Beautiful Soup Research Articles

CV Analysis Using Machine Learning

Abstract: Recruitment is a tiresome process wherein the very first task of a recruiter is to screen resumes (Curriculum Vitae). Nowadays, many companies prefer online job application in comparison to paper resumes. The proposed system is designed in such a way that applying for job openings & screening could be made easy for job applicants as well as the recruiters. The recruiters from the various companies can post their requirements for particular job openings available in their respective companies and on the other hand will allow the job applicants to submit their resumes and apply for job postings they are interest in. The resumes submitted by the candidates are then compared with the job profile requirements as stated by the company’s recruiter by using technologies like Machine Learning, Natural Language Processing which will not only help the recruiter to select the best candidates from the large pool of candidates but also help them in saving time that is invested in manual analysis of the resumes. Keywords: Machine Learning, Natural Language Processing, Curriculum Vitae, Parser, Natural Language Toolkit, Beautiful Soup, SpaCy, Named Entity Recognition.

CSR committees, politicians and CSR efforts

PurposeThe purpose of this paper is to study the relationships among the presence of corporate social responsibility (CSR) committee, politicians on CSR committee and CSR disclosure in 10-Ks using data from S&P 500 firms during 2005–2013.Design/methodology/approachThe authors manually check the information of CSR committees as well as committees with CSR/sustainability functions from proxy statements (DEF 14a). CSR disclosure from 10-Ks is obtained by using a Python library named Beautiful Soup 4 to clean the rough data from the raw format files from EDGAR.FindingsThe authors find that superior sustainability governance is associated with more voluntary CSR disclosure in their 10-K reports. More importantly, they find that CSR committee members with working experiences as politicians play an important role to improve CSR disclosure. In the robustness tests, they find that CSR committee and the politicians on CSR committee are also associated with high KLD CSR score ratings.Practical implicationsThe finding in our paper that politicians on CSR committee can enhance CSR efforts may provide practical implications to some companies. Companies may consider inviting people who have political connections and experience to serve on CSR committees.Originality/valueThe authors find the presence of politicians on CSR committee is associated with CSR disclosure and CSR performance. That's new to the CSR governance literature and makes contributions to CSR disclosures and CSR committee expertise and skills.

Python Data Analysis and Attribute Information Extraction Method Based on Intelligent Decision System

In order to improve the effect or characteristics of Python data analysis and attribute information extraction, a method for intelligent decision system is proposed. The content of the method is as follows: to create a big data mining model for optimal decision making, to use smart data integration method to integrate data functions, to reconstruct management data evaluation information, and to extract and use management multidimensional information parameters regularly. Characterization methods multidimensional information breakdown and optimization of features are performed, characteristics are classified according to their differences, and management and decision-making optimizations are implemented. Based on linguistic Python, combined with rich and powerful libraries such as regular expression, urllib2, and Beautiful Soup, this paper discusses the methods of building modular web data collection, HTML parsing, and capturing link data. The experimental results show that there is a certain gap in the decision support time of the three methods with the change of iteration times. Among them, the decision support time of the method in this paper is always less than 2 s, while the time of the other two methods is longer. Compared with the other two methods, the decision support time of the method in this paper is shortened by about 1.7 s and 3.1 s, respectively. This is because this method classifies the data attribute gap in decision support, which saves the time of decision support. It is verified that the method in this paper can carry out decision support quickly and has certain reliability. It is proved that intelligent decision system can effectively improve Python data analysis and attribute information extraction.

Predicting the impact of the third wave of COVID-19 in India using hybrid statistical machine learning models: A time series forecasting and sentiment analysis approach

BackgroundSince January 2020, India has faced two waves of COVID-19; preparation for the upcoming waves is the primary challenge for public health sectors and governments. Therefore, it is important to forecast future cumulative confirmed cases to plan and implement control measures effectively. MethodsThis study proposed a hybrid autoregressive integrated moving average (ARIMA) and Prophet model to predict daily confirmed and cumulative confirmed cases. The built-in auto.arima function was first used to select the optimal hyperparameter values of the ARIMA model. Then, the modified ARIMA model was used to find the best fit between the test and forecast data to find the best model parameter combinations. Articles, blog posts, and news stories from virologists, scientists, and health experts related to the third wave of COVID-19 were gathered using the Python web scraping package Beautiful Soup. Their opinions (sentiments) toward the potential third wave were analyzed using natural language processing (NLP) libraries. ResultsA spike in daily confirmed and cumulative confirmed cases was predicted in India in the next 180 days based on past time series data. The results were validated using various analytical tools and evaluation metrics, producing a root mean square error (RMSE) of 0.14 and a mean absolute percentage error (MAPE) of 0.06. The NLP processing results revealed negative sentiments in most articles and blogs, with few exceptions. ConclusionThe findings of this study suggest that there will be more active cases in the upcoming days. The proposed models can forecast future daily confirmed and cumulative confirmed cases. This study will help the country and states plan appropriate public health measures for the upcoming waves of COVID-19.

Portfolio Builder and Career Recommender by Scraping Data Using Flask and Tensorflow

Abstract: In this day and age, the use of physical copies for portfolios and resumes has largely been replaced with the use of online documents to showcase the skills and experience of the individual in question. However, this too is beginning to give way to a newform of a portfolio- a website that communicates all of the above information in a document, but in an aesthetic and elegant way. While this was only usually required or recommended for a specific portion of the workforce, an increasing number of employers have begun to prefer this creative approach, making it more appealing to any aspiring applicants. In this project, we plan to build a website that will take data from users and build a custom portfolio using a variety of pre-developed templates and designs. The user just needs to provide their LinkedIn profile which will then be used to scrape the data of the user, usin Flask, Selenium, and BeautifulSoup. After the information and details of the user has been extracted, our website will provide various templates to choose from. The user will then have the option to build the website or download the zip file of the created website which could be used in future or could be hosted by the user. Hence, in this paper we aim to build a website that would help create a resume quickly and professionally without the hassle and struggle of figuring out how to format everything or what fonts and styles to use. We will help generate a dynamic resume that will highlight the skills, achievements and talents and also be able to showcase the user’s thoughts and personality. Moreover, using Naive Bayes, we will provide a career recommendation to the user, which will guide steer them towards the most suitable profession for their skills Keywords: Portfolio, Flask, Selenium, TensorFlow, Naive Bayes, Beautiful Soup, Machine Learning

Web Crawling on News Web Page using Different Frameworks

Now a days Internet is widely used by users to find required information. Searching on web for useful information has become more difficult. Web crawler helps to extract the relevant and irrelevant links from the web. Web crawler downloads web pages through the program. This paper implements web crawler with Scrapy and Beautiful Soup python web crawler framework to crawls news on news web sites.Scrapy is a web crawling framework that allow programmer to create spider that define how a certain site or a group of sites will be scraped. It has built-in support for extracting data from HTML sources using XPath expression and CSS expression. BeautifulSoup is a framework that extract data from web pages. Beautiful Soup provides a few simple methods for navigating, searching and modifying a parse tree. BeautifulSoup automatically convert incoming document to Unicode and outgoing document to UTF-8.Proposed system use BeautifulSoup and scrapy framework to crawls news web sites. This paper also compares scrapy and beautiful Soup4 web crawler frameworks.

Image Information Collection System Based on Python Web Crawler Technology

Collecting data from the Internet is the key to solve the problem of data sources. This paper studies the image information collection system based on Python web crawler technology.This paper studies and develops a data acquisition system based on Python web crawler technology, which realizes the automatic collection of subject data. In this paper, we use urllib, beautiful soup, threading library to design and develop a system model framework including data crawling, exception handling, robots protocol management and multithreading management modules. Through the application of specific cases, this paper introduces the data acquisition process. Experimental data show that compared with the traditional manual data acquisition, the proposed method greatly improves the work efficiency.

빅데이터를 활용한 시대별 홈 트레이닝 키워드 분석

The purpose of this study was to analyze and visualize the keywords of home training by era using big data. In order to collect articles on home training and to crawl articles on portal site Naver, I used Python (Python ver2.7) package Beautiful Soup. For text mining of the collected materials, KoNLPy analysis was used to extract keywords, and stopwords unrelated to the research theme were deleted using CountVectorizer, and core keywords were extracted. Finally, in order to confirm the connection relationship of each word, the centrality (connection centrality, mediation centrality, proximity centrality, eigenvector centrality) is analyzed using the simultaneous appearance frequency DTM (Document Term Matrix). Goes through a visualization process via Network X. First of all, the main keywords of home training by era were the main keywords from June 1990 to 2019 and the movements of the main keywords of the 30-year material, the frequency of keywords such as center and representative, but In 2020, the frequency of keywords such as exercise, home, corona, and video was high. The second is the structure of the main keywords of home training by era. In 2020, the structure of keywords for exercise, corona, services, video, content, online keywords, and keywords for the rest of the era will be movement, representative, center, and competition. , Players, etc. had a high value of centrality. As a result, home training in 2020 was associated with coronavirus 19 and keywords were calculated, but in other times, keywords associated with the concept of training appeared more frequently and were published in the concept of home training. It has drawn conclusions that can be seen after 2020. In addition, after 2020, the content development industry will be the focus of attention in the home training related industry.

Impact of Unreliable Content on Social Media Users during COVID-19 and Stance Detection System

The abundant dissemination of misinformation regarding coronavirus disease 2019 (COVID-19) presents another unprecedented issue to the world, along with the health crisis. Online social network (OSN) platforms intensify this problem by allowing their users to easily distort and fabricate the information and disseminate it farther and rapidly. In this paper, we study the impact of misinformation associated with a religious inflection on the psychology and behavior of the OSN users. The article presents a detailed study to understand the reaction of social media users when exposed to unverified content related to the Islamic community during the COVID-19 lockdown period in India. The analysis was carried out on Twitter users where the data were collected using three scraping packages, Tweepy, Selenium, and Beautiful Soup, to cover more users affected by this misinformation. A labeled dataset is prepared where each tweet is assigned one of the four reaction polarities, namely, E (endorse), D (deny), Q (question), and N (neutral). Analysis of collected data was carried out in five phases where we investigate the engagement of E, D, Q, and N users, tone of the tweets, and the consequence upon repeated exposure of such information. The evidence demonstrates that the circulation of such content during the pandemic and lockdown phase had made people more vulnerable in perceiving the unreliable tweets as fact. It was also observed that people absorbed the negativity of the online content, which induced a feeling of hatred, anger, distress, and fear among them. People with similar mindset form online groups and express their negative attitude to other groups based on their opinions, indicating the strong signals of social unrest and public tensions in society. The paper also presents a deep learning-based stance detection model as one of the automated mechanisms for tracking the news on Twitter as being potentially false. Stance classifier aims to predict the attitude of a tweet towards a news headline and thereby assists in determining the veracity of news by monitoring the distribution of different reactions of the users towards it. The proposed model, employing deep learning (convolutional neural network(CNN)) and sentence embedding (bidirectional encoder representations from transformers(BERT)) techniques, outperforms the existing systems. The performance is evaluated on the benchmark SemEval stance dataset. Furthermore, a newly annotated dataset is prepared and released with this study to help the research of this domain.

Data on post bank customer reviews from web.

This document describes a set of customer feedback data concerning the Post Bank. We collected data from 16,659 feedback lines using the Beautiful Soup package from the authoritative site banki.ru is selected as the source of data for collection. The dataset is compiled to monitor the level of trust of bank customers in its banking service. The data presents text reviews for 2013 - 2019 and includes, with or without ratings. Scientists can predict feedback ratings with an empty value in the future. We added additional columns to the dataset with official comments of bank employees, as well as values for the fog-index by Gunning parameter, which is used for the readability of the text. The data can be useful for customer service managers to identify problems in customer service and solve these problems, to assess the dynamics of the appearance of positive and negative reviews of bank customers.

Web Scrapping: Data Extraction from Websites

Data is very important nowadays for almost all organizations for their existence as well as for their growth. The Internet has become the major source of data for individuals and almost all organizations. Authentic Websites are a major source of reliable data for many individuals and organizations. Extracting Data from websites is commonly referred as Web Scrapping, which refers to both manual and automated process. Extracting large amount of meaning full data from the websites manually is very difficult, tedious and redundant task. Automated Scrapping is done by writing specific programs to extract the required data from the websites. These programs are usually called as web scrappers. Web scrappers are written using many programming languages like Python, Node.js, Ruby, C++, PHP etc. Each language has its own unique features and built in libraries for performing data extraction. There are many web scrapping tools like Beautiful Soup, Octoparse, Parsehub etc. In this article we are going to analyses few recent Web Scrappers tools used in scrapping the Web.

Multi-text classification of Urdu/Roman using machine learning and natural language preprocessing techniques

Objectives: This research presents multi-text classification from the news text dataset. The main purpose of this work is to classify multi-text for Urdu and Roman language using Natural Language processing and Machine Learning classification models. Methods/Statistical analysis: In this research, online news data has been collected through beautiful soup web scraping tool. In order to analyze the model accuracy news data is divided into six categories which has been composed from various online newspaper platforms. The main news corpus data consists of 10500 news in Urdu and Roman Urdu language including, Accidental, Education, Entertainment, International, Sports and Weather news have been primarily focused in the proposed research study. Furthermore, preprocessing is performed on text corpus using Natural Language Processing technique; for example, data cleaning, data balancing, and stop word removal. For feature extraction count vector, TF-IDF and Chi2 are employed as word filtering. For multi-text classification the Machine Learning classification schemes have been implemented namely, Naive Bayes Classifier, Logistic Regression, Random Forest Classifier, Linear SVC, and KNeighbors Classifier. After comparative analysis results showed that Linear Support Vector Classifier provided 96% accuracy among other tested methods. Findings: Multi-Text classification of Urdu Roman language having different writing styles, word structure, irregularities, grammar, and combined corpus is a challenging task. For this purpose, we implemented different Machine Learning algorithms with Natural Language preprocessing technique which provided optimal results in classification of multi-text news data. Keywords: MultiText Classification; Machine Learning; NLP Preprocessing Techniques

News Text Summarization Based on Multi-Feature and Fuzzy Logic

Text Summarization as a phenomenon has always been present and rather an evolving one with the advent of new technologies both in terms of data collection as well for the processing of this data. One reason of using text summarization is the huge amount of data floating over the internet in the form of text files, comments which is though potent enough to be used to extract useful information. but since the amount of text present in these sources is too huge, so the need of text summarization becomes justified by every argument. Some of the areas where text summarization is vastly used is applications involved in providing capsule information such as compact news applications, or websites providing academic notes for various examinations This paper presents an auto text summarizer application which takes the URL of a web page as input, performs summarization on the selected elements and then presents this summarized text content on the front end of a web application. At the backend, the process of scraping of web page content (if an http URL is provided as input) using beautiful soup library or reading of text provided takes place. news in short forms, or micro blogging websites. The scraped content after being preprocessed properly is summarized using a suitable library which in our case is one among NLTK, Spacy, Genism and Sumy. The summarized content is presented at the frontend using flask framework of Python. The results produced using different libraries are compared in the end in terms of reading time of the summarized content. The application uses extractive text summarization technique in order to achieve its result which is a compact summary of the textual data prepared from the keywords already present in the document.

Visualization of Optimal Product Pricing using E-Commerce Data

With the number of e-commerce websites being increasing rapidly, online shopping has become the trend nowadays. Though, online shopping is very easy; however, when it comes to product selection it is a tedious task and time consuming to identify which online site gives us the best price and offers. Comparing products and filtering them from each online site is a very time consuming task for a buyer. This paper uses the techniques of Web Scraping using python libraries like Beautiful Soup, requests, matplotlib for identifying the best prices and for deciding the best product deal to the customer from different online websites. Web scraping is an automated technique of extracting data from websites. In this paper, real time data is extracted from various e-commerce sites and compared automatically. Finally, the results are graphically displayed based on which the customer makes the appropriate decision.

A Command Line Tool for Tracking Error Details of Program using Web Scrapper

Web scraping, a software technique is used to extract information from any website. The proposed system is a command line tool that uses web scrapping to extract the relevant data from stack overflow website using the library "beautiful soup". Programmers find difficulty to browse the information about the error encountered during the execution of their program. They take more time to find the appropriate information about the error. The aim of the proposed system is to help them for tracking error details thereby reducing/nullifying their browsing time for retrieving error details. This tool is implemented for five different programming languages and it works for almost all the operating system except windows 10.

Web Site Ranking Feedback System

We all search on google for something and get the results in the form of different websites with some description. We generally click on first or second website links if results are not found we go down on google page. The website ranking is given by search engines by different criteria. Last website won’t be seen by none of the people in most cases and first website will be having a great market compared to last website. So we need to help the last website to be moved up in results and help in generating revenue and have good rank on searching by giving feedback. This system will provide the difference between the first website and last website on the google results and will provide the feedback to the last website like content, links, images used by first website which helps the last website to be used in his webpage. This system is user friendly which is built on HTML as front end and Python flask as back end and used python package beautiful soup to parse HTML data and to automate browser behaviour with python. This system is done on web mining which has three categories firstly web content mining in which we scan the web pages and get to know the links, text, images used. Secondly web usage mining in which reports are generated after analysis which contain the details of text, images, links. Finally the web structure mining states that structural summary of website

Acquiring geographical data with web harvesting

Many websites contain very attractive and up to date geographical information. This information can be extracted, stored, analyzed and mapped using web harvesting techniques. Poorly organized data from websites are transformed with web harvesting into a more structured format, which can be stored in a database and analyzed. Almost 25% of web traffic is related to web harvesting, mostly while using search engines. This paper presents how to harvest geographic information from web documents using the free tool called the Beautiful Soup, one of the most commonly used Python libraries for pulling data from HTML and XML files. It is a relatively easy task to process one static HTML table. The more challenging task is to extract and save information from tables located in multiple and poorly organized websites. Legal and ethical aspects of web harvesting are discussed as well. The paper demonstrates two case studies. The first one shows how to extract various types of information about the Good Country Index from the multiple web pages, load it into one attribute table and map the results. The second case study shows how script tools and GIS can be used to extract information from one hundred thirty six websites about Nova Scotia wines. In a little more than three minutes a database containing one hundred and six liquor stores selling these wines is created. Then the availability and spatial distribution of various types of wines (by grape types, by wineries, and by liquor stores) are mapped and analyzed.

Intro to Beautiful Soup

Beautiful Soup is a Python library for getting data out of HTML, XML, and other markup languages.

A Super-Plan!! Dedicated to All Security Worshippers

A new party is being formed which will be called the Super Ultra Progressive Party, hereafter known as the SUP Party, pronounced the Soup Party as in “Soup of the Evening, beautiful, beautiful soup.” The fundamental goal of the SUP Party is the establishment of Automatic Economy to take the place of Planned Economy. Under Planned Economy the battle of words has reached colossal heights. With as much repetition as a Tibetan Prayer-wheel the respective camps blare forth stridently the terms “avaricous facists,” “reactionary capitalists,” “autocratic communists,” “self-centered, short-sighted trade unionist,” words, words with little meaning. To eliminate this strife we must pass through the puerile stage of Planned Economy to the final stage of Automatic Economy.