ABSTRACT
In this paper, we develop a daily-frequency measure of economic and policy uncertainty for Chile, employing information obtained from Twitter accounts using web scraping techniques and following closely the methodology proposed by Baker et al. (2016). Our proposed measure, called DEPUC, aims to capture the level of general disagreement –a proxy for economic and policy uncertainty– in topics such as the economy, economic policies, uncertainty about particular events, and Chile's conjuncture situation. The index, available from 2012 onwards, shows significant hikes that coincide with several local and international episodes that provoked extraordinary levels of uncertainty in Chile, especially after the events around the civil protests in mid-October 2019 and the start of the COVID-19 pandemic in mid-March 2020. An empirical exercise reveals that the proposed measure is a significant determinant of the nominal exchange rate dynamics, especially when this variable's magnitude is high and a week after the shock occurs. On the contrary, when the exchange rate is low, the impact of uncertainty on this variable is quantitatively smaller for any forecasting horizon. These features, and others discussed in the paper, highlight the usefulness of the proposed metric as an additional indicator that policymakers can incorporate into their monitoring toolkit. © 2023, Revista de Analisis Economico. All Rights Reserved.
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The consensus in the literature on providing accurate inflation forecasts underlines the importance of precise nowcasts. In this paper, we focus on this issue by employing a unique, extensive dataset of online food and non-alcoholic beverages prices gathered automatically from the webpages of major online retailers in Poland since 2009. We perform a real-time nowcasting experiment by using a highly disaggregated framework among popular, simple univariate approaches. We demonstrate that pure estimates of online price changes are already effective in nowcasting food inflation, but accounting for online food prices in a simple, recursively optimized model delivers further gains in the nowcast accuracy. Our framework outperforms various other approaches, includ-ing judgmental methods, traditional benchmarks, and model combinations. After the outbreak of the COVID-19 pandemic, its nowcasting quality has improved compared to other approaches and remained comparable with judgmental nowcasts. We also show that nowcast accuracy increases with the volume of online data, but their quality and relevance are essential for providing accurate in-sample fit and out-of-sample nowcasts. We conclude that online prices can markedly aid the decision-making process at central banks.(c) 2022 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
ABSTRACT
Automation has been into existence since the mid Fifties but had simplest began to gain attention lately. The RPA software program makes use of existing generation's interface to automate the human detail in the technique. So, essentially, there's no want for human intervention. web scraping is a software of robot system Automation that is used in almost all of the industries. either or not it's a e-trade internet site, commodities buying and selling web sites, or any internet site and so forth. you can scrape the information from any of them based on your hobby. Now, the problem with guide scraping by hand is that it's miles at risk of mistakes and takes numerous times. also, the facts available on websites does now not change in any respect. up to date regularly, for this reason facts saved domestically might not usually be terrible. So, industries can actually automate this mission. The main objective of the project is to save time and send the updated information to the person using RPA technology. As this COVID-19 Global Pandemic going on, we thought of creating a project around COVID-19. So, in the project we will use Data Scrapping to extract Web Table (which contains COVID-19 data such as number of affected people, recovered people etc.) from web page. And, write the extracted data into Excel then we will send that excel over the email as attachment. In this project we researched how we can send data through email using RPA and extract the data from live covid_19 website. For software automation, there are many software's that are available in market. The main RPA vendors are UiPath, Automation Anywhere, and Blue Prism. So, to complete our RPA project, we have chosen UiPath which the best in the field of automation. You should be familiar with at least one of these tools before working on the following projects. This paper aims to provide RPA reviews as technical, as well as its implementation applications. © 2022 IEEE.
ABSTRACT
While official statistics provide lagged and aggregate information on the housing market, extensive information is available publicly on real-estate websites. By web-scraping them for the UK on a daily basis, this paper extracts a large database from which we build timely and highly granular indicators. One originality of the dataset is to focus on the supply side of the housing market, allowing to compute innovative indicators reflecting the sellers' perspective such as the number of new listings posted or how prices fluctuate over time for existing listings. Matching listing prices in our dataset with transacted prices from the notarial database, using machine learning, also measures the negotiation margin of buyers. During the Covid-19 crisis, these indicators demonstrate the freezing of the market and the "wait-and-see" behaviour of sellers. They also show that listing prices after the lockdown experienced a continued decline in London but increased in other regions. [ FROM AUTHOR] Copyright of Journal of Housing Economics is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
With the emergence of coronavirus and the rapidly increasing number of cases, we observed that people were suffering from a lack of information about where to source critical requirements such as oxygen cylinders, beds, ambulance service, and ventilators. In this research paper and project, the authors have designed and developed an interactive information dashboard to access the availability of these resources and requirements at different locations across India from myriad sources. The information dashboard will show information for all the states across India. The dashboard visualizes the number of corona cases, hospital beds, blood bank, etc. The end-users and the COVID-19 support team can imagine all the requirements in the dashboard that is factual and credible within their vicinity from multiple information sources. The user need not search various information sites to search for different conditions. The project collates the data of other requirements from primary sources of information like Twitter and government websites and lists them on the dashboard. The authors used open-source programming and database technologies to display the data per user requirements. The dashboard was helpful to the end-users during COVID-19 times in terms of convenient accessibility and efficiency. © 2022 IEEE.
ABSTRACT
Due to the Coronavirus (Covid-19) pandemic, there was a positive shift in online shopping. On an e-commerce website like Shopee, consumers may post comments under the products they have purchased. This research study aims to conduct sentiment analysis on product reviews as customer recommendations in Shopee Philippines. The product reviews were first scraped from Shopee. After which, it is preprocessed and then annotated using VADER. The customers' sentiments were analyzed using Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM). The confusion matrix and a classification report were then used to determine the accuracy value, the precision value, the recall value, and the F-measure value of the results from the models. Lastly, the results from a survey would justify the model's results to customer recommendations. The final results of the research study show how reviews with positive, negative, and neutral sentiments can affect a product's condition to be recommended to other consumers or not. Based on the analysis of the product reviews, 83.6% are positive, 9.1% are negative, and 7.3% are neutral. The SVM model is found to be a better model than MNB which got an 83% accuracy score. The survey results which validated the model's results have found that 75.8% of the respondents would recommend a store or a product based on the number of positive reviews. © 2022 IEEE.
ABSTRACT
While official statistics provide lagged and aggregate information on the housing market, extensive information is available publicly on real-estate websites. By web-scraping them for the UK on a daily basis, this paper extracts a large database from which we build timely and highly granular indicators. One originality of the dataset is to focus on the supply side of the housing market, allowing to compute innovative indicators reflecting the sellers' perspective such as the number of new listings posted or how prices fluctuate over time for existing listings. Matching listing prices in our dataset with transacted prices from the notarial database, using machine learning, also measures the negotiation margin of buyers. During the Covid-19 crisis, these indicators demonstrate the freezing of the market and the "wait-and-see” behaviour of sellers. They also show that listing prices after the lockdown experienced a continued decline in London but increased in other regions.
ABSTRACT
The primary means of informing the population in modern society is through news portals. This paper analyses the characteristics and effects that such way of communication creates. The influence was studied in particular on an example of current global phenomenon 'vaccination' (hr. cijepljenje). The research method follows the CRISP-DM process adapted to the digitalized form of textual data. The analysed corpus, in the form of natural spoken language, was scraped from Croatian news portals. The subsequent processing extracts information from unstructured textual sources and provides valuable insights, like how much a particular topic is represented in the article. Modeling is based on the application of multiple text mining algorithms, like Words Cloud, Topic Modelling, Concordance and Sentiment Analysis. The implemented model produces indicators for objective information interpretation. The findings suggest that the portals associated the notion of vaccination with the COVID-19 pandemic. Furthermore, this term was often used in a political context. The words used and predominantly negative character of texts dealing with vaccination has led to the transmission of negative emotions to readers. A significant aspect of the study is the fact that it was conducted on the corpus of texts written in Croatian - a relatively small and morphologically complex language. © 2022 University of Split, FESB.
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Instagram has gained the attention of hundreds of millions of users and evolved quickly into a critical customer engagement tool for businesses worldwide, more so during Covid-19. Impacts of Covid-19 have fundamentally changed the market, and therefore, this paper explores the relationship between Instagram practices and the engagement of 20 Australian SMEs (Small medium enterprises) pre and during Covid-19. This study aims to answer the following questions: (1) How should user-generated content (UGC) and call to act content (CTA) be included as Instagram posts? (2) How to use #Hashtags and @Tagging in Instagram posts to keep a campaign going? (3) How Instagram can be utilised to mitigate the effect of Covid-19? Findings revealed a statistically significant relationship between the number of UGCs to Instagram engagement, while CTA content performance recorded a mixed result. However, both UGCs and CTA positively affect the engagement when used to build a virtual community and engage with followers rather than redirecting customers to online selling locations. Also, diversity in @Tagging and #Hashtag uses are found to be effective drivers of engagement. The results imply that addressing the Covid-19 related concerns of followers while showing genuine brand social responsibility can be rewarded by extra engagement © 2022 authors. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 Australia License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and AJIS are credited
ABSTRACT
Social media have conquered the widest spectrum of our lives. Companies, following the phenomena of the new digital era, are giving up traditional practices and use new policies of diffusion for advertising products and for engaging potential customers. In the context of ENIRISST+ (https://enirisst-plus.gr ), we started investigating whether transportation businesses (i.e., ferry companies, airlines, etc.) use the new era practices for promoting their products and services and then, extended our research on all kind of businesses. The work presented in this paper studies this shift for the Greek Instagram and YouTube community, records and analyses the activity of prominent Greek companies on social media, and measures the social and commercial impact of the emerging COVID-19 pandemic during 2020 on Greek users' digital behaviour. Subsequently, we use the acquired data and analysis (i) to draw conclusions about the digital behaviour and preferences of the Greek social media scene and (ii) to compare the results in marketing policies and behavioural patterns on two inherently different social media platforms. This is the first study in the literature to perform an analysis on the behaviour of the Greek community in different social media.
ABSTRACT
Due to the onset of the Covid-19 pandemic, people are compelled to maintain social distance in all spheres of life, forcing people to adopt virtual mode of activity. Usage of social media and other internet activity has shot up in this period, and consequently, cybercrimes have also increased. If cybercrimes are reported, computer forensics analysts will examine the concerned website, online forum, or social media to find meticulous details about the cybercrime. But webpage content seen on the day may not be available on the next day. The contents of the webpage, which is the subject of crime, will be deleted or withdrawn, or deactivated to destroy evidence to escape from legal proceedings. The victims usually produce a screenshot of the webpage or image or video as a piece of evidence. But there is a distinct possibility of manipulating the offensive materials and it may not be considered a valid piece of evidence before the court of law. Such a scenario requires a forensic technique that should acquire the content of the webpage before it is removed from web site to maintain the authenticity of captured data. So, we are proposing an automated system for the forensic acquisition of a website that will effectively capture all content from the live website and make it useful for forensic investigation and may be produced before the court as valid evidence of cybercrime. © 2022 IEEE.
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Mobile applications (apps) are becoming an essential tool when it comes to sightseeing. There is even a specific category for trips in the leading app stores. These are no strangers to the rise of the itinerant travel style, the caravans. The study aims to understand the situation of the main caravanning apps in Spain. We have carried out a web scraping methodology using a sample of 1,601 Spanish reviews of the main apps related to caravanning. The most interesting findings, among others, are that we are getting to know a sector that up to now was unknown and that even has not been affected by the pandemic crisis. Besides, the paper has demonstrated that developers do not follow the right strategies in caravanning apps. The paper also shows users' most crucial concerns about these apps. Therefore, managers of caravanning apps could improve their strategies by focusing their attention on users' concerns and, most important, reviews to respond.
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With the widespread use of social media platforms within our modern society, these platforms have become a popular medium for disseminating news across the globe. While some of these platforms are considered reliable sources for sharing news, others publicize the information without much validation. The transmission of fake news on social media impacts people's behavior and negatively influences people's decisions. During the COVID-19 outbreak, it was more evident than ever. This has led to a demand for conducting research studies to explore sophisticated approaches to assess the integrity of news worldwide. The main objective of this research paper was to outline our proposed experimental methodology to detect and access fake news using Data Mining and Natural Language Processing. The presented research effort provides a method to verify the authenticity of the news disseminated in social networks by dividing the process into four significant stages: news aggregation, publication collection, data analysis, and matching results. © 2022 ACM.
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Currently, the Dark Web is one key platform for the online trading of illegal products and services. Analysing the.onion sites hosting marketplaces is of interest for law enforcement and security researchers. This paper presents a study on 123k listings obtained from 6 different Dark Web markets. While most of current works leverage existing datasets, these are outdated and might not contain new products, e.g., those related to the 2020 COVID pandemic. Thus, we build a custom focused crawler to collect the data. Being able to conduct analyses on current data is of considerable importance as these marketplaces continue to change and grow, both in terms of products offered and users. Also, there are several anti-crawling mechanisms being improved, making this task more difficult and, consequently, reducing the amount of data obtained in recent years on these marketplaces. We conduct a data analysis evaluating multiple characteristics regarding the products, sellers, and markets. These characteristics include, among others, the number of sales, existing categories in the markets, the origin of the products and the sellers. Our study sheds light on the products and services being offered in these markets nowadays. Moreover, we have conducted a case study on one particular productive and dynamic drug market, i.e., Cannazon. Our initial goal was to understand its evolution over time, analyzing the variation of products in stock and their price longitudinally. We realized, though, that during the period of study the market suffered a DDoS attack which damaged its reputation and affected users' trust on it, which was a potential reason which lead to the subsequent closure of the market by its operators. Consequently, our study provides insights regarding the last days of operation of such a productive market, and showcases the effectiveness of a potential intervention approach by means of disrupting the service and fostering mistrust. © 2022 IEEE.
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The Covid-19 pandemic has had a profound effect on our daily lives. One of the most effective ways to protect ourselves from this virus is to wear face masks. This research paper introduces face mask detection that authorities can use to reduce and prevent COVID-19. The face mask recognition process in this research paper is done with a deep learning algorithm and image processing done using MobileNetV2. Steps to build the model are data collection, pre-processing, data classification, model training and model testing. The authors came up with this approach due to the recent Covid-19 situations for following specific guidelines and the uprising trend of Artificial Intelligence and Machine Learning and its real-world practices. This system has been made to detect more than one person whether they are wearing masks or not. This system also gives us the Covid cases-related worldwide updates as per our chosen country and type of cases like total cases, total deaths etc. Such systems are already available, but the efficiency of the available mask detection systems was not achieved thoroughly. This newly developed system proposes to take a step further, which recognizes more than one person at a time and increases the accuracy level to a much greater extent. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Forecasting the stock market is one of the most difficult undertakings in the financial industry due to its complex, volatile, noisy, and nonparametric character. However, as computer science advances, an intelligent model can help investors and analysts minimize investment risk. Public opinion on social media and other online portals is an important factor in stock market predictions. The COVID-19 pandemic stimulates online activities since individuals are compelled to remain at home, bringing about a massive quantity of public opinion and emotion. This research focuses on stock market movement prediction with public sentiments using the long short-term memory network (LSTM) during the COVID-19 flare-up. Here, seven different sentiment analysis tools, VADER, logistic regression, Loughran-McDonald, Henry, TextBlob, Linear SVC, and Stanford, are used for sentiment analysis on web scraped data from four online sources: stock-related articles headlines, tweets, financial news from "Economic Times" and Facebook comments. Predictions are made utilizing both feeling scores and authentic stock information for every one of the 28 opinion measures processed. An accuracy of 98.11% is achieved by using linear SVC to calculate sentiment ratings from Facebook comments. Thereafter, the four estimated sentiment scores from each of the seven instruments are integrated with stock data in a step-by-step fashion to determine the overall influence on the stock market. When all four sentiment scores are paired with stock data, the forecast accuracy for five out of seven tools is at its most noteworthy, with linear SVC computed scores assisting stock data to arrive at its most elevated accuracy of 98.32%.
ABSTRACT
The COVID-19 pandemic has been a menace to the World. According to WHO, a mortality rate of 1.99% is reported as of 28th November 2021. The need of the hour is to implement certain safety measures that may not eradicate but at least put a restriction on the rising number of COVID-19 cases all over the World. To ensure that the COVID-19 protocols are being abided by, a Convolutional Neural Network (CNN)-based framework "Co-Yudh" is being developed that comprises features like detecting face masks and social distancing, tracking the number of COVID-19 cases, and providing an online medical consultancy. The paper proposes two algorithms based on CNN for implementing the above features such as real-time face mask detection using the Transfer Learning approach in which the MobileNetV2 model is used which is trained on the Simulated Masked Face Dataset (SMFD). Further, the trained model is evaluated on the novel dataset-Mask Evaluation Dataset (MED). Additionally, the YOLOv4 model is used for detecting social distancing. It also uses web scraping for tracking the number of COVID-19 cases which updates on a daily basis. This is an easy-to-use framework that can be installed in various workplaces and can serve all the purposes to keep a check on the COVID-19 protocols in the area. Our preliminary results are quite satisfactory when tested against different environmental variables and show promising avenues for further exploration of the technique. The proposed framework is a more improved version of the existing works done so far.
ABSTRACT
Cooking has played an essential role in the growth of culture and civilization for the past 1.8 million years. However, the lockdown in various countries, including Germany, has prompted people to improve their health and well-being due to the coronavirus pandemic. While doing this, searching for recipes becomes one of the popular and essential activities as it allows people worldwide to prepare dishes from various countries. But finding recipes on the internet is like searching in the wild with thousands of recipes available for a single dish. Traditional recipes are essential in a human being’s life. However, for students away from home or working young people who have little time to cook, many recipes have been forgotten for a long time. Therefore, MISOhungry gives solutions to both the user groups through this platform. The recipes provided are by scraping data from online food blogs to create recipes complete with ingredients nutritional information. On the same site, youngsters may also access traditional recipes provided by the elderly. Studies show that sharing recipes linked with memories stimulates generative activity in older adults and makes them happy later. The study demonstrates that the platform is accessible to both user groups, young people are interested in receiving traditional recipes, and they would like to use this platform which directly bridges the generation gap in recipe sharing, search, and management. MISOhungry promotes the idea of “Happiness is Homemade” by making cooking more accessible to both user groups. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The paper has designed a dynamic dashboard that will show a summarized information of restaurants in Indonesia on four distinct metrics which are Food, Service, Ambience and Covid Safety. Each metrics shown will have their own ratings which shows the detailed score for each aspect of the restaurant. The data inside the dashboard have been developed by using semi supervised learning of aspect-based sentiment analysis approach. The idea is to analyze past reviews/comments of each restaurant in the current restaurant's online review platform and extract the sentiment as well as the aspect of each of the reviews. The restaurant lists and the reviews have been collected through web scraping method on one of the most used online review platforms in Indonesia which is Tripadvisor. Scraped data has been cleaned through several process of data pre-processing by utilizing Sastrawi and NLTK library for Indonesian languages. The machine learning tools that will extract the aspect and sentiments in every of the reviews will be built by applying Monkeylearn machine learning platform through APIs. Cleaned datasets have been imported into the platform for data annotations of model training to identify the set of words belongs in each aspect categories as well as their sentiment values. Although after reaching the end of the analysis, this paper has concluded that accuracy of the analysis may not be ideal due to lack of negative sentiment dataset being gathered which affects the model during the training process. In conclusion, the feature has successfully been built and implemented as well as deployed into a web server which supported by Ngrok services however, there are still more room for improvement regarding the analysis of the model. © 2021 IEEE.
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With the COVID-19 pandemic establishing its reign world-wide in the last two years with over 260 million cases and more than 5 million deaths, there has been an incessant need to quell the increasing fear and anxiety which has taken root in the minds of people. One way to do this has been to create an awareness about the global pandemic and arm people with a way to track various indispensable resources during these tough times. This paper discusses, in detail, the implementation of this idea - a real-time, COVID-19 resource tracker website which scrapes data from the most favored social media, Twitter and collates them to display the relevant information about the resources related to COVID-19;and provides vital particulars and facts about donation sites (in real-time) and general guidance in symptoms, treatments and precautions - integrated into an all-in-one website equipped with auto-completion feature and a handy navigation to increase its user friendly nature. © 2022 IEEE