data
play

Data Public opinion polls for the UK csv format (>400 data - PowerPoint PPT Presentation

Sep 20, 2023 •149 likes •186 views

Data Public opinion polls for the UK csv format (>400 data points) Time interval: 04/09/2010 -1/week-> 12/04/2011 -5/week-> now voting intention, government approval (all polls) parties & sensitive issues

  1. Data • Public opinion polls for the UK • csv format (>400 data points) • Time interval: 04/09/2010 -1/week-> 12/04/2011 -5/week-> now • voting intention, government approval (all polls) • parties & sensitive issues (e.g. Taxation, Education, Immigration) • politician traits (e.g. Charismatic, Strong, Decisive) (forthnightly) • current issues (e.g. EU membership, Scottish independence, Lybia) • Everything split by: Voting intention, VI relative to last election vote, Sex (2), Age group (4), Social Grade (2), Region (5) www.trendminer-project.eu

  2. Results • Forecasting polls using text regression from tweets • Novel model of sparse bilinear regression • Selects sparse set of both users and words Ground truth A User-centric Model of Voting Intention from Social Media Bill Lampos, Daniel Preotiuc-Pietro, Trevor Cohn ACL 2013 http://www.preotiuc.ro BGL

  3. Future plans • poll prediction: regional and demographic • prediction and features for the other time series (events, traits, issues) • identifying events that affect variation in time series e.g. increase in 'Sticks to what he believes in' for PM David Cameron after E.U. veto (2011)

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Data Preparation Data cleaning Data integration and transformation (Data

Data Preparation Data cleaning Data integration and transformation (Data

Data Preprocessing Why preprocess the data? Data Preparation Data cleaning Data integration and transformation (Data preprocessing) Data reduction, Feature selection Discretization and concept hierarchy generation 2

891 views • 21 slides
DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data

DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data

DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data mining is the use of efficient techniques for the analysis of very large collections of data and the extraction of useful and possibly unexpected

2.4k views • 94 slides
Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 68 / 91 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4 2

579 views • 8 slides
Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 28 / 89 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4

455 views • 23 slides
Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 51 / 89 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4

344 views • 17 slides
Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 68 / 91 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4 2

729 views • 24 slides
Data Preparation Data cleaning Discretization (Data preprocessing) Data

Data Preparation Data cleaning Discretization (Data preprocessing) Data

Data Preprocessing Why preprocess the data? Data Preparation Data cleaning Discretization (Data preprocessing) Data integration and transformation Data reduction, Feature selection 2 Why Prepare Data? Why Prepare

462 views • 15 slides
DATA QUALITY AND DATA DATA QUALITY AND DATA PROGRAMMING PROGRAMMING "Data cleaning and

DATA QUALITY AND DATA DATA QUALITY AND DATA PROGRAMMING PROGRAMMING "Data cleaning and

DATA QUALITY AND DATA DATA QUALITY AND DATA PROGRAMMING PROGRAMMING "Data cleaning and repairing account for about 60% of the work of data scientists." Christian Kaestner Required reading: Schelter, S., Lange, D., Schmidt, P.,

1.1k views • 86 slides
Data stories with The Pudding So what is data storytelling? Data academia & data science

Data stories with The Pudding So what is data storytelling? Data academia & data science

Data stories with The Pudding So what is data storytelling? Data academia & data science dense complex static/non-interactive often not accompanied by an easy-to-follow narrative Data art beautiful abstract

647 views • 5 slides
Data Acquisition Chapter 2 Data Acquisition 1 st step: get data Usually data gathered by

Data Acquisition Chapter 2 Data Acquisition 1 st step: get data Usually data gathered by

Data Acquisition Chapter 2 Data Acquisition 1 st step: get data Usually data gathered by some geophysical device Most surveys are comprised of linear traverses or transects Typically constant data spacing Perpendicular to

432 views • 12 slides
Data Collection and Aggregation Data Collection and Aggregation 1 Challenges: data Challenges:

Data Collection and Aggregation Data Collection and Aggregation 1 Challenges: data Challenges:

Data Collection and Aggregation Data Collection and Aggregation 1 Challenges: data Challenges: data Data type: numerical sensor readings. Rich and massive data, spatially distributed and correlated. Data dynamics: data

713 views • 48 slides
Business Statistics CONTENTS The role of data The data matrix Data types Aspects of data

Business Statistics CONTENTS The role of data The data matrix Data types Aspects of data

DATA Business Statistics CONTENTS The role of data The data matrix Data types Aspects of data Obtaining data Further study THE ROLE OF DATA Data refers to observed facts there are 82 persons in this train the weight of

683 views • 22 slides
DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type system sets the stage for the capabilities of the language Understanding data types empowers you as a data scientist DataCamp Data Types for Data

526 views • 23 slides
q.Datum Data Exchange The marketplace for big data Data is the new oil of the Internet and the

q.Datum Data Exchange The marketplace for big data Data is the new oil of the Internet and the

q.Datum Data Exchange The marketplace for big data Data is the new oil of the Internet and the new currency of the digital world . World Economic Forum 1 Problem: access to big data Data accessed & analyzed Data in organizations

543 views • 30 slides
Concatenating data Cleaning Data in Python Combining data Data may not always come in 1

Concatenating data Cleaning Data in Python Combining data Data may not always come in 1

CLEANING DATA IN PYTHON Concatenating data Cleaning Data in Python Combining data Data may not always come in 1 huge file 5 million row dataset may be broken into 5 separate datasets Easier to store and share May have

425 views • 26 slides
Prioritizing Data and Purpose of Data Points What data do I have? What data do I trust? What

Prioritizing Data and Purpose of Data Points What data do I have? What data do I trust? What

Prioritizing Data and Purpose of Data Points What data do I have? What data do I trust? What data do I use most? Session Target: After todays session, you will be able to: 1. Insert/include data opportunities into PL models. 2.

710 views • 12 slides
AnalysIs of Social Networks Among PhysICIans Employed at a MedIcal School MIE 2014 Istanbul,

AnalysIs of Social Networks Among PhysICIans Employed at a MedIcal School MIE 2014 Istanbul,

AnalysIs of Social Networks Among PhysICIans Employed at a MedIcal School MIE 2014 Istanbul, Turkey YK Yce, N Zayim, B Oguz, S Bozkurt, F Isleyen, KH Gulkesen Tue, 2nd Sep 2014 Outline Introduction n Problem: Technology

459 views • 33 slides
CSS 2013 CE&O Study Group Marge Bardeen, Fermilab Dan Cronin-Hennessy, University of

CSS 2013 CE&O Study Group Marge Bardeen, Fermilab Dan Cronin-Hennessy, University of

CSS 2013 CE&O Study Group Marge Bardeen, Fermilab Dan Cronin-Hennessy, University of Minnesota CSS 2013, Minneapolis, August 2013 Friday, July 26, 13 CEO Study Group Charge How to build support for & develop understanding of

967 views • 17 slides
About Patron Insight, Inc. Based in the Kansas City area Worked with more than 135 school

About Patron Insight, Inc. Based in the Kansas City area Worked with more than 135 school

4/25/17 Presentation to the Park Ridge-Niles School District 64 Board of Education About Patron Insight, Inc. Based in the Kansas City area Worked with more than 135 school districts in 13 states since 1992 Focused on communication,

396 views • 8 slides
Accountability, Responsibility and Robustness in Agent Organizations CILC 2019, Trieste Matteo

Accountability, Responsibility and Robustness in Agent Organizations CILC 2019, Trieste Matteo

Accountability, Responsibility and Robustness in Agent Organizations CILC 2019, Trieste Matteo Baldoni 1 , Cristina Baroglio 1 , Roberto Micalizio 1 1 Universit` a degli Studi di Torino - Dipartimento di Informatica, Torino, Italy Multiagent

663 views • 44 slides
Moonshot Initiative and Cancer Research National public opinion survey commissioned

Moonshot Initiative and Cancer Research National public opinion survey commissioned

Americans Views on Moonshot Initiative and Cancer Research National public opinion survey commissioned January 2016 by Research!America National Survey Methodology This nationwide online survey was conducted by Zogby Analytics on

511 views • 12 slides
Americans Views on Candidates and Medical Progress National public opinion survey commissioned

Americans Views on Candidates and Medical Progress National public opinion survey commissioned

Americans Views on Candidates and Medical Progress National public opinion survey commissioned by July 2016 Research!America National Survey Methodology This nationwide online survey was conducted by Zogby Analytics on behalf of

606 views • 32 slides
Why Did Clinton Loose? CTC2 V1A 7 Dec, 2016 1A 1A 2016 Schield CTC2 Trump 1 2016 Schield

Why Did Clinton Loose? CTC2 V1A 7 Dec, 2016 1A 1A 2016 Schield CTC2 Trump 1 2016 Schield

Why Did Clinton Loose? CTC2 V1A 7 Dec, 2016 1A 1A 2016 Schield CTC2 Trump 1 2016 Schield CTC2 Trump 2 We Want Explanations: Why Did Clinton Lose? Why Did Trump Win? Wrong Color Pantsuits? . Milo Schield Augsburg College Editor:

590 views • 38 slides
Voter Response to Iterated Poll Information Ulle Endriss Institute for Logic, Language and

Voter Response to Iterated Poll Information Ulle Endriss Institute for Logic, Language and

Voter Response to Iterated Poll Information Iterated Voting Workshop 2014 Voter Response to Iterated Poll Information Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam joint work with Annemieke

228 views • 18 slides

More recommend