Big Data Munich – Decision Automation and Big DataLars Trieloff
My presentation from Big Data Munich: How decision automation based on big data and machine learning can help you run a better business and avoid common cognitive biases.
The power of Data Visualization: Analyzing how world economics affects Averag...wellington palma
This presentation showcases my ability to tell a story with large data sets. The emphasize here, is to show how powerful visualization can be in calling attention to an issue. Tableau was the tool that was utilized to build the data visualization. In addition to demonstrating how powerful visualization can be, the fun pop quiz at the end demonstrates how marketers, managers,fiance professionals, and other thought leaders can think about how visualization can call attention to pressing issues.
The usual goal of sentiment analysis is to provide numeric measures of positive or negative valence for brands, products, and commodities, which can be aggregated over time or geographical regions to analyze patterns and trends. Dr. Shlomo Argamon discusses some new methods he and his team are developing which extend this paradigm in two ways. First, their systems analyze more aspects of each individual sentiment expression, including different types of attitude ("unwieldy" vs. "unreliable"), comparisons ("X is better than Y"), evaluative trends ("X is improving"), and modality ("possibly" vs. "likely" vs. "definitely"). Secondly, they are combining sentiment analysis with their methods for automated authorship profiling, which label texts with author characteristics such as gender, age, native language, education level, and so forth. When this is done, a new type of analysis emerges: data mining can be used to find "sentimental market segments", discovering, for example, that opinion is trending upwards for males aged 20-30, but downwards 30-50 year-olds who did not attend college. He presents some of their research results and discuss the implications for future applications and developments in sentiment analytics.
Lexalytics CEO Jeff Caitlin's presentation at the 2015 Sentiment Analysis Symposium.
The ability to parse syntax correctly is vital for accurate sentiment analysis. It’s not enough for a program to simply weigh individual words for their sentiment. Sentences can contain several different entities towards which different sentiments are being expressed, depending on how the sentence is ordered.
Text analytics can be leveraged in many areas of market research. Tom gives real case study examples of how his firm has merged text analytics with traditional market research and helped fortune 500 clients with customer satisfaction, competitive intelligence, and segmentation.
He discusses techniques that provide validation through triangulation. Going beyond verbatim concept, themes and negative/positive/neutral sentiment, Anderson Analytics also leverages psychological content analysis which utilize a priori word choice models and compares these to normative, category and demographic specific databases.
Big Data Munich – Decision Automation and Big DataLars Trieloff
My presentation from Big Data Munich: How decision automation based on big data and machine learning can help you run a better business and avoid common cognitive biases.
The power of Data Visualization: Analyzing how world economics affects Averag...wellington palma
This presentation showcases my ability to tell a story with large data sets. The emphasize here, is to show how powerful visualization can be in calling attention to an issue. Tableau was the tool that was utilized to build the data visualization. In addition to demonstrating how powerful visualization can be, the fun pop quiz at the end demonstrates how marketers, managers,fiance professionals, and other thought leaders can think about how visualization can call attention to pressing issues.
The usual goal of sentiment analysis is to provide numeric measures of positive or negative valence for brands, products, and commodities, which can be aggregated over time or geographical regions to analyze patterns and trends. Dr. Shlomo Argamon discusses some new methods he and his team are developing which extend this paradigm in two ways. First, their systems analyze more aspects of each individual sentiment expression, including different types of attitude ("unwieldy" vs. "unreliable"), comparisons ("X is better than Y"), evaluative trends ("X is improving"), and modality ("possibly" vs. "likely" vs. "definitely"). Secondly, they are combining sentiment analysis with their methods for automated authorship profiling, which label texts with author characteristics such as gender, age, native language, education level, and so forth. When this is done, a new type of analysis emerges: data mining can be used to find "sentimental market segments", discovering, for example, that opinion is trending upwards for males aged 20-30, but downwards 30-50 year-olds who did not attend college. He presents some of their research results and discuss the implications for future applications and developments in sentiment analytics.
Lexalytics CEO Jeff Caitlin's presentation at the 2015 Sentiment Analysis Symposium.
The ability to parse syntax correctly is vital for accurate sentiment analysis. It’s not enough for a program to simply weigh individual words for their sentiment. Sentences can contain several different entities towards which different sentiments are being expressed, depending on how the sentence is ordered.
Text analytics can be leveraged in many areas of market research. Tom gives real case study examples of how his firm has merged text analytics with traditional market research and helped fortune 500 clients with customer satisfaction, competitive intelligence, and segmentation.
He discusses techniques that provide validation through triangulation. Going beyond verbatim concept, themes and negative/positive/neutral sentiment, Anderson Analytics also leverages psychological content analysis which utilize a priori word choice models and compares these to normative, category and demographic specific databases.
Webinar - The Golden Key to Successful Grant Requests - 2018-05-10TechSoup
This presentation will help nonprofits uncover the most effective methods for documenting the need statement, as well as ways to use that information to engage the reader.
Tutorial on 'Explainability for NLP' given at the first ALPS (Advanced Language Processing) winter school: http://lig-alps.imag.fr/index.php/schedule/
The talk introduces the concepts of 'model understanding' as well as 'decision understanding' and provides examples of approaches from the areas of fact checking and text classification.
Exercises to go with the tutorial are available here: https://github.com/copenlu/ALPS_2021
Measuring Risk - What Doesn’t Work and What DoesJody Keyser
The topics for this webinar include:
The Problem – Why your method may be a “management placebo” and why that is the biggest risk you have Problems that many methods ignore – and problems some methods introduce What Does Work – Studies reveal some methods show consistent, measurable improvements on the forecasts and decisions of managers
Examples of Real Improvements
Overview of Applied Information Economics (AIE) Process Common Objections to quantitative methods and the misconceptions behind them
Questions & Answers
· Practical Op-Amps – Understanding Op Amp Parameters1. Search t.docxoswald1horne84988
· Practical Op-Amps – Understanding Op Amp Parameters
1. Search the Internet for a LM741 datasheet. texas Instruments can be a good source.
2. Answer the following questions:
1. Given a signal with a peak voltage of 10V and a frequency of 2kHz, calculate the SR for Figure A.4, pg. 212.
2. Given a total noise voltage of ent = 1mV, current noise In = 2pA/sqrt(Hz), and a source resistance Rs = 2kohms, calculate the voltage noise, Vn parameter. Requires solving for Vn in equation (A.3), pg. 207.
3. Given that the maximum frequency without distortion fmax is defined as fmax = SR/2πVp, calculate SR with fmax = 3kHz, and Vpp = 15.
4. Review the “LM741 datasheet” in your course materials and provide the following information:
1. Supply voltage range
2. Input Offset Voltage (typical) and (max)
3. Large Signal Voltage Gain (min) and (typical)
4. CMRR (typical)
5. SVRR (typical)
6. SR (typical)
7. How many BJT’s comprise the internal circuitry?
3. Scan all work and save it for upload with the title: “HW3_StudentID”, with your student id substituted in the file name. Show all work for full credit.
4. Upload file “HW3_StudentID”
INTEGRATIVE FRAMEWORK FOR UNDERSTANDING AND APPLYING OB Demographics are a key input that affects important OB processes, most particularly perceptions, which in turn affect the individual-level outcome of well-being/flourishing and the organizational outcomes of being an employer of choice and corporate reputation. Page 111 winning at work PERCEPTION PLAYS A KEY ROLE IN GETTING A JOB A recent survey of 400 humanresource professionals uncovered results that are important to college graduates looking for a job. The overwhelming conclusion? That “entry-level workers are an entitled, unprofessional bunch.” About 45 percent of the HR professionals believed that the work ethic of new college graduates had slipped in the past five years.1 Let’s consider how you can avoid being perceived so negatively. IMPRESSIONS FROM SOCIAL MEDIA The Internet is a gold mine of information for recruiters, and some of it creates a bad impression. Photos of drunken behavior, or rants with foul language or that “bash” your employer, won’t improve a recruiter’s perception. You need to be careful about your online presence because approximately 20 percent of all organizations browse sites like LinkedIn, Facebook, MySpace, and Twitter to help screen employees. Consider the experience of Pete Maulik, chief strategy officer at Fahrenheit 212. Maulik was ready to make an offer to an applicant, but first decided to check out the man’s LinkedIn profile—and decided that the applicant was not a team player. “He took credit for everything short of splitting the atom,” Mr. Maulik said. “Everything was ‘I did this.’ He seemed like a lone wolf. He did everything himself.” Maulik recalls another good applicant who used his Twitter account “to disparage just about every new innovation in the marketplace.” Maulik concluded that the applicant “was much more comfortabl.
Snakes on a Plane and Your Crisis Communications PlanThomas Ciesielka
How to effectively create a crisis communication plan, including:
• How to prepare you and your team to effectively manage a communication crisis
• Help leadership, staff, third-parties respond in a united and proficient way to a crisis
• Tactics to best communicate critical and privileged information to the media,
stakeholders, and the public
• The importance of creating a crisis communication plan and how to start one if
none exists; starting points for evaluating your current crisis communication plan
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
More Related Content
Similar to Sentiment Analysis Symposium 2010, welcoming address, Seth Grimes
Webinar - The Golden Key to Successful Grant Requests - 2018-05-10TechSoup
This presentation will help nonprofits uncover the most effective methods for documenting the need statement, as well as ways to use that information to engage the reader.
Tutorial on 'Explainability for NLP' given at the first ALPS (Advanced Language Processing) winter school: http://lig-alps.imag.fr/index.php/schedule/
The talk introduces the concepts of 'model understanding' as well as 'decision understanding' and provides examples of approaches from the areas of fact checking and text classification.
Exercises to go with the tutorial are available here: https://github.com/copenlu/ALPS_2021
Measuring Risk - What Doesn’t Work and What DoesJody Keyser
The topics for this webinar include:
The Problem – Why your method may be a “management placebo” and why that is the biggest risk you have Problems that many methods ignore – and problems some methods introduce What Does Work – Studies reveal some methods show consistent, measurable improvements on the forecasts and decisions of managers
Examples of Real Improvements
Overview of Applied Information Economics (AIE) Process Common Objections to quantitative methods and the misconceptions behind them
Questions & Answers
· Practical Op-Amps – Understanding Op Amp Parameters1. Search t.docxoswald1horne84988
· Practical Op-Amps – Understanding Op Amp Parameters
1. Search the Internet for a LM741 datasheet. texas Instruments can be a good source.
2. Answer the following questions:
1. Given a signal with a peak voltage of 10V and a frequency of 2kHz, calculate the SR for Figure A.4, pg. 212.
2. Given a total noise voltage of ent = 1mV, current noise In = 2pA/sqrt(Hz), and a source resistance Rs = 2kohms, calculate the voltage noise, Vn parameter. Requires solving for Vn in equation (A.3), pg. 207.
3. Given that the maximum frequency without distortion fmax is defined as fmax = SR/2πVp, calculate SR with fmax = 3kHz, and Vpp = 15.
4. Review the “LM741 datasheet” in your course materials and provide the following information:
1. Supply voltage range
2. Input Offset Voltage (typical) and (max)
3. Large Signal Voltage Gain (min) and (typical)
4. CMRR (typical)
5. SVRR (typical)
6. SR (typical)
7. How many BJT’s comprise the internal circuitry?
3. Scan all work and save it for upload with the title: “HW3_StudentID”, with your student id substituted in the file name. Show all work for full credit.
4. Upload file “HW3_StudentID”
INTEGRATIVE FRAMEWORK FOR UNDERSTANDING AND APPLYING OB Demographics are a key input that affects important OB processes, most particularly perceptions, which in turn affect the individual-level outcome of well-being/flourishing and the organizational outcomes of being an employer of choice and corporate reputation. Page 111 winning at work PERCEPTION PLAYS A KEY ROLE IN GETTING A JOB A recent survey of 400 humanresource professionals uncovered results that are important to college graduates looking for a job. The overwhelming conclusion? That “entry-level workers are an entitled, unprofessional bunch.” About 45 percent of the HR professionals believed that the work ethic of new college graduates had slipped in the past five years.1 Let’s consider how you can avoid being perceived so negatively. IMPRESSIONS FROM SOCIAL MEDIA The Internet is a gold mine of information for recruiters, and some of it creates a bad impression. Photos of drunken behavior, or rants with foul language or that “bash” your employer, won’t improve a recruiter’s perception. You need to be careful about your online presence because approximately 20 percent of all organizations browse sites like LinkedIn, Facebook, MySpace, and Twitter to help screen employees. Consider the experience of Pete Maulik, chief strategy officer at Fahrenheit 212. Maulik was ready to make an offer to an applicant, but first decided to check out the man’s LinkedIn profile—and decided that the applicant was not a team player. “He took credit for everything short of splitting the atom,” Mr. Maulik said. “Everything was ‘I did this.’ He seemed like a lone wolf. He did everything himself.” Maulik recalls another good applicant who used his Twitter account “to disparage just about every new innovation in the marketplace.” Maulik concluded that the applicant “was much more comfortabl.
Snakes on a Plane and Your Crisis Communications PlanThomas Ciesielka
How to effectively create a crisis communication plan, including:
• How to prepare you and your team to effectively manage a communication crisis
• Help leadership, staff, third-parties respond in a united and proficient way to a crisis
• Tactics to best communicate critical and privileged information to the media,
stakeholders, and the public
• The importance of creating a crisis communication plan and how to start one if
none exists; starting points for evaluating your current crisis communication plan
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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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.
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Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
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In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
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Download the full report from here:
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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.
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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;
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Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
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3. Two assertions: Human communications are inherently subjective. Opinion often masquerades as Fact.
4. Facts and Feelings The unemployment rate is 9.7%. Unemployment is WAY TOO HIGH!! The unemployment rate is higher than it was two years ago (5.1%). Former U.S. Federal Reserve Chairman Alan Greenspan said on Tuesday that the global recession will "surely be the longest and deepest" since the 1930s, adding that the Obama administration's Troubled Asset Relief Program will be insufficient to plug the yawning financial gap. [Reuters, Feb 18, 2009] Benjamin Bernanke is doing a better job than Greenspan. www.google.com/publicdata
5. We have a decision need, for monitoring, measurement, and analysis that support action. We = Consumers Marketers Managers Competitors Government Politicians
6. Questions... What are people saying? What’s hot? What are they saying about {topic|person|product} X? ... about X versus {topic|person|product} Y? How has opinion about X and Y trended/evolved? How has opinion correlated with {our|competitors’|general} {news|marketing|sales|events}? What’s behind opinion, the root causes? Who are opinion leaders? How does sentiment propagate across multiple channels?
8. Methods are. Yet counting term hits, in one source, doesn’t take you far. Good or bad? What’s behind the posts?
9. Beyond counting: “Sentiment analysis is the task of identifying positive and negative opinions, emotions, and evaluations.” -- Wilson, Wiebe & Hoffman, 2005, “Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis” Ingredients: Structured and unstructured sources. Subjectivity. Polarity. Intensity.
10. There are many complications. Simplified: Multiple levels: Corpus / data space, i.e., across multiple sources. Document. Statement / sentence. Entity / topic / concept. Human language is noisy and chaotic! Jargon, slang, irony, ambiguity, anaphora, polysemy, synonymy, etc. Context is key. Discourse analysis comes into play. Sentiment holder ≠ object: Greenspan said the recession will…
11.
12.
13. An accuracy aside: [WWH 2005] describes an inter-annotator agreement test. 10 documents w/ 447 subjective expressions.The two annotators agree on 82% of cases. Excluding uncertain subjective expressions (18%) boosts agreement to 90%.
14. Putting aside benefits of automation, how can machine accuracy approach human sensitivity? Claim: You fall short with (only) -- Doc-level analysis. Keyword-based analysis. You need strong natural language processing (NLP). You can also boost accuracy by, for example, ...
15. HappySadAngry Energetic Confused Aggravated Bouncy Crappy Angry Happy Crushed Bitchy Hyper Depressed Enraged Cheerful Distressed Infuriated Ecstatic Envious Irate Excited Gloomy Pissed off Jubilant Guilty Giddy Intimidated Giggly Jealous Lonely Rejected Sad Scared ----------------------- The three prominent mood groups that emerged from K-Means Clustering on the set of LiveJournalmood labels.