NLPExplorer

SemEval-2016 Task 4: Sentiment Analysis in Twitter

Paper Details:

Month: June
Year: 2016
Location: San Diego, California
Venue: *SEMEVAL |

Citations

URL

A Hybrid Deep Learning Architecture for Sentiment Analysis
Md Shad Akhtar | Ayush Kumar | Asif Ekbal | Pushpak Bhattacharyya |

On the Impact of Seed Words on Sentiment Polarity Lexicon Induction
Dame Jovanoski | Veno Pachovski | Preslav Nakov |

Encoding Sentiment Information into Word Vectors for Sentiment Analysis
Zhe Ye | Fang Li | Timothy Baldwin |

Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level
Sven Buechel | Udo Hahn |

Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification
Yi Tay | Anh Tuan Luu | Siu Cheung Hui | Jian Su |

Uncertainty-aware generative models for inferring document class prevalence
Katherine Keith | Brendan O’Connor |

A Multi-View Sentiment Corpus
Debora Nozza | Elisabetta Fersini | Enza Messina |

TDParse: Multi-target-specific sentiment recognition on Twitter
Bo Wang | Maria Liakata | Arkaitz Zubiaga | Rob Procter |

SMARTies: Sentiment Models for Arabic Target entities
Noura Farra | Kathy McKeown |

Exploring Convolutional Neural Networks for Sentiment Analysis of Spanish tweets
Isabel Segura-Bedmar | Antonio Quirós | Paloma Martínez |

Structural Attention Neural Networks for improved sentiment analysis
Filippos Kokkinos | Alexandros Potamianos |

NCYU at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases using Vector Representations
Jui-Feng Yeh | Jian-Cheng Tsai | Bo-Wei Wu | Tai-You Kuang |

Sentiment Lexicons for Arabic Social Media
Saif Mohammad | Mohammad Salameh | Svetlana Kiritchenko |

EN-ES-CS: An English-Spanish Code-Switching Twitter Corpus for Multilingual Sentiment Analysis
David Vilares | Miguel A. Alonso | Carlos Gómez-Rodríguez |

Sharing Copies of Synthetic Clinical Corpora without Physical Distribution — A Case Study to Get Around IPRs and Privacy Constraints Featuring the German JSYNCC Corpus
Christina Lohr | Sven Buechel | Udo Hahn |

An Integrated Representation of Linguistic and Social Functions of Code-Switching
Silvana Hartmann | Monojit Choudhury | Kalika Bali |

Sentiment after Translation: A Case-Study on Arabic Social Media Posts
Mohammad Salameh | Saif Mohammad | Svetlana Kiritchenko |

Ultradense Word Embeddings by Orthogonal Transformation
Sascha Rothe | Sebastian Ebert | Hinrich Schütze |

Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst Scaling
Svetlana Kiritchenko | Saif M. Mohammad |

DAG-Structured Long Short-Term Memory for Semantic Compositionality
Xiaodan Zhu | Parinaz Sobhani | Hongyu Guo |

Zero-Shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens
Marek Rei | Anders Søgaard |

Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces
Isabelle Augenstein | Sebastian Ruder | Anders Søgaard |

Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem
Sven Buechel | Udo Hahn |

Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable
Viktor Hangya | Fabienne Braune | Alexander Fraser | Hinrich Schütze |

Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora
Andrius Mudinas | Dell Zhang | Mark Levene |

ECNU: Multi-level Sentiment Analysis on Twitter Using Traditional Linguistic Features and Word Embedding Features
Zhihua Zhang | Guoshun Wu | Man Lan |

SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases
Svetlana Kiritchenko | Saif Mohammad | Mohammad Salameh |

CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification
Mahmoud Nabil | Amir Atyia | Mohamed Aly |

QCRI at SemEval-2016 Task 4: Probabilistic Methods for Binary and Ordinal Quantification
Giovanni Da San Martino | Wei Gao | Fabrizio Sebastiani |

LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification
David Vilares | Yerai Doval | Miguel A. Alonso | Carlos Gómez-Rodríguez |

ISTI-CNR at SemEval-2016 Task 4: Quantification on an Ordinal Scale
Andrea Esuli |

thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale
Vikrant Yadav |

NTNUSentEval at SemEval-2016 Task 4: Combining General Classifiers for Fast Twitter Sentiment Analysis
Brage Ekroll Jahren | Valerij Fredriksen | Björn Gambäck | Lars Bungum |

UDLAP at SemEval-2016 Task 4: Sentiment Quantification Using a Graph Based Representation
Esteban Castillo | Ofelia Cervantes | Darnes Vilariño | David Báez |

Aicyber at SemEval-2016 Task 4: i-vector based sentence representation
Steven Du | Xi Zhang |

PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis
Mateusz Lango | Dariusz Brzezinski | Jerzy Stefanowski |

mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter
Vittoria Cozza | Marinella Petrocchi |

Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis
Dario Stojanovski | Gjorgji Strezoski | Gjorgji Madjarov | Ivica Dimitrovski |

UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification
Omar Abdelwahab | Adel Elmaghraby |

INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification
Sebastian Ruder | Parsa Ghaffari | John G. Breslin |

UNIMELB at SemEval-2016 Tasks 4A and 4B: An Ensemble of Neural Networks and a Word2Vec Based Model for Sentiment Classification
Steven Xu | HuiZhi Liang | Timothy Baldwin |

SentiSys at SemEval-2016 Task 4: Feature-Based System for Sentiment Analysis in Twitter
Hussam Hamdan |

DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach
Víctor Martinez Morant | LLuís-F. Hurtado | Ferran Pla |

SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis
Mickael Rouvier | Benoit Favre |

UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification
Giuseppe Attardi | Daniele Sartiano |

IIP at SemEval-2016 Task 4: Prioritizing Classes in Ensemble Classification for Sentiment Analysis of Tweets
Jasper Friedrichs |

PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks.
Uladzimir Sidarenka |

ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter
Yunxiao Zhou | Zhihua Zhang | Man Lan |

NileTMRG at SemEval-2016 Task 7: Deriving Prior Polarities for Arabic Sentiment Terms
Samhaa R. El-Beltagy |

Detecting Stance in Tweets And Analyzing its Interaction with Sentiment
Parinaz Sobhani | Saif Mohammad | Svetlana Kiritchenko |

BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
Mathieu Cliche |

NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings
Yichun Yin | Yangqiu Song | Ming Zhang |

SINAI at SemEval-2017 Task 4: User based classification
Salud María Jiménez-Zafra | Arturo Montejo-Ráez | Maite Martin | L. Alfonso Ureña-López |

SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification
Raphaël Troncy | Enrico Palumbo | Efstratios Sygkounas | Giuseppe Rizzo |

TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier
Naveen Kumar Laskari | Suresh Kumar Sanampudi |

OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields
Chukwuyem Onyibe | Nizar Habash |

Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification
Hussam Hamdan |

YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter
Ming Wang | Biao Chu | Qingxun Liu | Xiaobing Zhou |

funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts
Quanzhi Li | Armineh Nourbakhsh | Xiaomo Liu | Rui Fang | Sameena Shah |

DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis
Christos Baziotis | Nikos Pelekis | Christos Doulkeridis |

INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis
Sabino Miranda-Jiménez | Mario Graff | Eric Sadit Tellez | Daniela Moctezuma |

YNU-HPCC at SemEval 2017 Task 4: Using A Multi-Channel CNN-LSTM Model for Sentiment Classification
Haowei Zhang | Jin Wang | Jixian Zhang | Xuejie Zhang |

TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis
Amit Ajit Deshmane | Jasper Friedrichs |

TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news
Leon Rotim | Martin Tutek | Jan Šnajder |

SemEval-2018 Task 3: Irony Detection in English Tweets
Cynthia Van Hee | Els Lefever | Véronique Hoste |

AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets
Mostafa Abdou | Artur Kulmizev | Joan Ginés i Ametllé |

RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning
Venkatesh Elango | Karan Uppal |

ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets
El Moatez Billah Nagoudi |

EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption
Liyuan Zhou | Qiongkai Xu | Hanna Suominen | Tom Gedeon |

WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony
Omid Rohanian | Shiva Taslimipoor | Richard Evans | Ruslan Mitkov |

Sentiment Analysis in Twitter: A SemEval Perspective
Preslav Nakov |

A Practical Guide to Sentiment Annotation: Challenges and Solutions
Saif Mohammad |

Feelings from the Past—Adapting Affective Lexicons for Historical Emotion Analysis
Sven Buechel | Johannes Hellrich | Udo Hahn |

Generating Sentiment Lexicons for German Twitter
Uladzimir Sidarenka | Manfred Stede |

Microblog Emotion Classification by Computing Similarity in Text, Time, and Space
Anja Summa | Bernd Resch | Michael Strube |

Towards Building a SentiWordNet for Tamil
Abishek Kannan | Gaurav Mohanty | Radhika Mamidi |

Potential and Limitations of Cross-Domain Sentiment Classification
Jan Milan Deriu | Martin Weilenmann | Dirk Von Gruenigen | Mark Cieliebak |

A Twitter Corpus and Benchmark Resources for German Sentiment Analysis
Mark Cieliebak | Jan Milan Deriu | Dominic Egger | Fatih Uzdilli |

Not All Segments are Created Equal: Syntactically Motivated Sentiment Analysis in Lexical Space
Muhammad Abdul-Mageed |

Comparison of Short-Text Sentiment Analysis Methods for Croatian
Leon Rotim | Jan Šnajder |

Using Convolutional Neural Networks to Classify Hate-Speech
Björn Gambäck | Utpal Kumar Sikdar |

Explaining Recurrent Neural Network Predictions in Sentiment Analysis
Leila Arras | Grégoire Montavon | Klaus-Robert Müller | Wojciech Samek |

NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets
Sreekanth Madisetty | Maunendra Sankar Desarkar |

Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings
Mats Byrkjeland | Frederik Gørvell de Lichtenberg | Björn Gambäck |

Field Of Study

Linguistic Trends

Embeddings

Task

Named Entity Recognition Sentiment Analysis Social Science

Approach

Deep Learning Structured Prediction

Language

English

Dataset

Twitter

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