Sentiment Analysis in R
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This document discusses sentiment analysis and how it is used. It defines sentiment analysis as extracting opinions, emotions, and sentiments from data. Examples are given of how companies like Delta Airlines and Macy's use sentiment analysis of social media to improve customer experience. Tools for implementing sentiment analysis are mentioned, and steps for performing sentiment analysis in R are outlined, including loading data, creating word lists, applying algorithms, and analyzing results.
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IRE2014-Sentiment Analysis
This document summarizes research on sentiment analysis in Twitter tweets. It discusses classifying the polarity of messages as positive, negative, or neutral. It also discusses determining the contextual polarity of words in tweets using features like part of speech tags, n-grams, emoticons, lexicon scores, and linguistic features. The researchers tested different machine learning models and achieved over 65% accuracy for message polarity classification and over 85% accuracy for contextual polarity disambiguation.
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This document summarizes a team's presentation on sentiment analysis of Twitter data. It introduces the purpose of sentiment analysis and challenges of using Twitter data. It then describes two classification algorithms - a Multinomial Naïve Bayes classifier and a Recursive Deep Model based on Recursive Neural Tensor Networks. The team contributed improvements to the Recursive Deep Model and tested both algorithms on 1400 classified tweets, finding the Recursive Deep Model achieved higher accuracy but with much longer execution time. The conclusion suggests the Recursive Deep Model could be enhanced to support multiple languages.
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Sentiment Analysis
This document discusses sentiment analysis. It defines sentiment analysis as analyzing text to determine the writer's feelings and opinions. It notes the rapid growth of subjective text online and how businesses and individuals can benefit from understanding sentiments. It describes common applications like brand analysis and political opinion mining. It also outlines different approaches to sentiment analysis like using semantics, machine learning classifiers, and sentiment lexicons. The document provides an example implementation and discusses advantages like lower costs and more accurate customer feedback.
Sentiment analysis presentation
Sentiment analysis techniques are used to analyze customer reviews and understand sentiment. Lexical analysis uses dictionaries to analyze sentiment while machine learning uses labeled training data. The document describes using these techniques to analyze hotel reviews from Booking.com. Word clouds and scatter plots of reviews are generated, showing mostly negative sentiment around breakfast, staff, rooms and facilities. Topic modeling reveals specific issues to address like soundproofing, air conditioning and parking. The analysis helps the hotel manager understand customer sentiment and priorities for improvement.
Introduction to Sentiment Analysis
This document provides an introduction to sentiment analysis. It begins with an overview of sentiment analysis and what it aims to do, which is to automatically extract subjective content like opinions from digital text and classify the sentiment as positive or negative. It then discusses the components of sentiment analysis like subjectivity and sources of subjective text. Different approaches to sentiment analysis are presented like lexicon-based, supervised learning, and unsupervised learning. Challenges in sentiment analysis are also outlined, such as dealing with language, domain, spam, and identifying reliable content. The document concludes with references for further reading.
Sentiment analysis
Sentiment Analysis also known as opinion mining and Emotional AI
Refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, quantify and study affective states and subjective information.
widely used in
Reviews
Survey responses
Online and social media
Health care
Recommended
IRE2014-Sentiment Analysis
This document summarizes research on sentiment analysis in Twitter tweets. It discusses classifying the polarity of messages as positive, negative, or neutral. It also discusses determining the contextual polarity of words in tweets using features like part of speech tags, n-grams, emoticons, lexicon scores, and linguistic features. The researchers tested different machine learning models and achieved over 65% accuracy for message polarity classification and over 85% accuracy for contextual polarity disambiguation.
Sentiment analysis of twitter data
This document summarizes a team's presentation on sentiment analysis of Twitter data. It introduces the purpose of sentiment analysis and challenges of using Twitter data. It then describes two classification algorithms - a Multinomial Naïve Bayes classifier and a Recursive Deep Model based on Recursive Neural Tensor Networks. The team contributed improvements to the Recursive Deep Model and tested both algorithms on 1400 classified tweets, finding the Recursive Deep Model achieved higher accuracy but with much longer execution time. The conclusion suggests the Recursive Deep Model could be enhanced to support multiple languages.
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This document analyzes sentiment toward two giant companies using tweets. It extracted 2500 tweets about the companies from Twitter and cleaned the data. Word clouds and histograms were created to visualize word frequencies for the overall data and positive or negative responses. A machine learning algorithm classified the sentiment with 87.4% and 84.5% accuracy. Both companies had overall positive sentiment but Amazon had slightly higher positive ratings, indicating it currently has stronger customer favorability.
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Sentiment analysis techniques are used to analyze customer reviews and understand sentiment. Lexical analysis uses dictionaries to analyze sentiment while machine learning uses labeled training data. The document describes using these techniques to analyze hotel reviews from Booking.com. Word clouds and scatter plots of reviews are generated, showing mostly negative sentiment around breakfast, staff, rooms and facilities. Topic modeling reveals specific issues to address like soundproofing, air conditioning and parking. The analysis helps the hotel manager understand customer sentiment and priorities for improvement.
Introduction to Sentiment Analysis
This document provides an introduction to sentiment analysis. It begins with an overview of sentiment analysis and what it aims to do, which is to automatically extract subjective content like opinions from digital text and classify the sentiment as positive or negative. It then discusses the components of sentiment analysis like subjectivity and sources of subjective text. Different approaches to sentiment analysis are presented like lexicon-based, supervised learning, and unsupervised learning. Challenges in sentiment analysis are also outlined, such as dealing with language, domain, spam, and identifying reliable content. The document concludes with references for further reading.
Sentiment analysis
Sentiment Analysis also known as opinion mining and Emotional AI
Refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, quantify and study affective states and subjective information.
widely used in
Reviews
Survey responses
Online and social media
Health care
Sentiment analysis of Twitter Data
Sentiment Analysis/Opinion Mining of Twitter Data on Unigram/Bigram/Unigram+Bigram Model using:
1. Machine Learning
2. Lexical Scores
3. Emoticon Scores
YouTube Video: https://youtu.be/VuR16P87yPE
Link to the WebPage: http://akirato.github.io/Twitter-Sentiment-Analysis-Tool
Github Page: https://github.com/Akirato/Twitter-Sentiment-Analysis-Tool
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This R Programming Tutorial will unravel the complete Introduction to R, Benefits of R for Business, What is Sentiment Analysis?, Advantages & Applications of Sentiment Analysis. In addition, we will also extensively cover Data Collection & Results using Sentiment Analysis.
At the end, you'll have strong knowledge regarding Sentiment Analytics via R Programming.
PPT Agenda
✓ Introduction to R Programming
✓ R for Data Analysis
✓ What is Sentiment Analysis all about?
✓ How Sentiment Analysis works
✓ Real World Applications of R Sentiment Analysis
✓ Job Trends for R
----------
What is R Programming?
R is a programming language and software environment for statistical computing and graphics. It is widely used among statisticians and data miners for data analysis and visualization.
What is Sentiment Analysis?
Sentiment analysis is the process of computing, identifying and categorizing opinions expressed in a blurb of text in order to determine whether a user's attitude towards a particular topic or product is positive, negative, or neutral. It uses natural language processing, text analysis and computational linguistics to identify and extract subjective information from text.
----------
Sentiment Analysis has the following components:
1. Collect Data from Desired Sources
2. Remove Sentiment Neutral Words
3. Two Way Categorization
4. Results are Positive on Negative
5. Act on the Model!
----------
Applications of Predictive Analysis
1. Analytical Customer Relationship Management (CRM)
2. Clinical decision support systems
3. Customer satisfaction & retention
4. Direct marketing
5. Fraud detection
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
social network analysis project twitter sentimental analysis
The analysis of Twitter Sentiment scores using different python libraries. Kind of social network analysis.Used Tweepy module to extract tweet.
Sentiment Analysis using Twitter Data
Sentiment Analysis helps to find the opinion of the users using the tweets posted in Twitter about the product.
Approaches to Sentiment Analysis
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
New sentiment analysis of tweets using python by Ravi kumar
Sentiment analysis of Tweets using python. it a minor project and ppt presented by us four students in 7th semester of engineering
Textual & Sentiment Analysis of Movie Reviews
This document discusses analyzing sentiment in movie reviews using machine learning. It motivates the use of sentiment analysis to help movie studios understand popularity and develop marketing strategies. It describes the dataset, objectives of analyzing sentiment, preliminary analysis showing 86% accuracy, and exploring models like SVC and KNN. Parameter tuning improved SVC accuracy to 84%. The document discusses identifying false positives/negatives and finding better features to distinguish sentiment. Overall it aims to help movie studios make business decisions from review sentiment analysis.
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Practical sentiment analysis
This document discusses a presentation given at the Sentiment Analysis Symposium in San Francisco in October 2012. The presentation introduces opinion mining and sentiment analysis, covering key concepts, applications, challenges, and techniques. Some of the main topics discussed include defining opinion mining and sentiment analysis, analyzing public mood and opinions on social media, predicting future trends from social data, and addressing challenges like determining the credibility and trustworthiness of opinions.
Twitter sentiment analysis project report
Project Report for Twitter Sentiment Analysis done using Apache Flume and data is analysed using Hive.
I intend to address the following questions:
How raw tweets can be used to find audience’s perception or sentiment about a person ?
How Hadoop can be used to solve this problem?
How Apache Hive can be used to organize the final data in a tabular format and query it?
How a data visualization tool can be used to display the findings?
Amazon sentimental analysis
1. The document describes an analysis of sentiment in reviews from Amazon Fine Foods using natural language processing techniques.
2. Over 568,454 reviews from 256,059 users on 74,258 products were analyzed to determine if each review expressed a positive, negative, or neutral sentiment.
3. After data cleaning and text preprocessing using techniques like removing stop words and applying stemming/lemmatization, different text vectorization techniques (bag-of-words, tf-idf, word2vec) were compared to represent the text of each review, with word2vec found to perform best.
4. Several classification algorithms were tested on the text vectors to predict sentiment, with logistic regression achieving the highest accuracy
How Sentiment Analysis works
Sentiment analysis software uses natural language processing and artificial intelligence to analyze text such as reviews and identify whether the opinions and sentiments expressed are positive or negative. It can help businesses understand customer perceptions of products and brands. While sentiment analysis works reasonably well for classifying simple positive and negative sentiments, it faces challenges in dealing with ambiguity and nuance in human language. The accuracy of sentiment analysis depends on factors such as the complexity of the language analyzed and how finely sentiments are classified.
Sentiment Analaysis on Twitter
Sentiment analysis on twitter
The document discusses sentiment analysis on tweets. It introduces sentiment analysis and why it is needed, particularly for promotion, products, politics and prediction. It describes Twitter terminology and presents a system architecture for sentiment analysis on tweets that includes preprocessing steps like removing URLs and tags, spell correction, emoticon tagging, part-of-speech tagging, and a scoring module using corpus-based and dictionary-based approaches to determine sentiment scores and classify tweets as positive, negative or neutral. Examples are provided to illustrate the sentiment analysis process.
Sentiment Analysis in Twitter
SentiTweet is a sentiment analysis tool for identifying the sentiment of the tweets as positive, negative and neutral.SentiTweet comes to rescue to find the sentiment of a single tweet or a set of tweets. Not only that it also enables you to find out the sentiment of the entire tweet or specific phrases of the tweet.
Sentiment analysis - Our approach and use cases
I. Introduction to Sentiment Analysis and its applications.
II. How to approach Sentiment Analysis?
III. 2015 Elections in Poland on Twitter.com & Onet.pl.
LSTM Based Sentiment Analysis
Sentimental analysis is a context based mining of text, which extracts and identify subjective information from a text or sentence provided. Here the main concept is extracting the sentiment of the text using machine learning techniques such as LSTM Long short term memory . This text classification method analyses the incoming text and determines whether the underlined emotion is positive or negative along with probability associated with that positive or negative statements. Probability depicts the strength of a positive or negative statement, if the probability is close to zero, it implies that the sentiment is strongly negative and if probability is close to1, it means that the statement is strongly positive. Here a web application is created to deploy this model using a Python based micro framework called flask. Many other methods, such as RNN and CNN, are inefficient when compared to LSTM. Dirash A R | Dr. S K Manju Bargavi "LSTM Based Sentiment Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42345.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42345/lstm-based-sentiment-analysis/dirash-a-r
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The document discusses enhancing sentiment analysis on tweets. It presents an architecture that extracts raw tweet data, performs data filtering, tokenization, and sentiment classification. Tweets are classified as positive, negative, or neutral. A rule-based approach and emotional rules are used to check polarity. Charts are used to represent the classified sentiment. The objective is to analyze tweets and represent them as charts for particular products.
Sentiment Analysis Using Hybrid Structure of Machine Learning Algorithms
Sentiment Analysis is the process used to determine the attitude/ opinion/ emotion expressed by a person about a particular topic. The presentation dealt with general approach and different machine learning based classification alogorithms. The slides is based on the work "Sentiment analysis using Neuro-Fuzzy and Hidden Markov models of text" by Rustamov S , Mustafayev E and Clements M A.
Twitter sentiment analysis.pptx
Six month major project on text classification with twitter sentiment analysis of US airlines.
It tells the importance of data and reviews given by the users for different airlines and helps recommending options to improve user experience.
Deep Dive - Consumer Sentiment Rating & Analysis White Paper
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Sentiment analysis of Twitter Data
Sentiment Analysis/Opinion Mining of Twitter Data on Unigram/Bigram/Unigram+Bigram Model using:
1. Machine Learning
2. Lexical Scores
3. Emoticon Scores
YouTube Video: https://youtu.be/VuR16P87yPE
Link to the WebPage: http://akirato.github.io/Twitter-Sentiment-Analysis-Tool
Github Page: https://github.com/Akirato/Twitter-Sentiment-Analysis-Tool
Sentiment analysis
This is a presentation on Sentiment Analysis.It gives a brief introduction about what is sentiment analysis
Sentiment Analysis via R Programming
This R Programming Tutorial will unravel the complete Introduction to R, Benefits of R for Business, What is Sentiment Analysis?, Advantages & Applications of Sentiment Analysis. In addition, we will also extensively cover Data Collection & Results using Sentiment Analysis.
At the end, you'll have strong knowledge regarding Sentiment Analytics via R Programming.
PPT Agenda
✓ Introduction to R Programming
✓ R for Data Analysis
✓ What is Sentiment Analysis all about?
✓ How Sentiment Analysis works
✓ Real World Applications of R Sentiment Analysis
✓ Job Trends for R
----------
What is R Programming?
R is a programming language and software environment for statistical computing and graphics. It is widely used among statisticians and data miners for data analysis and visualization.
What is Sentiment Analysis?
Sentiment analysis is the process of computing, identifying and categorizing opinions expressed in a blurb of text in order to determine whether a user's attitude towards a particular topic or product is positive, negative, or neutral. It uses natural language processing, text analysis and computational linguistics to identify and extract subjective information from text.
----------
Sentiment Analysis has the following components:
1. Collect Data from Desired Sources
2. Remove Sentiment Neutral Words
3. Two Way Categorization
4. Results are Positive on Negative
5. Act on the Model!
----------
Applications of Predictive Analysis
1. Analytical Customer Relationship Management (CRM)
2. Clinical decision support systems
3. Customer satisfaction & retention
4. Direct marketing
5. Fraud detection
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
social network analysis project twitter sentimental analysis
The analysis of Twitter Sentiment scores using different python libraries. Kind of social network analysis.Used Tweepy module to extract tweet.
Sentiment Analysis using Twitter Data
Sentiment Analysis helps to find the opinion of the users using the tweets posted in Twitter about the product.
Approaches to Sentiment Analysis
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
New sentiment analysis of tweets using python by Ravi kumar
Sentiment analysis of Tweets using python. it a minor project and ppt presented by us four students in 7th semester of engineering
Textual & Sentiment Analysis of Movie Reviews
This document discusses analyzing sentiment in movie reviews using machine learning. It motivates the use of sentiment analysis to help movie studios understand popularity and develop marketing strategies. It describes the dataset, objectives of analyzing sentiment, preliminary analysis showing 86% accuracy, and exploring models like SVC and KNN. Parameter tuning improved SVC accuracy to 84%. The document discusses identifying false positives/negatives and finding better features to distinguish sentiment. Overall it aims to help movie studios make business decisions from review sentiment analysis.
Linear Regression With R
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
Practical sentiment analysis
This document discusses a presentation given at the Sentiment Analysis Symposium in San Francisco in October 2012. The presentation introduces opinion mining and sentiment analysis, covering key concepts, applications, challenges, and techniques. Some of the main topics discussed include defining opinion mining and sentiment analysis, analyzing public mood and opinions on social media, predicting future trends from social data, and addressing challenges like determining the credibility and trustworthiness of opinions.
Twitter sentiment analysis project report
Project Report for Twitter Sentiment Analysis done using Apache Flume and data is analysed using Hive.
I intend to address the following questions:
How raw tweets can be used to find audience’s perception or sentiment about a person ?
How Hadoop can be used to solve this problem?
How Apache Hive can be used to organize the final data in a tabular format and query it?
How a data visualization tool can be used to display the findings?
Amazon sentimental analysis
1. The document describes an analysis of sentiment in reviews from Amazon Fine Foods using natural language processing techniques.
2. Over 568,454 reviews from 256,059 users on 74,258 products were analyzed to determine if each review expressed a positive, negative, or neutral sentiment.
3. After data cleaning and text preprocessing using techniques like removing stop words and applying stemming/lemmatization, different text vectorization techniques (bag-of-words, tf-idf, word2vec) were compared to represent the text of each review, with word2vec found to perform best.
4. Several classification algorithms were tested on the text vectors to predict sentiment, with logistic regression achieving the highest accuracy
How Sentiment Analysis works
Sentiment analysis software uses natural language processing and artificial intelligence to analyze text such as reviews and identify whether the opinions and sentiments expressed are positive or negative. It can help businesses understand customer perceptions of products and brands. While sentiment analysis works reasonably well for classifying simple positive and negative sentiments, it faces challenges in dealing with ambiguity and nuance in human language. The accuracy of sentiment analysis depends on factors such as the complexity of the language analyzed and how finely sentiments are classified.
Sentiment Analaysis on Twitter
Sentiment analysis on twitter
The document discusses sentiment analysis on tweets. It introduces sentiment analysis and why it is needed, particularly for promotion, products, politics and prediction. It describes Twitter terminology and presents a system architecture for sentiment analysis on tweets that includes preprocessing steps like removing URLs and tags, spell correction, emoticon tagging, part-of-speech tagging, and a scoring module using corpus-based and dictionary-based approaches to determine sentiment scores and classify tweets as positive, negative or neutral. Examples are provided to illustrate the sentiment analysis process.
Sentiment Analysis in Twitter
SentiTweet is a sentiment analysis tool for identifying the sentiment of the tweets as positive, negative and neutral.SentiTweet comes to rescue to find the sentiment of a single tweet or a set of tweets. Not only that it also enables you to find out the sentiment of the entire tweet or specific phrases of the tweet.
Sentiment analysis - Our approach and use cases
I. Introduction to Sentiment Analysis and its applications.
II. How to approach Sentiment Analysis?
III. 2015 Elections in Poland on Twitter.com & Onet.pl.
LSTM Based Sentiment Analysis
Sentimental analysis is a context based mining of text, which extracts and identify subjective information from a text or sentence provided. Here the main concept is extracting the sentiment of the text using machine learning techniques such as LSTM Long short term memory . This text classification method analyses the incoming text and determines whether the underlined emotion is positive or negative along with probability associated with that positive or negative statements. Probability depicts the strength of a positive or negative statement, if the probability is close to zero, it implies that the sentiment is strongly negative and if probability is close to1, it means that the statement is strongly positive. Here a web application is created to deploy this model using a Python based micro framework called flask. Many other methods, such as RNN and CNN, are inefficient when compared to LSTM. Dirash A R | Dr. S K Manju Bargavi "LSTM Based Sentiment Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42345.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42345/lstm-based-sentiment-analysis/dirash-a-r
sentiment analysis
Application of several machine learning algorithms for sentiment analysis and comparison of results.
Sentiment analysis in Twitter on Big Data
The document discusses enhancing sentiment analysis on tweets. It presents an architecture that extracts raw tweet data, performs data filtering, tokenization, and sentiment classification. Tweets are classified as positive, negative, or neutral. A rule-based approach and emotional rules are used to check polarity. Charts are used to represent the classified sentiment. The objective is to analyze tweets and represent them as charts for particular products.
Sentiment Analysis Using Hybrid Structure of Machine Learning Algorithms
Sentiment Analysis is the process used to determine the attitude/ opinion/ emotion expressed by a person about a particular topic. The presentation dealt with general approach and different machine learning based classification alogorithms. The slides is based on the work "Sentiment analysis using Neuro-Fuzzy and Hidden Markov models of text" by Rustamov S , Mustafayev E and Clements M A.
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social network analysis project twitter sentimental analysis
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New sentiment analysis of tweets using python by Ravi kumar
Sentiment Analysis Using Hybrid Structure of Machine Learning Algorithms
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Similar to Sentiment Analysis in R
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It tells the importance of data and reviews given by the users for different airlines and helps recommending options to improve user experience.
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This document summarizes DataRank's proprietary sentiment analysis model and methodology. It discusses using both automated machine learning sentiment analysis that is around 80% accurate, as well as manual human sentiment ratings. It also describes combining original sentiment ratings from sources with their own model to produce the most accurate overall sentiment ratings.
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Turn Online Reviews into Data Driven Business Decisions-2
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What to learn during the 21 days Lockdown | Edureka
Register Here: https://resources.edureka.co/21-days-learning-plan-webinar/
In light of the complete national lockdown for 21 days, we invite you to join a FREE webinar by renowned Mentor and Advisor, Nitin Gupta as he helps you create a 21-day learning gameplan to maximize returns for your career.
The webinar will help freshers and experienced professionals to capitalize on these 21 days and figure out the best technologies to learn while confined to home.
You will also get all your questions and doubts resolved in real-time.
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Top 10 Dying Programming Languages in 2020 | Edureka
YouTube Link: https://youtu.be/LSM7hD6GM4M
Get Edureka Certified in Trending Programming Languages: https://www.edureka.co
In this highly competitive IT industry, everyone wants to learn programming languages that will keep them ahead of the game. But knowing what to learn so you gain the most out of your knowledge is a whole other ball game. So, we at Edureka have prepared a list of Top 10 Dying Programming Languages 2020 that will help you to make the right choice for your career. Meanwhile, if you ever wondered about which languages are slated for continuing uptake and possible greatness, we have a list for that, too.
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Top 5 Trending Business Intelligence Tools | Edureka
YouTube Link: https://youtu.be/eEwq_mPd1iI
Edureka BI Certification Training Courses: https://www.edureka.co/bi-and-visualization-certification-courses
Receiving insights and finding trends is absolutely critical for businesses to scale and adapt as the years go on. This is exactly what business intelligence does and the best thing about these software solutions is that their potential uses are practically unlimited.
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Tableau Tutorial for Data Science | Edureka
YouTube Link:https://youtu.be/ZHNdSKMluI0
Edureka Tableau Certification Training: https://www.edureka.co/tableau-certification-training
This Edureka's PPT on "Tableau for Data Science" will help you to utilize Tableau as a tool for Data Science, not only for engagement but also comprehension efficiency. Through this PPT, you will learn to gain the maximum amount of insight with the least amount of effort.
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Python Programming Tutorial | Edureka
YouTube Link:https://youtu.be/CVv8zhYEjUE
Edureka Python Certification Training: https://www.edureka.co/data-science-python-certification-course
This Edureka PPT on 'Python Programming' will help you learn Python programming basics with the help of interesting hands-on implementations.
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Top 5 PMP Certifications | Edureka
YouTube Link:https://youtu.be/LvgqSMlIXFs
Get Edureka Certified in Trending Project Management Certifications: https://www.edureka.co/project-management-and-methodologies-certification-courses
Whether you want to scale up your career or are trying to switch your career path, Project Management Certifications seems to be a perfect choice in either case. So, we at Edureka have prepared a list of Top 5 Project Management Certifications that you must check out in 2020 for a major career boost.
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Top Maven Interview Questions in 2020 | Edureka
YouTube Link: https://youtu.be/5iTcAR4fScM
**DevOps Certification Courses - https://www.edureka.co/devops-certification-training***
This video on 'Maven Interview Questions' discusses the most frequently asked Maven Interview Questions. This PPT will help give you a detailed explanation of the topics which will help you in acing the interviews.
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Linux Mint Tutorial | Edureka
YouTube Link: https://youtu.be/xHUiYEIcY_I
** Linux Administration Certification Training - https://www.edureka.co/linux-admin **
Linux Mint is the first operating system that people from Windows or Mac are drawn towards when they have to switch to Linux in their work environment. Linux Mint has been around since the year 2006 and has grown and matured into a very user-friendly OS. Do watch the PPT till the very end to see all the demonstrations.
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How to Deploy Java Web App in AWS| Edureka
YouTube Link:https://youtu.be/Ozc5Yu_IcaI
** Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training**
This Edureka PPT shows how to deploy a java web application in AWS using AWS Elastic Beanstalk. It also describes the advantages of using AWS for this purpose.
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Importance of Digital Marketing | Edureka
YouTube Link:https://youtu.be/phPCkkWT76k
*** Edureka Digital Marketing Course: https://www.edureka.co/post-graduate/digital-marketing-certification***
This Edureka PPT on "Top 10 Reasons to Learn Digital Marketing" will help you understand why you should take up Digital Marketing
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RPA in 2020 | Edureka
YouTube Link: https://youtu.be/R132INtDg9k
** RPA Training: https://www.edureka.co/robotic-process-automation-training**
This PPT on RPA in 2020 will provide a glimpse of the accomplishments and benefits provided by RPA. Also, it will list out the new changes and technologies that will collaborate with RPA in 2020.
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Email Notifications in Jenkins | Edureka
YouTube Link: https://youtu.be/mb8WOHejlT8
**DevOps Certification Courses - https://www.edureka.co/devops-certification-training **
This PPT shows how to configure Jenkins to receive email notifications. It also includes a demo that shows how to do it in 6 simple steps in the Windows machine.
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EA Algorithm in Machine Learning | Edureka
YouTube Link: https://youtu.be/DIADjJXrgps
** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training **
This Edureka PPT on 'EM Algorithm In Machine Learning' covers the EM algorithm along with the problem of latent variables in maximum likelihood and Gaussian mixture model.
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Cognitive AI Tutorial | Edureka
Cognitive computing aims to mimic human reasoning and behavior to solve complex problems. It works by simulating human thought processes through adaptive, interactive, iterative and contextual means. Cognitive computing supplements human decision making in sectors like customer service and healthcare, while artificial intelligence focuses more on autonomous decision making with applications in finance, security and more. A use case of cognitive AI is using it to assess skills, find relevant jobs, negotiate pay, suggest career paths and provide salary comparisons and job openings to help humans.
AWS Cloud Practitioner Tutorial | Edureka
YouTube Link: https://youtu.be/0djPrlaxx_U
Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training
This Edureka PPT on AWS Cloud Practitioner will provide a complete guide to your AWS Cloud Practitioner Certification exam. It will explain the exam details, objectives, why you should get certified and also how AWS certification will help your career.
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Blue Prism Top Interview Questions | Edureka
YouTube Link: https://youtu.be/ykbRdUNIbyQ
** RPA Training: https://www.edureka.co/robotic-process-automation-certification-courses**
This PPT on Blue Prism Interview Questions will cover the Top 50 Blue Prism related questions asked in your interviews.
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Big Data on AWS Tutorial | Edureka
YouTube Link: https://youtu.be/ge4qhkl9uKg
AWS Architect Certification Training: https://www.edureka.co/aws-certification-training
This PPT will help you in understanding how AWS deals smartly with Big Data. It also shows how AWS can solve Big Data challenges with ease.
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A star algorithm | A* Algorithm in Artificial Intelligence | Edureka
YouTube Link: https://youtu.be/amlkE0g-YFU
** Artificial Intelligence and Deep Learning: https://www.edureka.co/ai-deep-learni... **
This Edureka PPT on 'A Star Algorithm' teaches you all about the A star Algorithm, the uses, advantages and disadvantages and much more. It also shows you how the algorithm can be implemented practically and has a comparison between the Dijkstra and itself.
Check out our playlist for more videos: http://bit.ly/2taym8X
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Kubernetes Installation on Ubuntu | Edureka
YouTube Link: https://youtu.be/UWg3ORRRF60
Kubernetes Certification: https://www.edureka.co/kubernetes-certification
This Edureka PPT will help you set up a Kubernetes cluster having 1 master and 1 node. The detailed step by step instructions is demonstrated in this PPT.
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Introduction to DevOps | Edureka
YouTube Link: https://youtu.be/GJQ36pIYbic
DevOps Training: https://www.edureka.co/devops-certification-training
This Edureka DevOps Tutorial for Beginners talks about What is DevOps and how it works. You will learn about several DevOps tools (Git, Jenkins, Docker, Puppet, Ansible, Nagios) involved at different DevOps stages such as version control, continuous integration, continuous delivery, continuous deployment, continuous monitoring.
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More from Edureka! (20)
What to learn during the 21 days Lockdown | Edureka
What to learn during the 21 days Lockdown | Edureka
Top 10 Dying Programming Languages in 2020 | Edureka
Top 10 Dying Programming Languages in 2020 | Edureka
Top 5 Trending Business Intelligence Tools | Edureka
Top 5 Trending Business Intelligence Tools | Edureka
A star algorithm | A* Algorithm in Artificial Intelligence | Edureka
A star algorithm | A* Algorithm in Artificial Intelligence | Edureka
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[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Mutation Testing for Task-Oriented Chatbots
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.Northern Engraving | Nameplate Manufacturing Process - 2024
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Apps Break Data
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
The Microsoft 365 Migration Tutorial For Beginner.pptx
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
AppSec PNW: Android and iOS Application Security with MobSF
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
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I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
GNSS spoofing via SDR (Criptored Talks 2024)
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
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Northern Engraving | Nameplate Manufacturing Process - 2024
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Sentiment Analysis in R
- 2. www.edureka.co/r-for-analytics What will you learn today? What is Sentiment Analysis? Sentiment Analysis Use Cases Sentiment Analysis Tools Hands-On : Sentiment Analysis in R
- 3. www.edureka.co/r-for-analytics What is Sentiment Analysis? Sentiment Analysis also called Opinion Mining implies extracting opinions, emotions and sentiments from data. Sentiment analysis is widely used to track attitudes and feelings on the web especially for measuring performance of products, services, brands.
- 5. www.edureka.co/r-for-analytics Sentiment Analysis : Airline Industry Delta is an American airline which monitors tweets to find out how their customers feel about delays, upgrades, in-flight entertainment and more. For example when a customer tweets negatively about his experience, Delta identifies such negative tweets and figure out the problem to improve the customer’s experience Delta Airline
- 6. www.edureka.co/r-for-analytics Sentiment Analysis : Retail Chain Macy’s is an American chain of department stores, Macy’s uses sentiment analysis as one of the way to improve customer experience and business growth. Macy’s
- 8. www.edureka.co/r-for-analytics Sentiment Analysis : Customer Acquisition Salesforce have built a product called Radian6 which identifies conversations that happen on social media about a particular company, its products or its competitors. Salesforce Radian6
- 9. www.edureka.co/r-for-analytics Sentiment Analysis : Financial Analysis Thomson Reuters EIKON software uses Sentiment Analysis to gain competitive advantage by analyzing the social media tweets/posts about various companies and people Thomson Reuters EIKON
- 10. www.edureka.co/r-for-analytics Sentiment Analysis Tools Below are some of the popular sentiment analysis tools :
- 11. www.edureka.co/r-for-analytics Sentiment Analysis in R Below are the steps to perform sentiment analysis in R 1. Load the data 2. Create a list of positive words 3. Create a list of negative words 4. Apply the sentiment algorithm 5. Analyze the result (e.g. frequency distribution, mean, median, histogram etc.)
- 13. www.edureka.co/r-for-analytics Survey Your feedback is vital for us, be it a compliment, a suggestion or a complaint. It helps us to make your experience better! Please spare few minutes to take the survey after the webinar.
- 14. www.edureka.co/r-for-analytics Thank You … Questions/Queries/Feedback Recording and presentation will be made available to you within 24 hours