NDLI: Large Scale Sentiment Learning with Limited Labels

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Foreword to the Applied Data Science: Invited Talks Track at KDD-2017

Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess?

Learning Certifiably Optimal Rule Lists

Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers

Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale

A Practical Algorithm for Solving the Incoherence Problem of Topic Models In Industrial Applications

The Future of Data Integration

More than the Sum of its Parts: Building Domino Data Lab

The Future of Artificially Intelligent Assistants

Improved Degree Bounds and Full Spectrum Power Laws in Preferential Attachment Networks

Tripoles: A New Class of Relationships in Time Series Data

Luck is Hard to Beat: The Difficulty of Sports Prediction

Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity

Three Principles of Data Science: Predictability, Stability and Computability

Mining Big Data in NeuroGenetics to Understand Muscular Dystrophy

Unsupervised Network Discovery for Brain Imaging Data

Post Processing Recommender Systems for Diversity

Planning Bike Lanes based on Sharing-Bikes' Trajectories

Extremely Fast Decision Tree Mining for Evolving Data Streams

Industrial Machine Learning

Patient Subtyping via Time-Aware LSTM Networks

Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews

TFX: A TensorFlow-Based Production-Scale Machine Learning Platform

Real-Time Optimization of Web Publisher RTB Revenues

Behavior Informatics to Discover Behavior Insight for Active and Tailored Client Management

Robust Top-k Multiclass SVM for Visual Category Recognition

Bolt: Accelerated Data Mining with Fast Vector Compression

LiJAR: A System for Job Application Redistribution towards Efficient Career Marketplace

Customer Lifetime Value Prediction Using Embeddings

It Takes More than Math and Engineering to Hit the Bullseye with Data

KATE: K-Competitive Autoencoder for Text

Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings

A Data Science Approach to Understanding Residential Water Contamination in Flint

TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks

Planning and Learning under Uncertainty: Theory and Practice

A Minimal Variance Estimator for the Cardinality of Big Data Set Intersection

DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

Estimation of Recent Ancestral Origins of Individuals on a Large Scale

Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices

Big Data in Climate: Opportunities and Challenges for Machine Learning

HyperLogLog Hyperextended: Sketches for Concave Sublinear Frequency Statistics

Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization

A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments

AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments

Addressing Challenges with Big Data for Media Measurement

Fast Enumeration of Large k-Plexes

On Sampling Strategies for Neural Network-based Collaborative Filtering

A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations

Automated Categorization of Onion Sites for Analyzing the Darkweb Ecosystem

Machine Learning Software in Practice: Quo Vadis?

Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery

Unsupervised Feature Selection in Signed Social Networks

FIRST: Fast Interactive Attributed Subgraph Matching

Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster

Designing AI at Scale to Power Everyday Life

metapath2vec: Scalable Representation Learning for Heterogeneous Networks

GRAM: Graph-based Attention Model for Healthcare Representation Learning

Prognosis and Diagnosis of Parkinson's Disease Using Multi-Task Learning

An Efficient Bandit Algorithm for Realtime Multivariate Optimization

Spaceborne Data Enters the Mainstream

Ego-Splitting Framework: from Non-Overlapping to Overlapping Clusters

Algorithmic Decision Making and the Cost of Fairness

A Data Mining Framework for Valuing Large Portfolios of Variable Annuities

Large Scale Sentiment Learning with Limited Labels

Contextual Motifs: Increasing the Utility of Motifs using Contextual Data

Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks

GELL: Automatic Extraction of Epidemiological Line Lists from Open Sources

Optimization Beyond Prediction: Prescriptive Price Optimization

Unsupervised P2P Rental Recommendations via Integer Programming

Revisiting Power-law Distributions in Spectra of Real World Networks

Google Vizier: A Service for Black-Box Optimization

Finding Precursors to Anomalous Drop in Airspeed During a Flight's Takeoff

The Co-Evolution Model for Social Network Evolving and Opinion Migration

REMIX: Automated Exploration for Interactive Outlier Detection

Predicting Clinical Outcomes Across Changing Electronic Health Record Systems

Ad Serving with Multiple KPIs

Groups-Keeping Solution Path Algorithm for Sparse Regression with Automatic Feature Grouping

Anarchists, Unite: Practical Entropy Approximation for Distributed Streams

HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network

Discovering Pollution Sources and Propagation Patterns in Urban Area

Clustering Individual Transactional Data for Masses of Users

Recurrent Poisson Factorization for Temporal Recommendation

Peeking at A/B Tests: Why it matters, and what to do about it

Discovering Enterprise Concepts Using Spreadsheet Tables

Network Inference via the Time-Varying Graphical Lasso

SPOT: Sparse Optimal Transformations for High Dimensional Variable Selection and Exploratory Regression Analysis

PNP: Fast Path Ensemble Method for Movie Design

Supporting Employer Name Normalization at both Entity and Cluster Level

Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

Incremental Dual-memory LSTM in Land Cover Prediction

Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data

BDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency

Efficient Correlated Topic Modeling with Topic Embedding

MetaPAD: Meta Pattern Discovery from Massive Text Corpora

FLAP: An End-to-End Event Log Analysis Platform for System Management

Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks

Accelerating Innovation Through Analogy Mining

Federated Tensor Factorization for Computational Phenotyping

Cascade Ranking for Operational E-commerce Search

Internet Device Graphs

Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines

Statistical Emerging Pattern Mining with Multiple Testing Correction

Developing a Comprehensive Framework for Multimodal Feature Extraction

RUSH!: Targeted Time-limited Coupons via Purchase Forecasts

A Hierarchical Algorithm for Extreme Clustering

Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites

Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction

Embedding-based News Recommendation for Millions of Users

Estimating Treatment Effect in the Wild via Differentiated Confounder Balancing

Prospecting the Career Development of Talents: A Survival Analysis Perspective

Compass: Spatio Temporal Sentiment Analysis of US Election What Twitter Says!

Learning to Count Mosquitoes for the Sterile Insect Technique

The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables

A Context-aware Attention Network for Interactive Question Answering

Backpage and Bitcoin: Uncovering Human Traffickers

An Intelligent Customer Care Assistant System for Large-Scale Cellular Network Diagnosis

Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics

Distributed Multi-Task Relationship Learning

Not All Passes Are Created Equal: Objectively Measuring the Risk and Reward of Passes in Soccer from Tracking Data

Deep Design: Product Aesthetics for Heterogeneous Markets

Is the Whole Greater Than the Sum of Its Parts?

Point-of-Interest Demand Modeling with Human Mobility Patterns

MARAS: Signaling Multi-Drug Adverse Reactions

Collecting and Analyzing Millions of mHealth Data Streams

Collaborative Variational Autoencoder for Recommender Systems

Functional Zone Based Hierarchical Demand Prediction For Bike System Expansion

A Practical Exploration System for Search Advertising

Dispatch with Confidence: Integration of Machine Learning, Optimization and Simulation for Open Pit Mines

Linearized GMM Kernels and Normalized Random Fourier Features

Unsupervised Discovery of Drug Side-Effects from Heterogeneous Data Sources

MOLIERE: Automatic Biomedical Hypothesis Generation System

"The Leicester City Fairytale?": Utilizing New Soccer Analytics Tools to Compare Performance in the 15/16 & 16/17 EPL Seasons

Discrete Content-aware Matrix Factorization

Let's See Your Digits: Anomalous-State Detection using Benford's Law

Quick Access: Building a Smart Experience for Google Drive

Matching Restaurant Menus to Crowdsourced Food Data: A Scalable Machine Learning Approach

Effective and Real-time In-App Activity Analysis in Encrypted Internet Traffic Streams

Mixture Factorized Ornstein-Uhlenbeck Processes for Time-Series Forecasting

The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms

The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue

Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning

Automatic Synonym Discovery with Knowledge Bases

DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution

Automatic Application Identification from Billions of Files

Discovering Reliable Approximate Functional Dependencies

An Alternative to NCD for Large Sequences, Lempel-Ziv Jaccard Distance

No Longer Sleeping with a Bomb: A Duet System for Protecting Urban Safety from Dangerous Goods

Learning to Generate Rock Descriptions from Multivariate Well Logs with Hierarchical Attention

Towards an Optimal Subspace for K-Means

Inferring the Strength of Social Ties: A Community-Driven Approach

A Quasi-experimental Estimate of the Impact of P2P Transportation Platforms on Urban Consumer Patterns

Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes

SPARTan: Scalable PARAFAC2 for Large & Sparse Data

Detecting Network Effects: Randomizing Over Randomized Experiments

KunPeng: Parameter Server based Distributed Learning Systems and Its Applications in Alibaba and Ant Financial

Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration

struc2vec: Learning Node Representations from Structural Identity

When is a Network a Network?: Multi-Order Graphical Model Selection in Pathways and Temporal Networks

Deep Embedding Forest: Forest-based Serving with Deep Embedding Features

A Hybrid Framework for Text Modeling with Convolutional RNN

Similarity Forests

ReasoNet: Learning to Stop Reading in Machine Comprehension

Formative Essay Feedback Using Predictive Scoring Models

Online Ranking with Constraints: A Primal-Dual Algorithm and Applications to Web Traffic-Shaping

DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams

Learning Temporal State of Diabetes Patients via Combining Behavioral and Demographic Data

On Finding Socially Tenuous Groups for Online Social Networks

Anomaly Detection in Streams with Extreme Value Theory

Local Algorithm for User Action Prediction Towards Display Ads

PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks

Relay-Linking Models for Prominence and Obsolescence in Evolving Networks

Visual Search at eBay

Multi-Aspect Streaming Tensor Completion

PAMAE: Parallel k-Medoids Clustering with High Accuracy and Efficiency

A Data-driven Process Recommender Framework

Scalable and Sustainable Deep Learning via Randomized Hashing

Sparse Compositional Local Metric Learning

Predicting Optimal Facility Location without Customer Locations

AnnexML: Approximate Nearest Neighbor Search for Extreme Multi-label Classification

End-to-end Learning for Short Text Expansion

DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks

Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking

Construction of Directed 2K Graphs

Stock Price Prediction via Discovering Multi-Frequency Trading Patterns

Structural Deep Brain Network Mining

Optimized Risk Scores

A Taxi Order Dispatch Model based On Combinatorial Optimization

Randomized Feature Engineering as a Fast and Accurate Alternative to Kernel Methods

A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users

Contextual Spatial Outlier Detection with Metric Learning

Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes

Adversary Resistant Deep Neural Networks with an Application to Malware Detection

Resolving the Bias in Electronic Medical Records

FORA: Simple and Effective Approximate Single-Source Personalized PageRank

Multi-Modality Disease Modeling via Collective Deep Matrix Factorization

STAR: A System for Ticket Analysis and Resolution

Large-scale Collaborative Ranking in Near-Linear Time

Decomposed Normalized Maximum Likelihood Codelength Criterion for Selecting Hierarchical Latent Variable Models

Optimized Cost per Click in Taobao Display Advertising

HoORaYs: High-order Optimization of Rating Distance for Recommender Systems

Structural Event Detection from Log Messages

Collaboratively Improving Topic Discovery and Word Embeddings by Coordinating Global and Local Contexts

Retrospective Higher-Order Markov Processes for User Trails

PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification

Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates

Local Higher-Order Graph Clustering

Evaluating U.S. Electoral Representation with a Joint Statistical Model of Congressional Roll-Calls, Legislative Text, and Voter Registration Data

Long Short Memory Process: Modeling Growth Dynamics of Microscopic Social Connectivity

Convex Factorization Machine for Toxicogenomics Prediction

Weisfeiler-Lehman Neural Machine for Link Prediction

Distributed Local Outlier Detection in Big Data

EmbedJoin: Efficient Edit Similarity Joins via Embeddings

Scalable Top-n Local Outlier Detection

TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams

Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation

Graph Edge Partitioning via Neighborhood Heuristic

Multi-task Function-on-function Regression with Co-grouping Structured Sparsity

Randomization or Condensation?: Linear-Cost Matrix Sketching Via Cascaded Compression Sampling

Learning from Labeled and Unlabeled Vertices in Networks

Tracking the Dynamics in Crowdfunding

Small Batch or Large Batch?: Gaussian Walk with Rebound Can Teach

Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks

Learning from Multiple Teacher Networks

Coresets for Kernel Regression

A Temporally Heterogeneous Survival Framework with Application to Social Behavior Dynamics

A Local Algorithm for Structure-Preserving Graph Cut

Inductive Semi-supervised Multi-Label Learning with Co-Training

Anomaly Detection with Robust Deep Autoencoders

LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity

Visualizing Attributed Graphs via Terrain Metaphor

Achieving Non-Discrimination in Data Release

Large Scale Sentiment Learning with Limited Labels

Content Provider	ACM Digital Library
Author	Iosifidis, Vasileios Ntoutsi, Eirini
Abstract	Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated in the social media on a daily basis. Although there is a lot of work on sentiment analysis, there are no many datasets available which one can use for developing new methods and for evaluation. To the best of our knowledge, the largest dataset for sentiment analysis is TSentiment [8], a 1.6 millions machine-annotated tweets dataset covering a period of about 3 months in 2009. This dataset however is too short and therefore insufficient to study heterogeneous, fast evolving streams. Therefore, we annotated the Twitter dataset of 2015 (228 million tweets without retweets and 275 million with retweets) and we make it publicly available for research. For the annotation we leverage the power of unlabeled data, together with labeled data using semi-supervised learning and in particular, Self-Learning and Co-Training. Our main contribution is the provision of the TSentiment15 dataset together with insights from the analysis, which includes a batch and a stream-processing of the data. In the former, all labeled and unlabeled data are available to the algorithms from the beginning, whereas in the later, they are revealed gradually based on their arrival time in the stream.
Starting Page	1823
Ending Page	1832
Page Count	10
File Format	PDF
ISBN	9781450348874
DOI	10.1145/3097983.3098159
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Co-training Sentiment analysis Self-learning Semi-supervised learning
Content Type	Text
Resource Type	Article

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