355 research outputs found
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
NLP tasks are often limited by scarcity of manually annotated data. In social
media sentiment analysis and related tasks, researchers have therefore used
binarized emoticons and specific hashtags as forms of distant supervision. Our
paper shows that by extending the distant supervision to a more diverse set of
noisy labels, the models can learn richer representations. Through emoji
prediction on a dataset of 1246 million tweets containing one of 64 common
emojis we obtain state-of-the-art performance on 8 benchmark datasets within
sentiment, emotion and sarcasm detection using a single pretrained model. Our
analyses confirm that the diversity of our emotional labels yield a performance
improvement over previous distant supervision approaches.Comment: Accepted at EMNLP 2017. Please include EMNLP in any citations. Minor
changes from the EMNLP camera-ready version. 9 pages + references and
supplementary materia
A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
Quantum theory, originally proposed as a physical theory to describe the
motions of microscopic particles, has been applied to various non-physics
domains involving human cognition and decision-making that are inherently
uncertain and exhibit certain non-classical, quantum-like characteristics.
Sentiment analysis is a typical example of such domains. In the last few years,
by leveraging the modeling power of quantum probability (a non-classical
probability stemming from quantum mechanics methodology) and deep neural
networks, a range of novel quantum-cognitively inspired models for sentiment
analysis have emerged and performed well. This survey presents a timely
overview of the latest developments in this fascinating cross-disciplinary
area. We first provide a background of quantum probability and quantum
cognition at a theoretical level, analyzing their advantages over classical
theories in modeling the cognitive aspects of sentiment analysis. Then, recent
quantum-cognitively inspired models are introduced and discussed in detail,
focusing on how they approach the key challenges of the sentiment analysis
task. Finally, we discuss the limitations of the current research and highlight
future research directions
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 classification systems
for subtasks A, B, C and D. Our approach consists of two steps. In the first
step, we generate and validate diverse feature sets for twitter sentiment
evaluation, inspired by the work of participants of previous editions of such
challenges. In the second step, we focus on the optimization of the evaluation
measures of the different subtasks. To this end, we examine different learning
strategies by validating them on the data provided by the task organisers. For
our final submissions we used an ensemble learning approach (stacked
generalization) for Subtask A and single linear models for the rest of the
subtasks. In the official leaderboard we were ranked 9/35, 8/19, 1/11 and 2/14
for subtasks A, B, C and D respectively.\footnote{We make the code available
for research purposes at
\url{https://github.com/balikasg/SemEval2016-Twitter\_Sentiment\_Evaluation}.
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