention and control status may be potential drivers of topic evolution in local regions.Originality/valueThe analysis results of media information can assist decision-makers to accurately grasp the subjective thoughts and emotional expressions of the public in terms of spatiotemporal perspective and provide decision support for macro-control response strategies and risk communication.

On-chain analysis-based detection of abnormal transaction amount on cryptocurrency exchanges

Cryptocurrency exchanges play an indispensable role in the cryptocurrency market. However, some exchanges are suspected to be involved in various abnormal or malicious behaviors while providing services to users, such as money laundering, wash trading and even running away. Besides, these behaviors are reported to be often accompanied by an anomalous increase in the transaction amount. Therefore, it is a topic worthy of study to detect whether the abnormal transaction amount occurs in the exchange and when it occurs. This paper uses web crawler tools to collect a relatively complete dataset of exchanges and then conducts a correlation analysis to obtain the most important factors that influence the transaction amount of different exchanges. Then, the prediction model of the influence of various factors on the transaction amount is obtained based on deep learning. The deviation between the predicting transaction amount and the actual transaction amount is calculated to provide a basis for abnormal transaction amount detection. Finally, through a case study on the detection results, some abnormal transaction amounts are related to policy changes and industry events, while the others are suspected to be related to illegal behaviors.

Analysis Comparissons of Ereciton Girder Implementations Methods Using Laucher Gantry and Crawler Crane Based on Cost and Time

Toll road infrastructure work in recent years has been carried out, one of which is cimanggiscibitung toll road project which has Elevated work that requires methods that must be considered. This final task aims to find out the comparison in terms of cost and time of erection girder work method using Gantry Launcher and Crawler Crane and compare the two effective tools in erection girder work in Cimanggis-Cibitung Toll Road Construction project Section II Cibubur Area. The method of data collection is primary data and secondary data, then perform a comparative analysis in terms of cost and time on the erection girder method using Launcher Gantry and Crawler Crane by calculating cycle time and price unit index. Then the researchers used quantitative research method by disseminating questionnaires to experts who are employees of PT. Waskita Karya Tbk. who works in cimanggi-cibitung toll road construction project section II Cibubur area with variables that affect the effectiveness of Gantry Launcher and Crawler Crane tools on Erection Girder work. Then the data of thequestionnaire results are made for conclusion. The results of this study showed that erection girder method using Crawler Crane is faster and cheaper compared to Erection Girder using Gantry Launcher. Then from the results of the questionnaire Gantry Launcher tool gets a presentation of 80%. Of the 10 variables the question has already been asked. and from the results of the questionnaire crane crawler tool obtained a percentage of 88% of the 10 variables of questions already asked. Keywords: Erection Girder, Launcher Gantry, Crawler Crane.

Application of Keras neural network in the era of big data

The experiment uses crawler tools to obtain data, and the data is preprocessed to find missing values and eliminate invalid data, meanwhile, the model is constructed by information entropy and ID3 algorithm so as to select the desired amount of features, and then basic modeling and data filtering is performed to train and evaluate the model for the first time, finally, in order to get a more ideal model, this experiment The optimal model is obtained by changing the number of hidden layers and neurons of the neural network to build a high-level neural network API neural network model written by pure python - Keras neural network model. The results show that when the model defines a 2-layer neural network and the number of neurons in the hidden layer is fourteen, the accuracy of the model is the highest, and the accuracy of the test set is as high as ninety-one percent.

Portrayals of 2v, 4v and 9vHPV vaccines on Chinese social media: a content analysis of hot posts on Sina Weibo

ABSTRACT Rather than receive the effective 2vHPV vaccines that are readily available in China, Chinese women usually wait to receive 4v and 9vHPV vaccines, which are difficult to acquire. This means that Chinese women miss the opportunity for optimal protection from cervical cancer. As social media platforms are the main channel by which Chinese women learn about HPV vaccines, this study aimed to explore how HPV vaccines are described on social media, and in particular how they discuss or distinguish 2 v, 4 v and 9vHPV vaccines. The Octopus Web crawler tool was used to capture hot Weibo posts from 2013–2021, and 1,164 valid data were obtained. Results suggested that there are very few posts with great influence on Weibo about HPV vaccines among 9 years and much of them are created by “lay people.” HPV-related topics lacked persistent popularity, comprised highly repetitive content and the spread of information was geographically diverse. There were significant differences in the media descriptions of different kinds of HPV vaccines. Price was mentioned more often in the descriptions of 2vHPV vaccines, whereas appointments were referred to most often in the descriptions of 9vHPV vaccines. There was little media attention paid to the safety and effectiveness of HPV vaccines. Chinese media should develop better collaborations with public health professionals, pay more attention to the originality of their news coverage of HPV vaccines and strive to promote HPV vaccination. Such collaboration will help news media to better understand the key points of HPV information that need to be disseminated.

Research on the Evaluation of Customer requirement Importance of Smart Products Based on Online Comments and Improved Quantitative Kano Model

With the development and popularization of smart products, the technological differences of products are decreasing, and the phenomenon of product homogeneity is becoming more and more obvious. It is necessary for the smart product manufacturing firms have the capability to analyze customer requirement deeply and adapt to the dynamically changing market quickly. Therefore, the traditional technology-oriented product development model is no longer suitable for manufacturers to obtain a competitive advantage. Based on this, this paper proposed a method to evaluate the importance of customer demands based on online comments and quantitative Kano model. First, the Python crawler tool is used to obtain online customer reviews of relevant products and the word segmentation processing is performed to obtain the product features and frequency that customers are mainly concerned about, and then the initial importance of demand can be calculated. Furthermore, use the quantitative Kano model to determine the customer satisfaction and revise the initial importance of the requirements to obtain a more reasonable ranking of the importance of user needs. Finally, a case study is carried out with the smart bracelet as an example to verify the effectiveness and feasibility of the model proposed in this paper.

Evaluation and Design Method for Product Form Aesthetics Based on Deep Learning

Currently, evaluations of products from aesthetics are mostly carried out with knowledge expressions of aesthetic features as tools, achieving remarkable results. However, obtaining a large aesthetic feature vocabulary is a challenge because of the experience of researchers and the comprehension abilities of subjects. In addition, due to manual feature extraction, the sample sizes of experimental dataset are generally small, leading to results with poor generalization. To address this problem, a method of aesthetic evaluation and form design for products based on deep learning was proposed. First, a crawler tool was used to collect the front images of cars with corresponding appearance ratings, and a dataset was constructed with users’ intuitive and simple ratings as the labels. A deep convolutional neural network (CNN) was designed, and a grading threshold was used as the classification basis. During the process of training the network, batch normalization and other methods were used to optimize the network, and good classification effects were achieved. Based on the above work, an adversarial neural network was used for the aesthetic design of a product form, a shape sketch of an automobile front face was generated, the proposed evaluation model was used to evaluate it, and the result obtained was excellent. These results show that the method used in this study can correctly evaluate product form aesthetics and then generate a design scheme with a high aesthetic level, thereby providing powerful technical support for the intelligent design of product forms.

Intelligent Acquisition and Analysis of Commodity Review Data

When shopping on online e-commerce platforms as they can only contact products through photos and other means, it is easy to have cognitive deviations on the products on online platforms. This paper makes intelligent acquisition and analysis of commodity review data by using web crawler tools. Through a series of operations such as comment data capture, data preprocessing, Chinese word segmentation, emotional tendency analysis, LDA model analysis, it analyzes buyers’ emotional tendency of this commodity to help consumers choose the commodities they really like.

Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data.

BackgroundThe COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited.ObjectiveThe aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic.MethodsWe used a web crawler tool and a set of predefined search terms (New Coronavirus Pneumonia, New Coronavirus, and COVID-19) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China.ResultsBased on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients’ outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation.ConclusionsConcerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics.

Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics.

COVID-19 blocked Wuhan in China, which was sealed off on Chinese New Year's Eve. During this period, the research on the relevant topics of COVID-19 and emotional expressions published on social media can provide decision support for the management and control of large-scale public health events. The research assisted the analysis of microblog text topics with the help of the LDA model, and obtained 8 topics ("origin", "host", "organization", "quarantine measures", "role models", "education", "economic", "rumor") and 28 interactive topics. Obtain data through crawler tools, with the help of big data technology, social media topics and emotional change characteristics are analyzed from spatiotemporal perspectives. The results show that: (1) "Double peaks" feature appears in the epidemic topic search curve. Weibo on the topic of the epidemic gradually reduced after January 24. However, the proportion of epidemic topic searches has gradually increased, and a "double peaks" phenomenon appeared within a week; (2) The topic changes with time and the fluctuation of the topic discussion rate gradually weakens. The number of texts on different topics and interactive topics changes with time. At the same time, the discussion rate of epidemic topics gradually weakens; (3) The political and economic center is an area where social media is highly concerned. The areas formed by Beijing, Shanghai, Guangdong, Sichuan and Hubei have published more microblog texts. The spatial division of the number of Weibo social media texts has a high correlation with the economic zone division; (4) The existence of the topic of "rumor" will enable people to have more communication and discussion. The interactive topics of "rumors" always have higher topic popularity and low emotion text expressions. Through the analysis of media information, it helps relevant decision makers to grasp social media topics from spatiotemporal characteristics, so that relevant departments can accurately grasp the public's subjective ideas and emotional expressions, and provide decision support for macro-control response strategies and measures and risk communication.

Destination Image Recognition And Emotion Analysis: Evidence From User-Generated Content Of Online Travel Communities

Abstract The tourism destination image is an intangible value that enhances the internal and external spiritual value of the region. To improve tourist experiences and provide reference for relevant departments, we applied the GooSeeker web data crawler tool and Python data mining kit to crawl and analyze the representative online tourism community data. We conduct an empirical analysis through data from the online tourist community ‘mafengwo’. The result, based on the user-generated content data analysis of online travel community, shows that the tourists' perception of the destination image, cognitive theme and emotional experience has different effects on the tourist experience. This research offers insights into destination image cognitive theme and traveler behavior habits, which can provide guidance for platform and destination managers.

Exploration of the Application of Virtual Reality and Internet of Things in Film and Television Production Mode

In order to reduce some of the problems of technological restructuring and insufficient expansion in the current film and television production mode, the application of emerging technologies such as artificial intelligence (AI), virtual reality (VR), and Internet of Things (IoT) in the film and television industry is introduced in this research. First, a topical crawler tool was constructed to grab relevant texts about “AI”, “VR”, and “IoT” crossover “film and television”, and the grasping accuracy rate and recall rate of this tool were compared. Then, based on the extracted text, the data of recent development in related fields were extracted. The AdaBoost algorithm was used to improve the BP (Back Propagation) neural network (BPNN). This model was used to predict the future development scale of related fields. Finally, a virtual character interaction system based on IoT-sensor technology was built and its performance was tested. The results showed that the topical crawler tool constructed in this study had higher recall rate and accuracy than other tools, and a total of 188 texts related to AI, VR, and IoT crossover television films were selected after Naive Bayes classification. In addition, the error of the BPNN prediction model based on the AdaBoost algorithm was less than 20%, and it can effectively predict the future development scale of AI and other fields. In addition, the virtual character interaction system based on IoT technology constructed in this study has a high motion recognition rate, produces a strong sense of immersion among users, and can realize real-time capture and imitation of character movements. In a word, the field of AI and VR crossover film and television has great development prospects in the future. Therefore, the application of IoT technology in building the virtual-character interaction system can improve the effect of VR or AI film and television production.

Research topic: Tourists’ preference for public space based on Big Data online. Case study: Fuzhou National Forest Park

Taking the Fuzhou National Forest Park as the research sample, this study uses the crawler tool to capture the comments of tourists on the network of Fuzhou National Forest Park, and uses ROST6.0 information mining software to analyze the emotional distribution and classification of word frequency. When analysed, the results showed that:(1) On the whole, tourists showed positive emotions towards Fuzhou National Forest Park, and the proportion of their neutral emotions and negative emotions was small.(2) Visitors’ favourite activities were “barbecuing”, “mountain hiking”, “leisure”, “walking”, “sightseeing”, “playing games”, “exercising”, “tourist-visit”, “entertainment” and “picnicking”.(3) Visitors’ favourite scenic spots in turn were “Banyan”, “Sakura blossoms”, “Peach Blossom”, “Bird Language Forest”, “Longtan pond”, “trees”, “Botanical Garden”, “waterfall”, “vegetation”, “reservoir”, “garden”, “museum”, “Bamboo forest” and “Plank road”(4) In the insufficiency aspect, the Fuzhou National Forest Park had the situation where there are too many people queuing up at certain times of the day.Finally, a set of public space management mechanism based on Big Data is summarized, which provides theoretical support for its management and optimization.