Sentiment analysis, also known as opinion mining, uses natural language processing to analyze text and determine if the sentiment expressed is positive, negative, or neutral. It can analyze sentiment at various levels, from entire documents or websites down to specific sentences, aspects of entities, or keywords. Challenges include distinguishing objective from subjective text and resolving ambiguities to accurately determine the sentiment toward a particular entity, person, or topic. Metadata and contextual cues can help with disambiguation.
This is a simple powerpoint presentation meant to be used as a revision tool or for the purpose of self-learning. This covers the different techniques of answering SBQ questions and how to identify and recognise what type of question it is and which asnwering technique to use.
This is a follow-up to the previous exam guide for Social Studies Exam, titled How To Tackle SBQ, which I had created for students to use as a revision tool, in place of myself. It is tailored to meet my students' needs, to help them build their confidence and level of preparedness for the exam.
This is a simple powerpoint presentation meant to be used as a revision tool or for the purpose of self-learning. This covers the different techniques of answering SBQ questions and how to identify and recognise what type of question it is and which asnwering technique to use.
This is a follow-up to the previous exam guide for Social Studies Exam, titled How To Tackle SBQ, which I had created for students to use as a revision tool, in place of myself. It is tailored to meet my students' needs, to help them build their confidence and level of preparedness for the exam.
Describes the anatomy of an annotated bibliography as well as how to develop one.
For a presentation with active hyperlinks, link here: https://docs.google.com/presentation/d/1ykDgN2tlhV-aEGVJqz_ikc0OSDgpXqiLHItKKc0KMFU/edit?usp=sharing
1 Recognizing Assignment Expectations Implied by Key Ver.docxjeremylockett77
1
Recognizing Assignment Expectations Implied by Key
Verbs
In order to do well on assignments, including demonstrating mastery of the course
competencies that are assessed, it is important to have a clear understanding of what you are
expected to do.
Therefore, while the complexity of assignment instructions can sometimes be daunting, it always
pays to focus special attention on the operative verbs that delineate the actions that you should
take.
Certain verbs are frequently used in assignment instructions, but learners are not always clear
about what expectations are usually implied when they are used. Key examples of such verbs
include identify, define, describe, analyze, evaluate, and synthesize.
Verb Example
Identify Bicycle.
Define A bicycle is a two-wheeled vehicle powered by a crank pushed by the feet
with pedals.
Describe This single-speed bicycle has a bright aqua step-through frame, purple
seat, and 26-inch whitewall tires accented with deep purple rims.
Analyze The step-through frame of the bicycle presents less risk of stretching or
ripping clothes compared to models that have a frame with a crossbar.
Evaluate The distinctive coloring of this bicycle, and its step-through frame that
presents less risk of wear and tear on clothing, make this model a good
choice for those with a sophisticated sense of style. However, its fixed
gear ratio would make it undesirable for those who frequently travel up
steep hills.
Synthesize A new line of accessories, including saddle bags and footwear with similar
colors and retro styling, is proposed for marketing with this bicycle.
• When asked to describe something, it is usually not enough to simply name it or label it.
You should summarize all the salient characteristics that are relevant to the question at
hand. When asked to identify something, its name or its label may be enough, but
characterizing it with description may demonstrate a more distinguished level of
proficiency.
• The verb define means to precisely describe the most characteristic features of an
object or objects. Focus on the attributes that are shared by things that are similar;
those that allow you to recognize that these types of thing are different from other types
of things.
2
• To analyze something means to study it closely, often by describing its components and
how they work together to produce some end result. The verbs examine, explain, or
perhaps discuss, are sometimes used when analysis is wanted. Instructions to compare
and contrast generally mean to analyze by looking for similarities and/or differences
between two or more things. If asked to describe a process or interaction and its results
in detail, the desired result may be similar to an analysis.
• Evaluate means to judge the value, significance, quality, or condition of something. Verbs
that may be used in place of evaluate include assess, appraise, gauge, and judge.
Expectations could also be similar if the ...
1 Recognizing Assignment Expectations Implied by Key Ver.docxcroftsshanon
1
Recognizing Assignment Expectations Implied by Key
Verbs
In order to do well on assignments, including demonstrating mastery of the course
competencies that are assessed, it is important to have a clear understanding of what you are
expected to do.
Therefore, while the complexity of assignment instructions can sometimes be daunting, it always
pays to focus special attention on the operative verbs that delineate the actions that you should
take.
Certain verbs are frequently used in assignment instructions, but learners are not always clear
about what expectations are usually implied when they are used. Key examples of such verbs
include identify, define, describe, analyze, evaluate, and synthesize.
Verb Example
Identify Bicycle.
Define A bicycle is a two-wheeled vehicle powered by a crank pushed by the feet
with pedals.
Describe This single-speed bicycle has a bright aqua step-through frame, purple
seat, and 26-inch whitewall tires accented with deep purple rims.
Analyze The step-through frame of the bicycle presents less risk of stretching or
ripping clothes compared to models that have a frame with a crossbar.
Evaluate The distinctive coloring of this bicycle, and its step-through frame that
presents less risk of wear and tear on clothing, make this model a good
choice for those with a sophisticated sense of style. However, its fixed
gear ratio would make it undesirable for those who frequently travel up
steep hills.
Synthesize A new line of accessories, including saddle bags and footwear with similar
colors and retro styling, is proposed for marketing with this bicycle.
• When asked to describe something, it is usually not enough to simply name it or label it.
You should summarize all the salient characteristics that are relevant to the question at
hand. When asked to identify something, its name or its label may be enough, but
characterizing it with description may demonstrate a more distinguished level of
proficiency.
• The verb define means to precisely describe the most characteristic features of an
object or objects. Focus on the attributes that are shared by things that are similar;
those that allow you to recognize that these types of thing are different from other types
of things.
2
• To analyze something means to study it closely, often by describing its components and
how they work together to produce some end result. The verbs examine, explain, or
perhaps discuss, are sometimes used when analysis is wanted. Instructions to compare
and contrast generally mean to analyze by looking for similarities and/or differences
between two or more things. If asked to describe a process or interaction and its results
in detail, the desired result may be similar to an analysis.
• Evaluate means to judge the value, significance, quality, or condition of something. Verbs
that may be used in place of evaluate include assess, appraise, gauge, and judge.
Expectations could also be similar if the .
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative...CITE
5 March 2010 (Friday) | 09:00 - 12:30 | http://citers2010.cite.hku.hk/abstract/69 | Dr. Kwok Ping CHAN, Associate Professor, Department of Computer Science, HKU
Describes the anatomy of an annotated bibliography as well as how to develop one.
For a presentation with active hyperlinks, link here: https://docs.google.com/presentation/d/1ykDgN2tlhV-aEGVJqz_ikc0OSDgpXqiLHItKKc0KMFU/edit?usp=sharing
1 Recognizing Assignment Expectations Implied by Key Ver.docxjeremylockett77
1
Recognizing Assignment Expectations Implied by Key
Verbs
In order to do well on assignments, including demonstrating mastery of the course
competencies that are assessed, it is important to have a clear understanding of what you are
expected to do.
Therefore, while the complexity of assignment instructions can sometimes be daunting, it always
pays to focus special attention on the operative verbs that delineate the actions that you should
take.
Certain verbs are frequently used in assignment instructions, but learners are not always clear
about what expectations are usually implied when they are used. Key examples of such verbs
include identify, define, describe, analyze, evaluate, and synthesize.
Verb Example
Identify Bicycle.
Define A bicycle is a two-wheeled vehicle powered by a crank pushed by the feet
with pedals.
Describe This single-speed bicycle has a bright aqua step-through frame, purple
seat, and 26-inch whitewall tires accented with deep purple rims.
Analyze The step-through frame of the bicycle presents less risk of stretching or
ripping clothes compared to models that have a frame with a crossbar.
Evaluate The distinctive coloring of this bicycle, and its step-through frame that
presents less risk of wear and tear on clothing, make this model a good
choice for those with a sophisticated sense of style. However, its fixed
gear ratio would make it undesirable for those who frequently travel up
steep hills.
Synthesize A new line of accessories, including saddle bags and footwear with similar
colors and retro styling, is proposed for marketing with this bicycle.
• When asked to describe something, it is usually not enough to simply name it or label it.
You should summarize all the salient characteristics that are relevant to the question at
hand. When asked to identify something, its name or its label may be enough, but
characterizing it with description may demonstrate a more distinguished level of
proficiency.
• The verb define means to precisely describe the most characteristic features of an
object or objects. Focus on the attributes that are shared by things that are similar;
those that allow you to recognize that these types of thing are different from other types
of things.
2
• To analyze something means to study it closely, often by describing its components and
how they work together to produce some end result. The verbs examine, explain, or
perhaps discuss, are sometimes used when analysis is wanted. Instructions to compare
and contrast generally mean to analyze by looking for similarities and/or differences
between two or more things. If asked to describe a process or interaction and its results
in detail, the desired result may be similar to an analysis.
• Evaluate means to judge the value, significance, quality, or condition of something. Verbs
that may be used in place of evaluate include assess, appraise, gauge, and judge.
Expectations could also be similar if the ...
1 Recognizing Assignment Expectations Implied by Key Ver.docxcroftsshanon
1
Recognizing Assignment Expectations Implied by Key
Verbs
In order to do well on assignments, including demonstrating mastery of the course
competencies that are assessed, it is important to have a clear understanding of what you are
expected to do.
Therefore, while the complexity of assignment instructions can sometimes be daunting, it always
pays to focus special attention on the operative verbs that delineate the actions that you should
take.
Certain verbs are frequently used in assignment instructions, but learners are not always clear
about what expectations are usually implied when they are used. Key examples of such verbs
include identify, define, describe, analyze, evaluate, and synthesize.
Verb Example
Identify Bicycle.
Define A bicycle is a two-wheeled vehicle powered by a crank pushed by the feet
with pedals.
Describe This single-speed bicycle has a bright aqua step-through frame, purple
seat, and 26-inch whitewall tires accented with deep purple rims.
Analyze The step-through frame of the bicycle presents less risk of stretching or
ripping clothes compared to models that have a frame with a crossbar.
Evaluate The distinctive coloring of this bicycle, and its step-through frame that
presents less risk of wear and tear on clothing, make this model a good
choice for those with a sophisticated sense of style. However, its fixed
gear ratio would make it undesirable for those who frequently travel up
steep hills.
Synthesize A new line of accessories, including saddle bags and footwear with similar
colors and retro styling, is proposed for marketing with this bicycle.
• When asked to describe something, it is usually not enough to simply name it or label it.
You should summarize all the salient characteristics that are relevant to the question at
hand. When asked to identify something, its name or its label may be enough, but
characterizing it with description may demonstrate a more distinguished level of
proficiency.
• The verb define means to precisely describe the most characteristic features of an
object or objects. Focus on the attributes that are shared by things that are similar;
those that allow you to recognize that these types of thing are different from other types
of things.
2
• To analyze something means to study it closely, often by describing its components and
how they work together to produce some end result. The verbs examine, explain, or
perhaps discuss, are sometimes used when analysis is wanted. Instructions to compare
and contrast generally mean to analyze by looking for similarities and/or differences
between two or more things. If asked to describe a process or interaction and its results
in detail, the desired result may be similar to an analysis.
• Evaluate means to judge the value, significance, quality, or condition of something. Verbs
that may be used in place of evaluate include assess, appraise, gauge, and judge.
Expectations could also be similar if the .
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative...CITE
5 March 2010 (Friday) | 09:00 - 12:30 | http://citers2010.cite.hku.hk/abstract/69 | Dr. Kwok Ping CHAN, Associate Professor, Department of Computer Science, HKU
Module 7 Discussion Board Algebra1. What does it mean when s.docxmoirarandell
Module 7 Discussion Board Algebra
1. What does it mean when something grows or decays exponentially? How is that different then rising or falling linearly?
2. Give an example of a real life application of exponential growth or decay. Include the link to a website to show this.
Please answer as two different posts. You need three posts for full credit.
When you reply to others in the class, your replies should contain original thought and/or a follow up question.
Classical Argument
Persuasion and ArgumentPersuasion is the process of drawing conclusions and getting others to accept them and act upon them.Argumentation is the process of drawing conclusions after looking at both sides of an issue and getting others to accept one side based upon logic and careful exploration of facts.
Rhetoric and AudienceRhetoric is the “art of speaking or writing effectively”It is a set of skills used in college and in the business world Effective communication is an important skill in the work forceEffective communication gets our point across without embarrassment for ourselves or others. Effective communication understands what the audience does and does not know about the topic.
Aristotle’s Appeals: Ethos, Logos, and PathosAristotle was a student of Plato. Later, he was a teacher for Alexander the Great. He identified three appeals that can be used to persuade others. Ethos=ethics. Logos=logic. Pathos=emotion
Ethos=Ethics, CredibilityAs a writer, you will establish your credibility through careful research. Articles from experts in the field of study will help you build your ethos in the paper. An advertisement using ethos would be a McDonald’s commercial stating the number of years in business (hence they know how to make a decent hamburger). A car dealership might also state how long they have been at the same location. Or, the dealership might make sure you know“ 2013 Time Dealer of the year award nominee for being among the nation’s most successful auto dealers who also demonstrate a long-standing commitment to community service” (Fuson Automotive).
Logos=LogicWhen using logos in an argument, we provide facts, statistics, evidence, and reason. An automobile commercial stating the vehicle gets x mpg is proving a logos appeal. When gas prices climb, auto makers want to highlight how many miles per gallon the car can travel. Therefore, a car advertised as getting 40 mpg would appeal to a consumer who travels a distance of 40 miles to and from work. If the reader finds the evidence given “logical” it will appeal to the reader.
Pathos=EmotionA pathos appeal will tap into human emotions. Some commercials are deliberately funny and are geared to draw us to the restaurant or product being advertised. From about mid-January until February 14, TV ads focus on how we should tell our “significant” other “I love you.” Commercials such as the “Sandals Resort” plays to adults who want a romantic get-away. We are frequently told ...
A quick explanation showing the process behind students sharing standard written journals through cameraphone imagery. It's a presentation made during the 2021 CESICON in Ireland that recaps successful integration of Microsoft Teams, Class Notebooks, and Moodle to complement taskings set on Moodle.
Presented during a #voicesineducation workshop as "First attempts in learning" by @topgold in LIT-Thurles. Revealing things that broke, things that worked, and ideas that lie ahead.
Here's a first look at The Wundering Moleskine, a set of water colour journals sent to creative people between the ages of six and 106. The Moleskines have social beacons that allow their content to be shared and repurposed as they travel from country to country before returning to the Limerick City of Culture in late 2014.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
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.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
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.
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.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
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
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
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?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Essentials of Automations: The Art of Triggers and Actions in FME
Intro to Sentiment Analysis
1. Intro to Sentiment Analysis
“FAST, NEAT, AVERAGE, FRIENDLY, GOOD, GOOD” was the author’s first sentiment.
2. aka Opinion Mining
Sentiment analysis is opinion mining.
Uses Natural Language Processing.
Dives deep into text analysis.
Leverages computational linguistics.
Develops meta data with business intelligence.
3. Basic Opinion Mining
Construct a range of polarity for opinion markers.
Classify statements by their polarity.
Analyse several levels deep.
Websites are one level.
Authors are another level.
Web page is a third level.
A sentence is a fourth level.
4. Ranges of Polarity
Classify emotional states.
“Angry” can be codified as “upset” or “cross”.
“Sad” may be “disappointed” or “confused”.
“Happy” may be “amazing” or “gorgeous”.
5. Scaling Systems
Some words are negative and deserve to be minus 10.
Some words are neutral and should be equal to five.
Some words are positive and could range from six to 10.
7. Subjectivity and Objectivity
Starts with classifying a given text (no more than a paragraph).
Mark the media text as objective or subjective.
The challenge lies in the subtlety of expression or the compound effect of multiple authors.
Proper analysis normally means removing objective statements from the given text.
8. Aspect-Based Sentiment Analysis
Determine opinions based on features.
Mark the media text as objective or subjective.
The challenge lies in the subtlety of expression or the compound effect of multiple authors.
Proper analysis normally means removing objective statements from the given text.
10. When Something is Ambiguous
Detect entity within text, such as person, place or company.
Get detailed view at entity level, not document-level.
“I love Ireland but I hate traveling on Irish roads.”
11. Disambiguation
Detect entity within text, such as person, place or company.
Get detailed view at entity level, not document-level.
“I love Ireland but I hate traveling on Irish roads.”
12. Entity-Level
Detect entity within text, such as person, place or company.
Get detailed view at entity level, not document-level.
“I love Ireland but I hate traveling on Irish roads.”
13. Keyword-Level Sentiment
Gleans sentiment for every detected keyword.
Much more detailed than view at document-level.
BMW can determine positive comments about cars mention quality of handling.
14. User-Specified Sentiment
You, the analyst, target specific words or phrases.
So you specify a restaurant’s name and return sentiment scores based on that name.
You cull various media texts for sentiment about a specific hotel.
15. Directional Sentiment
Identifies the commentator and emotional range.
First, discover the incident where emotion is expressed.
Second, determine the degree of positive or negative response.
Third, conclude who is mentioning both the product and how negatively.
16. Disambiguation by Location
Identifies the exact point on the earth.
Use contextual cues.
Perhaps where something is posted or where commentator is based.
17. Disambiguation: Meta Data
Meta data provides data about data.
Links can remove ambiguity.
Past geographical movements clarify reach of commentators.
Simple internet searches can provide accurate profile data.
18. Entity Subtypes
Author is a real person.
Author is a man.
Man’s name is Paul O’Connell.
This Paul O’Connell is Munster.
19. Exact Quotations
What was said.
Who said what.
When it was said.
Where it was said.
This exactness provides context.
20. Author Profile
Analyse the text.
Validate the context.
Extract the concept.
Extract the keywords.
Apply to author profile.
Determine what author’s write about.
21. References
Turney and Pang applied methods for detecting polarity at the document level.
Pang and Snyder classified documents on a multi-way scale, such as “five stars”.
Katie Paine wrote “Measure What Matters”
22. Useful Links
For Immediate Release G+ Community
Marketing Over Coffee Podcast
KD Paine’s Blog
The Alchemy Blog
This is the first look at sentiment analysis during a discussion with business students in the Limerick Institute of Technology in October 2013. It is based on professional experience shared by Bernard @topgold Goldbach, Katie @kdpaine Paine, Neville @jangles Hobson, Christopher @cspenn Penn and The Alchemy Group. The author of this deck lives at http://www.insideview.ie.
Sentiment analysis (also known as opinion mining ) refers to the use of natural language processing , text analysis and computational linguistics to identify and extract subjective information in source materials. Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation (see appraisal theory ), affective state (that is to say, the emotional state of the author when writing), or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader).
A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level — whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as "angry," "sad," and "happy."
Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as "angry," "sad," and "happy."
A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral or positive sentiment with them are given an associated number on a -10 to +10 scale (most negative up to most positive) and when a piece of unstructured text is analyzed using natural language processing , the subsequent concepts are analyzed for an understanding of these words and how they relate to the concept [ citation needed ] . Each concept is then given a score based on the way sentiment words relate to the concept, and their associated score. This allows movement to a more sophisticated understanding of sentiment based on an 11 point scale. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text.
Another research direction is subjectivity/objectivity identification . According to Wikipedia, this task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective. This problem can sometimes be more difficult than polarity classification: the subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Results are largely dependent on the definition of subjectivity used when annotating texts. (Su) As Pang’s research shows, removing objective sentences from a document before classifying its polarity helped improve performance.
Another research direction is subjectivity/objectivity identification . This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective. This problem can sometimes be more difficult than polarity classification: the subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions). Results leargely depend on the definition of subjectivity used when annotating texts. (Su) Removing objective sentences from a document before classifying its polarity helped improve performance. (Pang)
The more fine-grained analysis model is called the feature/aspect-based sentiment analysis . It refers to determining the opinions or sentiments expressed on different features or aspects of entities, e.g., of a cell phone, a digital camera, or a bank. A feature or aspect is an attribute or component of an entity, e.g., the screen of a cell phone, or the picture quality of a camera. This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral. More detailed discussions about this level of sentiment analysis can be found in Liu's NLP Handbook chapter, "Sentiment Analysis and Subjectivity”.
Ambiguous: open to more than one interpretation. Disambiguation: clarification that follows from the removal of ambiguity.
AMBIGUOUS. You need to provide sentiment data for every detected entity within text, such as person, place, organization. You need to give clients a more detailed view than document-level sentiment analysis.
REMOVE AMBIGUITY WITH DISAMBIGUATION TACTICS.
Entity-Level Sentiment Analysis provides sentiment data for every detected entity within text, such as person, place, organization. Alchemy algorithms do this kind of work.
Keyword-Level Sentiment Analysis provides sentiment data for every detected keyword so that instead of generating sentiment by document, it’s possible to generate sentiment for keywords within the document. For example, when analyzing car posts, determine that of the 70% posts that were positive, 80% of them mentioned road handling and 30% complained about the road tax.
User-Specified Sentiment Analysis allows the user to target specific words or phrases. For instance, specifying a movie title returns sentiment scores based on that phrase. This can be done by hand or by Alchemy API.
Directional Sentiment Analysis reveals who is emitting the sentiment. For example, if a person spoke negatively about a product, determine not only that the product was mentioned negatively, but who mentioned the product negatively.
Disambiguation: Dominos in Limerick or Dominos all across Ireland? Since one business can have multiple locations, you need to be able to distinguish by location. This effectively means you are using a disambiguation technique to ferret out the various locations. You can often located contextual cues within the text or by geolocation in a Foursquare tip.
Disambiguation: Additional Information Disambiguation provides additional information for the people, places and things mentioned in a document such as links to their official websites, Wikipedia pages, geographical coordinates and more.
Entity Subtypes: Paul O’Connell, a Person and an Athlete. In addition to the most common entity types, such as person or organization, you should seek to identify subtypes. For example, your basic text analysis services will identify Paul O’Connell as a man but you need to know he is a prominent rugby player for Munster. That way, you know he is an influencer.
Quotations Extraction: What Was Said and Who Said It Entity extraction determines what was said, but quotations extraction tells you who said what by extracting a quote and attributing it back to the person or organization responsible. Knowing that a company was mentioned in a piece of text is important, however, finding out who mentioned the company gives a fuller story. For example, entity extraction can provide you with a list of news articles where a topic and Willie O’Dea were both mentioned, but quotations extraction can provide you with a list of news articles where Willie O’Dea was quoted mentioning that topic.
Author Extraction For data to be meaningful, your text analysis service must be able to contribute to building an author profile. Comments on web pages, tweets, image collections, and site critiques provide excellent data sets. Author extraction combined with concept extraction, keyword extraction, and entity extraction provides information on what topics specific authors write about.
Early work in that area includes Turney and Pang who applied different methods for detecting the polarity of product reviews and movie reviews respectively. This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang and Snyder . This expanded the basic task of classifying a movie review as either positive or negative to predicting star ratings on either a 3 or a 4 star scale, while Snyder performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale). Peter Turney (2002). "Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews". Proceedings of the Association for Computational Linguistics . pp. 417–424. Bo Pang; Lillian Lee and Shivakumar Vaithyanathan (2002). "Thumbs up? Sentiment Classification using Machine Learning Techniques" . Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) . pp. 79–86. Bo Pang; Lillian Lee (2005). "Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales" . Proceedings of the Association for Computational Linguistics (ACL) . pp. 115–124. Benjamin Snyder; Regina Barzilay (2007). "Multiple Aspect Ranking using the Good Grief Algorithm" . Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL) . pp. 300–307.
The FIR Community is at https://plus.google.com/communities/112349929544876511942 MOC is http://marketingovercoffee.com KD Paine blogs at http://kdpaine.blogs.com/ Alchemy’s blog is at http://www.alchemyapi.com/blog/
The Moodle Document concerning sentiment analysis is at http://bit.ly/crm-document04 but that might change as the years go on. MOC is http://marketingovercoffee.com KD Paine blogs at http://kdpaine.blogs.com/ Alchemy’s blog is at http://www.alchemyapi.com/blog/ You can contact the author by using the nic “topgold” on all good social networks. This document was written to support the business curriculum in LIT.ie on 11 October 2013.