SemEval-2016 task 4: Sentiment analysis in Twitter
This paper discusses the fourth year of the``Sentiment Analysis in Twitter Task''. SemEval-
2016 Task 4 comprises five subtasks, three of which represent a significant departure from …
2016 Task 4 comprises five subtasks, three of which represent a significant departure from …
Datastories at semeval-2017 task 4: Deep lstm with attention for message-level and topic-based sentiment analysis
In this paper we present two deep-learning systems that competed at SemEval-2017 Task 4
“Sentiment Analysis in Twitter”. We participated in all subtasks for English tweets, involving …
“Sentiment Analysis in Twitter”. We participated in all subtasks for English tweets, involving …
Evaluation of deep learning techniques in sentiment analysis from twitter data
D Goularas, S Kamis - … on deep learning and machine learning …, 2019 - ieeexplore.ieee.org
This study presents a comparison of different deep learning methods used for sentiment
analysis in Twitter data. In this domain, deep learning (DL) techniques, which contribute at …
analysis in Twitter data. In this domain, deep learning (DL) techniques, which contribute at …
Multi-label emotion classification in texts using transfer learning
Social media is a widespread platform that provides a massive amount of user-generated
content that can be mined to reveal the emotions of social media users. This has many …
content that can be mined to reveal the emotions of social media users. This has many …
A comparative study of effective approaches for Arabic sentiment analysis
Sentiment analysis (SA) is a natural language processing (NLP) application that aims to
analyse and identify sentiment within a piece of text. Arabic SA started to receive more …
analyse and identify sentiment within a piece of text. Arabic SA started to receive more …
Cross-media learning for image sentiment analysis in the wild
Much progress has been made in the field of sentiment analysis in the past years.
Researchers relied on textual data for this task, while only recently they have started …
Researchers relied on textual data for this task, while only recently they have started …
Ntua-slp at semeval-2018 task 1: Predicting affective content in tweets with deep attentive rnns and transfer learning
In this paper we present deep-learning models that submitted to the SemEval-2018 Task~ 1
competition:" Affect in Tweets". We participated in all subtasks for English tweets. We …
competition:" Affect in Tweets". We participated in all subtasks for English tweets. We …
Lexicon integrated CNN models with attention for sentiment analysis
With the advent of word embeddings, lexicons are no longer fully utilized for sentiment
analysis although they still provide important features in the traditional setting. This paper …
analysis although they still provide important features in the traditional setting. This paper …
Textual sentiment analysis via three different attention convolutional neural networks and cross-modality consistent regression
Z Zhang, Y Zou, C Gan - Neurocomputing, 2018 - Elsevier
Word embeddings and CNN (convolutional neural networks) architecture are crucial
ingredients of sentiment analysis. However, sentiment and lexicon embeddings are rarely …
ingredients of sentiment analysis. However, sentiment and lexicon embeddings are rarely …
Attention and lexicon regularized LSTM for aspect-based sentiment analysis
L Bao, P Lambert, T Badia - … of the 57th annual meeting of the …, 2019 - aclanthology.org
Attention based deep learning systems have been demonstrated to be the state of the art
approach for aspect-level sentiment analysis, however, end-to-end deep neural networks …
approach for aspect-level sentiment analysis, however, end-to-end deep neural networks …