SemEval-2013 Task 2: Sentiment Analysis in Twitter

by   Preslav Nakov, et al.
USC Information Sciences Institute
Johns Hopkins University
Qatar Foundation
Columbia University
University of Washington

In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hindered by the lack of suitable datasets, complicating the comparison between approaches. To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a message-level subtask. We used crowdsourcing on Amazon Mechanical Turk to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks. All datasets used in the evaluation are released to the research community. The task attracted significant interest and a total of 149 submissions from 44 teams. The best-performing team achieved an F1 of 88.9 69


page 1

page 2

page 3

page 4


SemEval-2014 Task 9: Sentiment Analysis in Twitter

We describe the Sentiment Analysis in Twitter task, ran as part of SemEv...

Monitoring stance towards vaccination in Twitter messages

We developed a system to automatically classify stance towards vaccinati...

SemEval-2016 Task 4: Sentiment Analysis in Twitter

This paper discusses the fourth year of the “Sentiment Analysis in Twitt...

TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification

This paper describes the participation of the team "TwiSE" in the SemEva...

Twitter Sentiment Analysis using Deep Learning

In this Paper, addresses the problem of sentiment classification on twit...

Real Time Sentiment Change Detection of Twitter Data Streams

In the past few years, there has been a huge growth in Twitter sentiment...

A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets

Climate change has become one of the biggest challenges of our time. Soc...

Code Repositories


Targeted sentiment analysis using neural networks based on package

view repo


Mood_India involves mining the rich Twitter Data to capture national mood patterns and visualise the geographical and temporal features.

view repo


Sentiment Analysis with Incremental Learning WITH COMMAND LINE OPTIONS FOR AUTOMATION

view repo

Please sign up or login with your details

Forgot password? Click here to reset