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Related Papers
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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. …...
KLUE: Simple and robust methods for polarity classification
This paper describes our approach to the SemEval-2013 task on “Sentiment Analysis in Twitter”. We use simple bag-of-words mod- els, a freely available sentiment dictionary auto- matically extended with distributionally …...
NILC_USP: A Hybrid System for Sentiment Analysis in Twitter Messages
This paper describes the NILC USP system that participated in SemEval-2013 Task 2: Sentiment Analysis in Twitter. Our system adopts a hybrid classification process that uses three classification approaches: rule- …...
OPTWIMA: Comparing Knowledge-rich and Knowledge-poor Approaches for Sentiment Analysis in Short Informal Texts
The fast development of Social Media made it possible for people to no loger remain mere spectators to the events that happen in the world, but become part of them, …...
SSA-UO: Unsupervised Sentiment Analysis in Twitter
This paper describes the specifications and re- sults of SSA-UO, unsupervised system, pre- sented in SemEval 2013 for Sentiment Analy- sis in Twitter (Task 2) (Wilson et al., 2013). The …...
SZTE-NLP: Sentiment Detection on Twitter Messages
In this paper we introduce our contribution to the SemEval-2013 Task 2 on “Sentiment Analysis in Twitter”. We participated in “task B”, where the objective was to build mod- els …...
UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment Analysis
In this paper, the UNITOR system participat- ing in the SemEval-2013 Sentiment Analysis in Twitter task is presented. The polarity de- tection of a tweet is modeled as a classifica- …...
UT-DB: An Experimental Study on Sentiment Analysis in Twitter
This paper describes our system for participat- ing SemEval2013 Task2-B (Kozareva et al., 2013): Sentiment Analysis in Twitter. Given a message, our system classifies whether the message is positive, negative …...
Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble
We describe our approach for the SemEval-2014 task 9: Sentiment Analy- sis in Twitter. We make use of an en- semble learning method for sentiment classification of tweets that relies …...
Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble
We describe our approach for the SemEval-2014 task 9: Sentiment Analy- sis in Twitter. We make use of an en- semble learning method for sentiment classification of tweets that relies …...
Columbia NLP: Sentiment Detection of Sentences and Subjective Phrases in Social Media
We present two supervised sentiment de- tection systems which were used to com- pete in SemEval-2014 Task 9: Senti- ment Analysis in Twitter. The first sys- tem (Rosenthal and McKeown, …...
SemEval-2014 Task 9: Sentiment Analysis in Twitter
We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year’s task that ran successfully as part of SemEval- 2013. …...
Think Positive: Towards Twitter Sentiment Analysis from Scratch
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 …...
UMCC_DLSI: Sentiment Analysis in Twitter using Polirity Lexicons and Tweet Similarity
This paper describes a system sub- mitted to SemEval-2014 Task 4B: Sentiment Analysis in Twitter, by the team UMCC DLSI Sem integrated by researchers of the University of Matanzas, Cuba …...
GTI: An Unsupervised Approach for Sentiment Analysis in Twitter
This paper presents the approach of the GTI Research Group to SemEval-2015 task 10 on Sentiment Analysis in Twitter, or more specif- ically, subtasks A (Contextual Polarity Disam- biguation) and …...
UNIBA: Sentiment Analysis of English Tweets Combining Micro-blogging, Lexicon and Semantic Features
This paper describes the UNIBA team partic- ipation in the Sentiment Analysis in Twitter task (Task 10) at SemEval-2015. We propose a supervised approach relying on keyword, lex- icon and …...
PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis
This paper describes a classification system that participated in SemEval-2016 Task 4: Sentiment Analysis in Twitter. The proposed approach competed in subtasks A, B, and C, which involved tweet polarity …...
Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation
We describe our submission to SemEval2016 Task 4: Sentiment Analysis in Twitter. The proposed system ranked first for the sub- task B. Our system comprises of multiple in- dependent models …...
TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
This paper describes the participation of the team “TwiSE” in the SemEval 2016 challenge. Specifically, we participated in Task 4, namely “Sentiment Analysis in Twitter” for which we implemented sentiment …...
BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches
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 …...
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis
AbstractIn this paper we present two deep-learning systems that competed at SemEval-2017 Task 4 “Sentiment Analysis in Twitter”. We participated in all subtasks for English tweets, involving message-level and topic-based …...
EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification
AbstractThis paper describes our approach for SemEval-2017 Task 4 - Sentiment Analysis in Twitter (SAT). Its five subtasks are divided into two categories: (1) sentiment classification, i.e., predicting topic-based tweet …...
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts
AbstractThis paper describes the approach we used for SemEval-2017 Task 4: Sentiment Analysis in Twitter. Topic-based (target-dependent) sentiment analysis has become attractive and been used in some applications recently, but …...
SemEval-2017 Task 4: Sentiment Analysis in Twitter
AbstractThis paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying …...
TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification
AbstractThe paper describes the participation of the team “TwiSE” in the SemEval-2017 challenge. Specifically, I participated at Task 4 entitled “Sentiment Analysis in Twitter” for which I implemented systems for …...
UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter
AbstractThis paper describes the system submitted to SemEval-2017 Task 4-A Sentiment Analysis in Twitter developed by the UCSC-NLP team. We studied how relationships between sense n-grams and sentiment polarities can …...