Aspect-Level Sentiment Analysis through Aspect-Oriented Features
HighTech and Innovation JournalAspect-level sentiment analysis is essential for businesses to comprehend sentiment polarities associated with various aspects within unstructured texts.
Mikail Bin Muhammad Azman Busst +2 more
doaj +1 more source
SemEval-2016 Task 2: Interpretable Semantic Textual Similarity [PDF]
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016 Comunicació presentada al 10th International Workshop on Semantic Evaluation (SemEval-2016), celebrat els dies 16 i 17 de juny de 2016 a San Diego, Califòrnia. The final goal of Interpretable Semantic Textual Similarity (iSTS) is to build systems that explain which are the differences and commonalities between two sentences. The task
Agirre, Eneko +5 more
openaire +3 more sources
General Purpose Textual Sentiment Analysis and Emotion Detection Tools [PDF]
, 2013 Textual sentiment analysis and emotion detection consists in retrieving the sentiment or emotion carried by a text or document. This task can be useful in many domains: opinion mining, prediction, feedbacks, etc.
Bellalem, Nadia +2 more
core +2 more sources
Noise or music? Investigating the usefulness of normalisation for robust sentiment analysis on social media data [PDF]
, 2017 In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy.
De Clercq, Orphée +5 more
core +1 more source
Relation Classification via Recurrent Neural Network with Attention and Tensor Layers
Big Data Mining and Analytics, 2018 Relation classification is a crucial component in many Natural Language Processing (NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture (using Long Short-Term Memory, LSTM, cells) for relation ...
Runyan Zhang +3 more
doaj +1 more source
A Lexicon-Enhanced Attention Network for Aspect-Level Sentiment Analysis
IEEE Access, 2020 Aspect-level sentiment classification is a fine-grained task in sentiment analysis. In recent years, researchers have realized the importance of the relationship between aspect term and sentence and many classification models based on deep learning ...
Zhiying Ren +5 more
doaj +1 more source
HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings [PDF]
, 2019 We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (QasemiZadeh et al., 2019). Our approach separates this task into two independent steps: verb clustering using word and their context embeddings
arxiv +1 more source
Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM [PDF]
arXiv, 2017 This paper describes team Turing's submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A). Subtask A addresses the challenge of rumour stance classification, which involves identifying the attitude of Twitter users towards the truthfulness of the rumour they are discussing.
arxiv
A Hybrid Deep Implicit Neural Model for Sentiment Analysis via Transfer Learning
IEEE AccessWe present a neural model for sentiment analysis of social network texts with a special focus on cryptocurrency-related content using deep transfer learning. A challenge of deep learning is its need for abundant data.
Kia Jahanbin, Mohammad Ali Zare Chahooki
doaj +1 more source
SemEval-2014 Task 9: Sentiment Analysis in Twitter [PDF]
SemEval-2014, 2019 We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year's task that ran successfully as part of SemEval-2013. As in 2013, this was the most popular SemEval task; a total of 46 teams contributed 27 submissions for subtask A (21 teams) and 50 submissions for subtask B (44 teams).
arxiv