Facebook uses machine learning in several ways:
1. Automatic friend tagging suggestions and mutual friend analysis use face recognition and clustering algorithms.
2. The newsfeed arranges posts using ML to prioritize those from close friends and favorite pages.
3. Friend suggestions are made based on mutual friend circles using ML.
4. Collaborative filtering is used for recommendations like pages, groups, events and games based on what similar users engage with.
5. Sentiment analysis of comments classifies them as positive, negative or neutral emotions to provide insight.
Tweeting for Hillary - DS 501 case study 1Yousef Fadila
source code: https://github.com/yousef-fadila/casestudy1/blob/master/CaseStudy1.ipynb
This slides were presented as part of case study 1: Collecting Data from Twitter for DS501:Introduction to Data Science course
code is written in python; Charts and Maps were also produced in Python as well.
Focused on social media strategies and effective ways to monitor success for your non-profit or change-focused organization. Christopher Berry, Group Director of Marketing Science at Critical Mass will speak on practical social analytics.
Tweeting for Hillary - DS 501 case study 1Yousef Fadila
source code: https://github.com/yousef-fadila/casestudy1/blob/master/CaseStudy1.ipynb
This slides were presented as part of case study 1: Collecting Data from Twitter for DS501:Introduction to Data Science course
code is written in python; Charts and Maps were also produced in Python as well.
Focused on social media strategies and effective ways to monitor success for your non-profit or change-focused organization. Christopher Berry, Group Director of Marketing Science at Critical Mass will speak on practical social analytics.
Facebook sentiment analysis is the automated process of understanding a person’s opinion about a particular subject based on a comment or post. When you can’t figure out the emotion that fueled a customer’s written response, you run the risk of staring at the same words for hours on end. Thankfully, that’s where sentiment analysis comes in. Using AI technology, sentiment analysis can interpret customer’s feelings by using natural language processing models.
BytesView's advanced machine learning techniques can help you analyze the emotions expressed by the author in a piece of text.
It can be easily done based on the types of feelings expressed in the text such as fear, anger, happiness, sadness, love, inspiring, or neutral.
Top 5 Survey Data Analysis Software .pptxRepustate
Giving a voice to customers and employees begins with asking the right questions. An ideal survey should ensure that true opinions come through. That’s why questionnaires must have open-ended questions so that respondents freely express their opinions.
SENTIMENT ANALYSIS APPROACH IN NATURAL LANGUAGE PROCESSING FOR DATA EXTRACTIONIAEME Publication
The study of sentiment analysis and opinion mining examines how people's opinions, sentiments, assessments, attitudes, and emotions are expressed in written language. In addition to being heavily researched in data mining, web mining, and text mining, it is one of the most active research fields in natural language processing. Applications for sentiment analysis include analysing the effects of events in social networks and examining consumer views of goods and services. With the expansion of social media, including reviews, forum conversations, blogs, microblogs, Twitter, and social networks, sentiment analysis is becoming more and more important. For measuring sentiments with a large volume of opinionated data captured in digital form for analysis, techniques like supervised machine learning and lexical-based approaches are available.
Building a Sentiment Analytics Solution Powered by Machine Learning- Impetus ...Impetus Technologies
For Impetus’ White Papers archive, visit- http://www.impetus.com/whitepaper
This white paper focuses on why Sentiment Analysis is vital in today’s world, the existing solutions landscape and why Machine learning is recommended to build such a solution and gather better business insights.
Using Facebook insights to create target customer and buyer personas Duncan Connor
Buyer personas are usually inaccurate, based on biases and anecdotes rather than data. With Facebook Audience Insights, you can base your personas on the people who actually use your product or visit your website or Facebook page.
Learn how personas can shape your optimization programVWO
Personas seem to have fallen out of favour in recent years. “I don’t think you can build a great product or experience for a person that doesn’t exist,” Jason Fried famously wrote. The criticism has not been unwarranted. Many personas are a waste of time. On the other hand, there are businesses who have gained success by simply defining their day-to-day marketing around personas.
In this webinar, CRO experts from AWA digital and Phil Cave will share their first-hand experience about building personas and what value well-constructed personas adds to your optimization program.
Data Augmentation for Improving Emotion Recognition in Software Engineering C...Preetha Chatterjee
Emotions (e.g., Joy, Anger) are prevalent in daily software engineer- ing (SE) activities, and are known to be significant indicators of work productivity (e.g., bug fixing efficiency). Recent studies have shown that directly applying general purpose emotion classifica- tion tools to SE corpora is not effective. Even within the SE domain, tool performance degrades significantly when trained on one com- munication channel and evaluated on another (e.g, StackOverflow vs. GitHub comments). Retraining a tool with channel-specific data takes significant effort since manually annotating a large dataset of ground truth data is expensive.
In this paper, we address this data scarcity problem by auto- matically creating new training data using a data augmentation technique. Based on an analysis of the types of errors made by popu- lar SE-specific emotion recognition tools, we specifically target our data augmentation strategy in order to improve the performance of emotion recognition. Our results show an average improvement of 9.3% in micro F1-Score for three existing emotion classification tools (ESEM-E, EMTk, SEntiMoji) when trained with our best aug- mentation strategy.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Facebook sentiment analysis is the automated process of understanding a person’s opinion about a particular subject based on a comment or post. When you can’t figure out the emotion that fueled a customer’s written response, you run the risk of staring at the same words for hours on end. Thankfully, that’s where sentiment analysis comes in. Using AI technology, sentiment analysis can interpret customer’s feelings by using natural language processing models.
BytesView's advanced machine learning techniques can help you analyze the emotions expressed by the author in a piece of text.
It can be easily done based on the types of feelings expressed in the text such as fear, anger, happiness, sadness, love, inspiring, or neutral.
Top 5 Survey Data Analysis Software .pptxRepustate
Giving a voice to customers and employees begins with asking the right questions. An ideal survey should ensure that true opinions come through. That’s why questionnaires must have open-ended questions so that respondents freely express their opinions.
SENTIMENT ANALYSIS APPROACH IN NATURAL LANGUAGE PROCESSING FOR DATA EXTRACTIONIAEME Publication
The study of sentiment analysis and opinion mining examines how people's opinions, sentiments, assessments, attitudes, and emotions are expressed in written language. In addition to being heavily researched in data mining, web mining, and text mining, it is one of the most active research fields in natural language processing. Applications for sentiment analysis include analysing the effects of events in social networks and examining consumer views of goods and services. With the expansion of social media, including reviews, forum conversations, blogs, microblogs, Twitter, and social networks, sentiment analysis is becoming more and more important. For measuring sentiments with a large volume of opinionated data captured in digital form for analysis, techniques like supervised machine learning and lexical-based approaches are available.
Building a Sentiment Analytics Solution Powered by Machine Learning- Impetus ...Impetus Technologies
For Impetus’ White Papers archive, visit- http://www.impetus.com/whitepaper
This white paper focuses on why Sentiment Analysis is vital in today’s world, the existing solutions landscape and why Machine learning is recommended to build such a solution and gather better business insights.
Using Facebook insights to create target customer and buyer personas Duncan Connor
Buyer personas are usually inaccurate, based on biases and anecdotes rather than data. With Facebook Audience Insights, you can base your personas on the people who actually use your product or visit your website or Facebook page.
Learn how personas can shape your optimization programVWO
Personas seem to have fallen out of favour in recent years. “I don’t think you can build a great product or experience for a person that doesn’t exist,” Jason Fried famously wrote. The criticism has not been unwarranted. Many personas are a waste of time. On the other hand, there are businesses who have gained success by simply defining their day-to-day marketing around personas.
In this webinar, CRO experts from AWA digital and Phil Cave will share their first-hand experience about building personas and what value well-constructed personas adds to your optimization program.
Data Augmentation for Improving Emotion Recognition in Software Engineering C...Preetha Chatterjee
Emotions (e.g., Joy, Anger) are prevalent in daily software engineer- ing (SE) activities, and are known to be significant indicators of work productivity (e.g., bug fixing efficiency). Recent studies have shown that directly applying general purpose emotion classifica- tion tools to SE corpora is not effective. Even within the SE domain, tool performance degrades significantly when trained on one com- munication channel and evaluated on another (e.g, StackOverflow vs. GitHub comments). Retraining a tool with channel-specific data takes significant effort since manually annotating a large dataset of ground truth data is expensive.
In this paper, we address this data scarcity problem by auto- matically creating new training data using a data augmentation technique. Based on an analysis of the types of errors made by popu- lar SE-specific emotion recognition tools, we specifically target our data augmentation strategy in order to improve the performance of emotion recognition. Our results show an average improvement of 9.3% in micro F1-Score for three existing emotion classification tools (ESEM-E, EMTk, SEntiMoji) when trained with our best aug- mentation strategy.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
2. In Facebook
• Automatic friend tagging suggestions: When a pic is uploaded on
facebook, a suggestion asking if you want to tag your friend in the pic
appears. This is done by Facebook's face detection and recognition
algorithms based on the advanced deep learning neural network
research project Deepface.
• Mutual friend analysis: Facebook uses the clustering algorithm to
find mutual friends.
• Newsfeed: facebook uses ML to arrange your Newsfeed too. Like
posts of close friends may come up first. Posts related to your
favourite pages come up first.
• Friend Suggestions: Machine learning is used by FB to suggest new
friends based on mutual friend circles.
3.
4.
5.
6. • CF[Collaborative Filtering] is a recommender systems
technique that helps people discover items that are most
relevant to them. At Facebook, this might include pages,
groups, events, games, and more. CF is based on the idea that
the best recommendations come from people who have similar
tastes. In other words, it uses historical item ratings of like-
minded people to predict how someone would rate an item.
7.
8. Ads on facebook
• The process of placing an ad on News Feed is a complicated
dance. Facebook has to decide not only which ad to show to its
users, but when to show it to them. There isn't a dedicated
"slot," so to speak, for an ad in News Feed, so the team must
time the ads based what the user is doing on Facebook at that
given moment.
9. On Facebook
Business people use facebook data to:
* Promote relevant products
* Grow brand awareness
* Get qualified leads
* Close the loop
10. Sentiment Analysis
• Sentiment Analysis can be used to automatically detect
emotions, speculations, evaluations and opinions in the content
that people write. The sentiment analysis tool extracts data from
the comments on a post, cleanses the data and processes it to
give us an analysis in the form of a graph that classifies all the
comments into polarity and sentiments. This provides insight
into comments by classifying them into three polarities
(positive, negative & neutral) and into six different emotions
(anger, disgust, fear, joy, sadness, surprise). Most of the
algorithms for sentiment analysis are based on a classifier
11. Bayes' Theorem
p(Ck)= p (occurrence of class) [prior]
p(x)= p (instance of word) [likelihood]
12. • Its classifications regarding the decisions are surprisingly accurate.
The above function returns an object of class (data.frame) with seven
columns (anger, disgust, fear, joy, sadness, surprise and best_fit
category). This best_fit is the most likely sentiment category among
the six emotionsfor a given content item. Similarly, we will classify
polarity in the text and combine the emotions of all the comments. In
simple words the approach is, if a piece of content has more positive
keywords than negative keywords, it’s a positive content; if it has
more negative keywords than positive keywords, it’s a negative
content.
13. • After the classification, we fetch the “best_fit” category for
analysis. When all the data is cleansed and processed we enter
the next phase: strategic representation of data. In this phase the
processed data is subjected to a function named ‘ggplot()’,
which plots the distribution of emotions (anger, disgust, fear,
joy, sadness, surprise). Similarly, we can plot the distribution of
polarity (positive, negative and neutral).
14.
15. Deep Facebook Analysis for business
*Analyze Your Competitors
*Gather Your Data
*Analyze Your Facebook Page Data
*Analyze Your Facebook Posts
*Ask Yourself the Right Questions
*What to Do After Checking Page & Post Data
16.
17.
18.
19.
20.
21.
22.
23. Conclusion
• Facebook use our data to provide better services to us and
business people use this platform to manufacture the products
based on people's interest which is a good sign.