The document discusses global analytics including text, speech, sentiment, and sense. It provides a history of analytics from the 1950s to present day, covering developments in extracting information from text, modeling patterns and insights, and connecting information. It also explores challenges and opportunities in natural language processing, including context, interaction, sentiment analysis, question answering, and cross-lingual implementation.
These slides cover the final defense presentation for my Doctorate degree. Th...Eric Brown
These slides cover the final defense presentation for my Doctorate degree. The topic: Analysis of Twitter Messages for Sentiment and Insight for use in Stock Market Decision Making.
An informative session on Amazon Mechanical Turk where you will learn how your company can leverage the human crowd for human sentiment analysis of content such as tweets, articles, RSS feeds and blog posts. This session digs into the details of getting started and provides information on how to be successful so you get accurate results. Additionally, FreedomOSS will share their experiences designing and managing sentiment tasks and demo's their CrowdControl crowdsourcing platform that is built on top of Mechanical Turk.
Twitter Sentiment & Investing - modeling stock price movements with twitter s...Eric Brown
In this presentation, I provide an overview of my research into using twitter sentiment and message volume as inputs into modeling stock price movements. A quick and dirty linear regression model using Twitter Sentiment, the Number of Tweets per day, the VIX Closing price and the VIX Price change delivers a simple model for the S&P 500 SPY ETF that has an accuracy of 57% over 6 months (tested on out-of sample data). This model was built using data from July 11 2011 to August 11 2011.
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
A set of practical strategies and techniques for tackling vagueness in data modeling and creating models that are semantically more accurate and interoperable.
DataKind SG sharing on our first DataDive with Humanitarian Organization for Migration Economics (HOME) and Earth Hour.
Know of other non-profits we can help? Reach out to singapore@datakind.org or drop me a note =)
Research Proposal- Integrating Need for CognitionLeslie McFarlin
A proposal for a major retailer who wanted to better understand aspects of users' attitudes and how they related to people's responsiveness to email campaigns.
These slides cover the final defense presentation for my Doctorate degree. Th...Eric Brown
These slides cover the final defense presentation for my Doctorate degree. The topic: Analysis of Twitter Messages for Sentiment and Insight for use in Stock Market Decision Making.
An informative session on Amazon Mechanical Turk where you will learn how your company can leverage the human crowd for human sentiment analysis of content such as tweets, articles, RSS feeds and blog posts. This session digs into the details of getting started and provides information on how to be successful so you get accurate results. Additionally, FreedomOSS will share their experiences designing and managing sentiment tasks and demo's their CrowdControl crowdsourcing platform that is built on top of Mechanical Turk.
Twitter Sentiment & Investing - modeling stock price movements with twitter s...Eric Brown
In this presentation, I provide an overview of my research into using twitter sentiment and message volume as inputs into modeling stock price movements. A quick and dirty linear regression model using Twitter Sentiment, the Number of Tweets per day, the VIX Closing price and the VIX Price change delivers a simple model for the S&P 500 SPY ETF that has an accuracy of 57% over 6 months (tested on out-of sample data). This model was built using data from July 11 2011 to August 11 2011.
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
A set of practical strategies and techniques for tackling vagueness in data modeling and creating models that are semantically more accurate and interoperable.
DataKind SG sharing on our first DataDive with Humanitarian Organization for Migration Economics (HOME) and Earth Hour.
Know of other non-profits we can help? Reach out to singapore@datakind.org or drop me a note =)
Research Proposal- Integrating Need for CognitionLeslie McFarlin
A proposal for a major retailer who wanted to better understand aspects of users' attitudes and how they related to people's responsiveness to email campaigns.
Sentiment Analysis: The Marketplace and ProvidersSeth Grimes
Short tutorial presentation by Seth Grimes, presented as part of the Practical Sentiment Analysis tutorial on May 7, 2013, prior to the Sentiment Analysis Symposium, http://sentimentsymposium.com/
언제 어디서나
여러분이 걷고 있거나, 운전 중 이거나, 대중교통을 이용하고 있을 때, 눈이 피로해 쉬고 싶을 때, 너무 바빠서 여러 일을 한꺼번에 해야 할 때
웹이나 앱사용을 도와주는 기능이 필요합니다.
언제 어디서나, 장애와 남녀노소, 언어에 관계없이
누구나 웹과 앱을 등을 수 있도록 해주는
실시간 음성변환 기술을 활용한
웹톡스가 이런 문제를
해결해 줍니다.
Presentation regarding development of text-to-speech system for Gujarati. Input would be arbitrary Gujarati unicode text while output would equivalent speech sound.
Technology Frontiers: Text, Sentiment, and Sense by Seth Grimes of Alta Plana...InsightInnovation
A basic definition: Text analytics transforms text-sourced information into data to help you generate insights that fuel better-informed business decision-making. Methods are applied to online and social information, as well as enterprise feedback, to complement and extend traditional and emerging research methods. Text analytics is the leading opinion mining technique, evolving to link emotion and intent signals to behaviors, profiles, and transactions. If text analytics isn’t part of your data toolkit, it should be; if you’re already exploiting text analytics, you’ll want to stay on top of developments. Seth Grimes, in this What’s Next talk, will tell you how.
A New Approach to Real Time Intent and Sentiment Analysisxale4
Detecting actionable intent in online and messaging text-based posts, especially in near real-time, is becoming significant for customer acquisition, marketing, support and product management. Location based services and wide-spread adoption of mobile devices also increases the importance of intent such as intent to buy or making a commitment, or an occurrence of an event. In this talk and demonstration, we will present a non-traditional approach developed by Cruxly to intent as well as sentiment analysis.
“C’mon – You Should Read This”: Automatic Identification of Tone from Languag...Waqas Tariq
Information extraction researchers have recently recognized that more subtle information beyond the basic semantic content of a message can be communicated via linguistic features in text, such as sentiments, emotions, perspectives, and intentions. One way to describe this information is that it represents something about the generator’s mental state, which is often interpreted as the tone of the message. A current technical barrier to developing a general-purpose tone identification system is the lack of reliable training data, with messages annotated with the message tone. We first describe a method for creating the necessary annotated data using human-based computation, based on interactive games between humans trying to generate and interpret messages conveying different tones. This draws on the use of game with a purpose methods from computer science and wisdom of the crowds methods from cognitive science. We then demonstrate the utility of this kind of database and the advantage of human-based computation by examining the performance of two machine learning classifiers trained on the database, each of which uses only shallow linguistic features. Though we already find near-human levels of performance with one classifier, we also suggest more sophisticated linguistic features and alternate implementations for the database that may improve tone identification results further.
Abstract: Speech technology and systems in human computer interaction have witnessed a stable and remarkable advancement over the last two decades. Today, speech technologies are commercially available for an unlimited but interesting range of tasks. These technologies enable machines to respond correctly and reliably to human voices, and provide useful and valuable services. This thesis presents the characteristics of emotion in voice and on that basis propose a new method to detecting emotion in a simplified way by using a prosodic features and spectral from speech. We classify seven emotions: happy, anger, fear, disgust, sadness and neutral inner state. This thesis discusses the method to extract features from a recorded speech sample, and using those features, to detect the emotion of the subject. Every emotion comprises different vocal parameters exhibiting diverse characteristics of speech, which is used for preliminary classification. Then Mel-Frequency Cepstrum Coefficient (MFCC) method was used to extract spectral features. The MFCC coefficients were again trained by Artificial Neural Network (ANN) which then classifies the input in particular emotional class.
Manichean Progress: Positive and Negative States of the Art in Web-Scale Data...Lewis Shepherd
Discussion of current Microsoft Research projects and prospects which help drive open innovation and agile experimentation via cloud-based services; and projects which aim at advancing the state-of-the-art in knowledge representation and reasoning under uncertainty at web scale. I also begin by discussing potential malign implications of mass automated implementations of linked-data systems, as functions of what governments (and users of public data) can/should/shouldn’t do in promoting mass activity.
Content Analysis Overview for Persona DevelopmentPamela Rutledge
After developing an Ad Hoc persona as the core of your engagement strategy, it's important to test your assumptions against real people and real data. Content analysis is a methodology for evaluating text-based data that can be gathered from social media tools.
Opinion mining in hindi language a surveyijfcstjournal
Opinions are very important in the life of human beings. These Opinions helped the humans to carry out
the decisions. As the impact of the Web is increasing day by day, Web documents can be seen as a new
source of opinion for human beings. Web contains a huge amount of information generated by the users
through blogs, forum entries, and social networking websites and so on To analyze this large amount of
information it is required to develop a method that automatically classifies the information available on the
Web. This domain is called Sentiment Analysis and Opinion Mining. Opinion Mining or Sentiment Analysis
is a natural language processing task that mine information from various text forms such as reviews, news,
and blogs and classify them on the basis of their polarity as positive, negative or neutral. But, from the last
few years, enormous increase has been seen in Hindi language on the Web. Research in opinion mining
mostly carried out in English language but it is very important to perform the opinion mining in Hindi
language also as large amount of information in Hindi is also available on the Web. This paper gives an
overview of the work that has been done Hindi language.
Microscope, macroscope and zoom lens: close , distant and scalable reading in the Humanities. Also being an argument for the use of corpus linguistics tools and methods across multiple disciplines.
Hi There, please kindly use my PPT for powering your learning, please let me know if you want to discuss more.
Email : silviananda.putrierito@gmail.com
Creating an AI Startup: What You Need to KnowSeth Grimes
Seth Grimes presented "Creating an AI Startup: What You Need to Know," at a May 20, 2021 Launch Annapolis + Maryland AI (https://www.meetup.com/MarylandAI) program, focusing on opportunity and resources for Maryland tech entrepreneurs.
Efficient Deep Learning in Natural Language Processing Production, with Moshe...Seth Grimes
Moshe Wasserblat, Intel AI, presents on Efficient Deep Learning in Natural Language Processing Production to an online NLP meetup audience, August 3, 2020. Visit https://www.meetup.com/NY-NLP for the New York NLP meetup.
From Customer Emotions to Actionable Insights, with Peter DorringtonSeth Grimes
From Customer Emotions to Actionable Insights -- A presentation by Peter Dorrington, founder, XMplify Consulting, at the 2020 CX Emotion conference (https://cx-emotion.com), July 22, 2020.
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AISeth Grimes
Dan Lee from Dentuit AI presented an Intro to Deep Learning for Medical Image Analysis at the Maryland AI meetup (https://www.meetup.com/Maryland-AI), May 27, 2020. Visit https://www.youtube.com/watch?v=xl8i7CGDQi0 for video.
Emotion AI refers to a set of technologies -- natural language processing, voice tech, facial coding, neuroscience, and behavioral analytics -- applied to interactions to extract, convey, and induce emotion. Emotion AI is a presentation by Seth Grimes at AI for Human Language, March 5, 2020 in Tel Aviv.
Text Analytics for NLPers, a presentation by Seth Grimes, created for the December 2, 2019 Natural Language Processing-New York (NYC-NLP) meetup, https://www.meetup.com/NLP-NY/events/266093296/
Our FinTech Future – AI’s Opportunities and Challenges? Seth Grimes
"Our FinTech Future – AI’s Opportunities and Challenges?" is a presentation by Jim Kyung-Soo Liew, Ph.D. to the Artificial Intelligence Maryland (MD-AI) meetup (https://www.meetup.com/Maryland-AI/), November 20, 2019. Dr. Liew is Co-Founder of SoKat.co and Associate Professor at Johns Hopkins Carey Business School.
Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Auto...Seth Grimes
Presentation by Nathan Schneider, Assistant Professor of Linguistics and Computer Science at Georgetown University, to the Washington DC Natural Language Processing meetup, October 14, 2019 (https://www.meetup.com/DC-NLP/events/264894589/).
The Ins and Outs of Preposition Semantics: Challenges in Comprehensive Corpu...Seth Grimes
Presentation by Nathan Scheider, Georgetown University, to the Washington DC Natural Language Processing meetup, October 14, 2019, https://www.meetup.com/DC-NLP/events/264894589/.
Nick Schmidt of BLDS, LLC to the Maryland AI meetup, June 4, 2019 (https://www.meetup.com/Maryland-AI). Nick discusses ideas of fairness and how they apply to machine learning. He explores recent academic work on identifying and mitigating bias, and how his work in lending and employment can be applied to other industries. Nick explains how to measure whether an algorithm is fair and also demonstrate the techniques that model builders can use to ameliorate bias when it is found.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Global Analytics: Text, Speech, Sentiment, and Sense
1. Global Analytics: Text, Speech,
Sentiment, and Sense
Seth Grimes
Alta Plana Corporation
@sethgrimes
December 4, 2014
2. Global Analytics: Text, Speech, Sentiment, and Sense
2
“Reading from Text is a Hard Problem”
Thus the Orb he roam'd
With narrow search; and with inspection deep
Consider'd every Creature, which of all
Most opportune might serve his Wiles.
-- John Milton, Paradise Lost
LT-Accelerate – 4 December, 2014
Eugène
Delacroix,
St. Michael
Defeats the
Devil
3. Global Analytics: Text, Speech, Sentiment, and Sense
3
“Reading from Text is a Hard Problem”
Thus the Orb he roam'd
With narrow search; and with inspection deep
Consider'd every Creature, which of all
Most opportune might serve his Wiles.
-- John Milton, Paradise Lost
LT-Accelerate – 4 December, 2014
Eugène
Delacroix,
St. Michael
Defeats the
Devil
Data Space,
Indexing
Searc
h
Analysis
Intent,
Goals
Context
5. Global Analytics: Text, Speech, Sentiment, and Sense
5
Analytics is the systematic application of
algorithmic methods that derive and deliver
information, typically expressed
quantitatively, whether in the form of
indicators, tables, visualizations, or models.
• Systematic means formal & repeatable.
• Algorithmic contrasts with heuristic.
Analytics creates and/or applies models.
LT-Accelerate – 4 December, 2014
6. Global Analytics: Text, Speech, Sentiment, and Sense
6
Models make the unstructured computable.
LT-Accelerate – 4 December, 2014
http://www.tropicalisland.de/NYC_New_York_
Brooklyn_Bridge_from_World_Trade_Center_
b.jpg
x(t) = t
y(t) = ½ a (et/a + e-t/a)
= acosh(t/a)
http://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg
7. Global Analytics: Text, Speech, Sentiment, and Sense
7
Sixty+ years of analysis & modelling
progress:
LT-Accelerate – 4 December, 2014
Text
Numbers
Patterns & Insights
Connections
Interactions
8.
9. Document
input and
processing
Knowledge
handling is
key
Desk Set (1957): Computer engineer
Richard Sumner (Spencer Tracy)
and television network librarian
Bunny Watson (Katherine Hepburn)
and the "electronic brain" EMERAC.
Hans Peter Luhn
“A Business Intelligence System”
IBM Journal, October 1958
10. Global Analytics: Text, Speech, Sentiment, and Sense
10
Luhn’s analysis of
Messengers of the Nervous
System, a Scientific American
article
http://wordle.net,
applied to a Luhn-cited
NY Times article
“Statistical information derived from word frequency and distribution is
used by the machine to compute a relative measure of significance, first for
individual words and then for sentences. Sentences scoring highest in
significance are extracted and printed out to become the auto-abstract.”
H.P. Luhn, The Automatic Creation of Literature Abstracts, IBM Journal, 1958.
LT-Accelerate – 4 December, 2014
11. Global Analytics: Text, Speech, Sentiment, and Sense
11
LT-Accelerate – 4 December, 2014
“This rather unsophisticated argument on
‘significance’ avoids such linguistic
implications as grammar and syntax... No
attention is paid to the logical and semantic
relationships the author has established.”
-- H.P. Luhn
~ 2004-5
12.
13.
14.
15. Global Analytics: Text, Speech, Sentiment, and Sense
15
Patterns, Insights & Connections
~ 2009-
12
LT-Accelerate – 4 December, 2014
16. Global Analytics: Text, Speech, Sentiment, and Sense
16
LT-Accelerate – 4 December, 2014
… also
commonly
explored via
dashboards.
17. Global Analytics: Text, Speech, Sentiment, and Sense
17
Do you currently need (or expect to need) to extract or analyze...
Expect, 22%
Expect, 28%
Expect, 33%
Expect, 25%
Expect, 23%
Expect, 23%
Expect, 24%
LT-Accelerate – 4 December, 2014
Current, 66%
Current, 54%
Current, 47%
Current, 56%
Current, 51%
Current, 47%
Current, 34%
Current, 31%
Current, 33%
Expect, 21%
Expect, 28%
Topics and themes
Sentiment, opinions, attitudes, emotions,…
Relationships and/or facts
Named entities – people, companies, …
Concepts, that is, abstract groups of entities
Metadata such as document author,…
Other entities – phone numbers, part/product …
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Semantic annotations
Events
Text Analytics 2014
http://altaplana.com/TA2014
What information?
18. Global Analytics: Text, Speech, Sentiment, and Sense
18
Emotion and outcomes
LT-Accelerate – 4 December, 2014
19. Global Analytics: Text, Speech, Sentiment, and Sense
19
LT-Accelerate – 4 December, 2014
“The share rise in users
who selected
Arabic…coincided with
much of the civil
unrest… in Middle
Eastern countries.”
http://bits.blogs.nytimes.com/2014/03/09/the
-languages-of-twitter-users/
20. Global Analytics: Text, Speech, Sentiment, and Sense
20
24%
LT-Accelerate – 4 December, 2014
Non-English language support?
5%
2%
1%
3%
Other
Other European or Slavic/Cyrillic
Other East Asian
Other Arabic script (including Urdu,…
2%
1%
7%
10%
3%
4%
Other African
Turkish or Turkic
Spanish
Scandinavian or Baltic
Russian
Portuguese
Polish
Korean
Japanese
Italian
Hindi, Urdu, Bengali, Punjabi, or other…
2%
1%
0%
16%
9%
34%
36%
2%
18%
7%
13%
8%
38%
3%
9%
17%
3%
28%
7%
17%
2%
10%
11%
15%
8%
4%
17%
21%
3%
20%
4%
0%
2%
0% 10% 20% 30% 40% 50% 60%
Greek
German
French
Dutch
Chinese
Bahasa Indonesia or Malay
Arabic
Current
Within 2 years
Text Analytics 2014
http://altaplana.com/TA2014
21. Global Analytics: Text, Speech, Sentiment, and Sense
21
LT-Accelerate – 4 December, 2014
Audio including speech
Images
Video
IOT
http://www.geekosystem.com/
facebook-face-recognition/
http://www.sciencedirect.com/science
/article/pii/S0167639312000118
http://flylib.com/books/en/2.495.1.54/1/
Beyond text
22. Global Analytics: Text, Speech, Sentiment, and Sense
22
http://searchuserinterfaces.com/
LT-Accelerate – 4 December, 2014
“It is convenient to divide the entire
information access process into two
main components: information
retrieval through searching and
browsing, and analysis and synthesis
of results. This broader process is
often referred to in the literature as
sensemaking.
Sensemaking refers to an iterative
process of formulating a conceptual
representation from of a large
volume of information.”
– Marti Hearst, 2009
Sensemaking
23. Global Analytics: Text, Speech, Sentiment, and Sense
23
Challenges
Context
Interaction
Narrative and discourse
Correlation, integration, and synthesis
Sentiment++: Mood, opinions, emotions, intent
Question answering
Dialog, storytelling
Cross-lingual / “omni-channel” implementation
…
Prescription, autonomy
…
Singularity?
LT-Accelerate – 4 December, 2014
24. Global Analytics: Text, Speech, Sentiment, and Sense
24
Opportunity enablers
LT-Accelerate – 4 December, 2014
The API economy
I.e., on-demand, via-API Web services
Cloud deployment and service delivery
…enabling rapid deployment
Data aggregation and enrichment
Examples: Gnip, DataSift, Spinn3r, and Moreover
Growth hacking
Knowledge graphs
Machine learning
Supervised, unsupervised, active, deep
Open source
Platforms and frameworks
Examples: UIMA, GATE… Salesforce, QlikView… Python, R
25. Global Analytics: Text, Speech, Sentiment, and Sense
25
Where to?
LT-Accelerate – 4 December, 2014
26. Global Analytics: Text, Speech,
Sentiment, and Sense
Seth Grimes
Alta Plana Corporation
@sethgrimes
December 4, 2014