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task ID: messagepolarityclassification

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[LVIC-LIMSI]: Using Syntactic Features and Multi-polarity Words for Sentiment Analysis in Twitter

Authors: nan

Publication: TIPSTER (2013)

DOI: nan

This paper presents the contribution of our team at task 2 of SemEval 2013: Sentiment Analysis in Twitter. We submitted a con- strained run for each of the two subtasks. …...

23087

AVAYA: Sentiment Analysis on Twitter with Self-Training and Polarity Lexicon Expansion

Authors: nan

Publication: TIPSTER (2013)

DOI: nan

This paper describes the systems submitted by Avaya Labs (AVAYA) to SemEval-2013 Task 2 - Sentiment Analysis in Twitter. For the constrained conditions of both the message polarity classification and …...

23100

FBK: Sentiment Analysis in Twitter with Tweetsted

Authors: nan

Publication: TIPSTER (2013)

DOI: nan

This paper presents the Tweetsted system im- plemented for the SemEval 2013 task on Sen- timent Analysis in Twitter. In particular, we participated in Task B on Message Polar- ity …...

23079

AUEB: Two Stage Sentiment Analysis of Social Network Messages

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

This paper describes the system submit- ted for the Sentiment Analysis in Twitter Task of SEMEVAL 2014 and specifically the Message Polarity Classification sub- task. We used a 2–stage pipeline …...

22881

BUAP: Polarity Classification of Short Texts

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

We report the results we obtained at the sub- task B (Message Polarity Classification) of Se- mEval 2014 Task 9. The features used for representing the messages were basically tri- …...

22871

CISUC-KIS: Tackling Message Polarity Classification with a Large and Diverse Set of Features

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

This paper presents the approach of the CISUC-KIS team to the SemEval 2014 task on Sentiment Analysis in Twitter, more precisely subtask B - Message Polar- ity Classification. We followed …...

22877

IITPatna: Supervised Approach for Sentiment Analysis in Twitter

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

In this paper we report our works for SemEval-2014 Sentiment Analysis Twitter evaluation challenge. This is thein first time we attempt for this task, and our submissions are based on …...

22838

Indian Institute of Technology-Patna: Sentiment Analysis in Twitter

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Publication: TIPSTER (2014)

DOI: nan

This paper is an overview of the system submitted to the SemEval-2014 shared task on sentiment analysis in twitter. For the very first time we participated in both the tasks, …...

22835

Think Positive: Towards Twitter Sentiment Analysis from Scratch

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Publication: TIPSTER (2014)

DOI: nan

In this paper we describe a Deep Convo- lutional Neural Network (DNN) approach to perform two sentiment detection tasks: message polarity classification and con- textual polarity disambiguation. We apply the …...

22718

TUGAS: Exploiting unlabelled data for Twitter sentiment analysis

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

This paper describes our participation in the message polarity classification task of SemEval 2014. We focused on exploiting unlabeled data to improve accuracy, com- bining features leveraging word represen- tations …...

22711

CIS-positive: A Combination of Convolutional Neural Networks and Support Vector Machines for Sentiment Analysis in Twitter

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This paper describes our automatic sentiment analysis system – CIS-positive – for SemEval 2015 Task 10 “Sentiment Analysis in Twit- ter”, subtask B “Message Polarity Classifica- tion”. In this system, …...

22678

ECNU: Multi-level Sentiment Analysis on Twitter Using Traditional Linguistic Features and Word Embedding Features

Authors: nan

Publication: TIPSTER (2015)

DOI: nan

This paper reports our submission to task 10 (Sentiment Analysis on Tweet, SAT) (Rosen- thal et al., 2015) in SemEval 2015 , which contains five subtasks, i.e., contextual polar- ity …...

22672

ELiRF: A SVM Approach for SA tasks in Twitter at SemEval-2015

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Publication: TIPSTER (2015)

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This paper describes our participation at tasks 10 (sub-task B, Message Polarity Classifica- tion) and 11 task (Sentiment Analysis of Fig- urative Language in Twitter) of Semeval2015. We describe the …...

22652

IIIT-H at SemEval 2015: Twitter Sentiment Analysis – The Good, the Bad and the Neutral!

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Publication: TIPSTER (2015)

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This paper describes the system that was sub- mitted to SemEval2015 Task 10: Sentiment Analysis in Twitter. We participated in Sub- task B: Message Polarity Classification. The task is a …...

22679

INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces

Authors: nan

Publication: TIPSTER (2015)

DOI: nan

We present the INESC-ID system for the mes- sage polarity classification task of SemEval 2015. The proposed system does not make use of any hand-coded features or linguistic resources. It …...

22638

aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis

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This paper describes the system with which we participated in SemEval-2016 Task 4 (Sen- timent Analysis in Twitter) and specifically the Message Polarity Classification subtask. Our system is a weighted …...

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CICBUAPnlp at SemEval-2016 Task 4-A: Discovering Twitter Polarity using Enhanced Embeddings

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Publication: TIPSTER (2016)

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This paper presents our approach for SemEval 2016 task 4: Sentiment Analysis in Twitter. We participated in Subtask A: Message Polar- ity Classification. The aim is to classify Twit- ter …...

22574

DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach

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Publication: TIPSTER (2016)

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This paper contains the description of our par- ticipation at task 4 (sub-task A, Message Po- larity Classification) of SemEval-2016. Our proposed system consists mainly of three steps. Firstly, the …...

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MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification

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Publication: TIPSTER (2016)

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This paper describes our system submitted for the Sentiment Analysis in Twitter task of SemEval-2016, and specifically for the Mes- sage Polarity Classification subtask. We used a system that combines …...

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mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter

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Publication: TIPSTER (2016)

DOI: nan

This work presents our team solution for task 4a (Message Polarity Classification) at the Se- mEval 2016 challenge. Our experiments have been carried out over the Twitter dataset pro- vided …...

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SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision

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Publication: TIPSTER (2016)

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In this paper, we propose a classifier for predicting message-level sentiments of En- glish micro-blog messages from Twitter. Our method builds upon the convolutional sen- tence embedding approach proposed by …...

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BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches

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Publication: TIPSTER (2017)

DOI: nan

AbstractThis paper describes our approach for SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have participated in Subtask A: Message Polarity Classification subtask and developed two systems. The first system …...

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ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThis paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic-Based Message Polarity Classification according to a two-point scale) of SemEval-2017 Task …...

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Sentiment Analysis on Social Network: Using Emoticon Characteristics for Twitter Polarity Classification

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

In this paper, we describe a sentiment analysis system implemented for the semantic-evaluation task of message polarity classification for English on Twitter. Our system contains modules of data pre-processing, word …...

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