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NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets
In this paper, we describe how we created two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and one to detect the sentiment of
SemEval-2016 Task 6: Detecting Stance in Tweets
A shared task on detecting stance from tweets: given a tweet and a target entity (person, organization, etc.), automatic natural language systems must determine whether the tweeter is in favor of the given target, against thegiven target, or whether neither inference is likely.
It is shown how the combined strength and wisdom of the crowds can be used to generate a large, high‐quality, word–emotion and word–polarity association lexicon quickly and inexpensively.
SemEval-2018 Task 1: Affect in Tweets
This work presents the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet, with a focus on the techniques and resources that are particularly useful.
Sentiment Analysis of Short Informal Texts
We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word
NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews
Submissions to SemEval-2014 stood first in detecting aspect categories, first in detects sentiment towards aspects categories, third in detecting aspects terms, and first and second in detecting sentiment towards aspect terms in the laptop and restaurant domains, respectively.
Stance and Sentiment in Tweets
It is shown that although knowing the sentiment expressed by a tweet is beneficial for stance classification, it alone is not sufficient and additional unlabeled data is used through distant supervision techniques and word embeddings to further improve stance classification.
Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon
This paper shows how to create a high-quality, moderate-sized emotion lexicon using Mechanical Turk, and identifies which emotions tend to be evoked simultaneously by the same term and shows that certain emotions indeed go hand in hand.
SemEval-2015 Task 10: Sentiment Analysis in Twitter
The 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years.
#Emotional Tweets
This paper describes how a Twitter emotion corpus is created from Twitter posts using emotion-word hashtags, and extracts a word-emotion association lexicon that leads to significantly better results than the manually crafted WordNet Affect lexicon in an emotion classification task.