SemEval-2015 Task 10: Sentiment Analysis in Twitter
نویسندگان
چکیده
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.
منابع مشابه
SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter
This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analysis of figurative language on Twitter (Task 11). This is the first sentiment analysis task wholly dedicated to analyzing figurative language on Twitter. Specifically, three broad classes of figurative language are considered: irony, sarcasm and metaphor. Gold standard sets of 8000 training tweets...
متن کاملUPF-taln: SemEval 2015 Tasks 10 and 11. Sentiment Analysis of Literal and Figurative Language in Twitter
In this paper, we describe the approach used by the UPF-taln team for tasks 10 and 11 of SemEval 2015 that respectively focused on “Sentiment Analysis in Twitter” and “Sentiment Analysis of Figurative Language in Twitter”. Our approach achieved satisfactory results in the figurative language analysis task, obtaining the second best result. In task 10, our approach obtained acceptable performanc...
متن کاملSentiSys at SemEval-2016 Task 4: Feature-Based System for Sentiment Analysis in Twitter
This paper describes our sentiment analysis system which has been built for Sentiment Analysis in Twitter Task of SemEval-2016. We have used a Logistic Regression classifier with different groups of features. This system is an improvement to our previous system Lsislif in Semeval-2015 after removing some features and adding new features extracted from a new automatic constructed sentiment lexicon.
متن کاملSentibase: Sentiment Analysis in Twitter on a Budget
Like SemEval 2013 and 2014, the task Sentiment Analysis in Twitter found a place in this year’s SemEval too and attracted an unprecedented number of participations. This task comprises of four sub-tasks. We participated in subtask 2 — Message polarity classification. Although we lie a few notches down from the top system, we present a very simple yet effective approach to handle this problem th...
متن کاملUIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015
Microblogs are considered as We-Media information with many real-time opinions. This paper presents a Twitter-OpinMiner system for Twitter sentiment analysis evaluation at SemEval 2015. Our approach stems from two different angles: topic detection for discovering the sentiment distribution on different topics and sentiment analysis based on a variety of features. Moreover, we also implemented i...
متن کامل