SemEval-2014 Task 9: Sentiment Analysis in Twitter

2019/12/02 by Sara Rosenthal, Saif M Mohammad, Rosenthal, Sara +6 · 11 citations
Computer Science · #68T50 #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2.7 #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Sentiment Analysis and Opinion Mining #Spam and Phishing Detection

paper · doi:10.48550/arxiv.1912.00741

openalex publication_date 2019/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. This year's shared task competition consisted of five sentiment prediction subtasks. Two were reruns from previous years: (A) sentiment expressed by a phrase in the context of a tweet, and (B) overall sentiment of a tweet. We further included three new subtasks asking to predict (C) the sentiment towards a topic in a single tweet, (D) the overall sentiment towards a topic in a set of tweets, and (E) the degree of prior polarity of a phrase.

Citations

• The Earth Mover's Distance as a Metric for Image Retrieval
• Understanding the Demographics of Twitter Users

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Related

• TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
• SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets
• NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis
• ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset
• SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets