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"Sentiment": models, code, and papers

A Survey on Stance Detection for Mis- and Disinformation Identification

Feb 27, 2021
Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein

Detecting attitudes expressed in texts, also known as stance detection, has become an important task for the detection of false information online, be it misinformation (unintentionally false) or disinformation (intentionally false, spread deliberately with malicious intent). Stance detection has been framed in different ways, including: (a) as a component of fact-checking, rumour detection, and detecting previously fact-checked claims; or (b) as a task in its own right. While there have been prior efforts to contrast stance detection with other related social media tasks such as argumentation mining and sentiment analysis, there is no survey examining the relationship between stance detection detection and mis- and disinformation detection from a holistic viewpoint, which is the focus of this survey. We review and analyse existing work in this area, before discussing lessons learnt and future challenges.

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A Koopman Approach to Understanding Sequence Neural Models

Feb 15, 2021
Ilan Naiman, Omri Azencot

We introduce a new approach to understanding trained sequence neural models: the Koopman Analysis of Neural Networks (KANN) method. Motivated by the relation between time-series models and self-maps, we compute approximate Koopman operators that encode well the latent dynamics. Unlike other existing methods whose applicability is limited, our framework is global, and it has only weak constraints over the inputs. Moreover, the Koopman operator is linear, and it is related to a rich mathematical theory. Thus, we can use tools and insights from linear analysis and Koopman Theory in our study. For instance, we show that the operator eigendecomposition is instrumental in exploring the dominant features of the network. Our results extend across tasks and architectures as we demonstrate for the copy problem, and ECG classification and sentiment analysis tasks.

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Social Biases in NLP Models as Barriers for Persons with Disabilities

May 02, 2020
Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, Kellie Webster, Yu Zhong, Stephen Denuyl

Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models. In particular, representations encoded in models often inadvertently perpetuate undesirable social biases from the data on which they are trained. In this paper, we present evidence of such undesirable biases towards mentions of disability in two different English language models: toxicity prediction and sentiment analysis. Next, we demonstrate that the neural embeddings that are the critical first step in most NLP pipelines similarly contain undesirable biases towards mentions of disability. We end by highlighting topical biases in the discourse about disability which may contribute to the observed model biases; for instance, gun violence, homelessness, and drug addiction are over-represented in texts discussing mental illness.

* ACL 2020 
* ACL 2020 short paper. 5 pages 

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Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness

Oct 31, 2019
Alexandre Bérard, Ioan Calapodescu, Marc Dymetman, Claude Roux, Jean-Luc Meunier, Vassilina Nikoulina

We share a French-English parallel corpus of Foursquare restaurant reviews (, and define a new task to encourage research on Neural Machine Translation robustness and domain adaptation, in a real-world scenario where better-quality MT would be greatly beneficial. We discuss the challenges of such user-generated content, and train good baseline models that build upon the latest techniques for MT robustness. We also perform an extensive evaluation (automatic and human) that shows significant improvements over existing online systems. Finally, we propose task-specific metrics based on sentiment analysis or translation accuracy of domain-specific polysemous words.

* WNGT 2019 Paper 

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uniblock: Scoring and Filtering Corpus with Unicode Block Information

Aug 26, 2019
Yingbo Gao, Weiyue Wang, Hermann Ney

The preprocessing pipelines in Natural Language Processing usually involve a step of removing sentences consisted of illegal characters. The definition of illegal characters and the specific removal strategy depend on the task, language, domain, etc, which often lead to tiresome and repetitive scripting of rules. In this paper, we introduce a simple statistical method, uniblock, to overcome this problem. For each sentence, uniblock generates a fixed-size feature vector using Unicode block information of the characters. A Gaussian mixture model is then estimated on some clean corpus using variational inference. The learned model can then be used to score sentences and filter corpus. We present experimental results on Sentiment Analysis, Language Modeling and Machine Translation, and show the simplicity and effectiveness of our method.

* EMNLP2019 

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Evaluating Language Model Finetuning Techniques for Low-resource Languages

Jun 30, 2019
Jan Christian Blaise Cruz, Charibeth Cheng

Unlike mainstream languages (such as English and French), low-resource languages often suffer from a lack of expert-annotated corpora and benchmark resources that make it hard to apply state-of-the-art techniques directly. In this paper, we alleviate this scarcity problem for the low-resourced Filipino language in two ways. First, we introduce a new benchmark language modeling dataset in Filipino which we call WikiText-TL-39. Second, we show that language model finetuning techniques such as BERT and ULMFiT can be used to consistently train robust classifiers in low-resource settings, experiencing at most a 0.0782 increase in validation error when the number of training examples is decreased from 10K to 1K while finetuning using a privately-held sentiment dataset.

* Pretrained models and datasets available at 

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Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy

Sep 03, 2018
Lina Rojas-Barahona, Bo-Hsiang Tseng, Yinpei Dai, Clare Mansfield, Osman Ramadan, Stefan Ultes, Michael Crawford, Milica Gasic

In recent years, we have seen deep learning and distributed representations of words and sentences make impact on a number of natural language processing tasks, such as similarity, entailment and sentiment analysis. Here we introduce a new task: understanding of mental health concepts derived from Cognitive Behavioural Therapy (CBT). We define a mental health ontology based on the CBT principles, annotate a large corpus where this phenomena is exhibited and perform understanding using deep learning and distributed representations. Our results show that the performance of deep learning models combined with word embeddings or sentence embeddings significantly outperform non-deep-learning models in this difficult task. This understanding module will be an essential component of a statistical dialogue system delivering therapy.

* Accepted for publication at LOUHI 2018: The Ninth International Workshop on Health Text Mining and Information Analysis 

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Text Classification based on Multiple Block Convolutional Highways

Jul 23, 2018
Seyed Mahdi Rezaeinia, Ali Ghodsi, Rouhollah Rahmani

In the Text Classification areas of Sentiment Analysis, Subjectivity/Objectivity Analysis, and Opinion Polarity, Convolutional Neural Networks have gained special attention because of their performance and accuracy. In this work, we applied recent advances in CNNs and propose a novel architecture, Multiple Block Convolutional Highways (MBCH), which achieves improved accuracy on multiple popular benchmark datasets, compared to previous architectures. The MBCH is based on new techniques and architectures including highway networks, DenseNet, batch normalization and bottleneck layers. In addition, to cope with the limitations of existing pre-trained word vectors which are used as inputs for the CNN, we propose a novel method, Improved Word Vectors (IWV). The IWV improves the accuracy of CNNs which are used for text classification tasks.

* arXiv admin note: text overlap with arXiv:1711.08609 

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Improving Aspect Term Extraction with Bidirectional Dependency Tree Representation

May 21, 2018
Huaishao Luo, Tianrui Li, Bing Liu, Bin Wang, Herwig Unger

Aspect term extraction is one of the important subtasks in aspect-based sentiment analysis. Previous studies have shown that dependency tree structure representation is promising for this task. In this paper, we propose a novel bidirectional dependency tree network to extract dependency structure features from the given sentences. The key idea is to explicitly incorporate both representations gained separately from the bottom-up and top-down propagation on the given dependency syntactic tree. An end-to-end framework is proposed to integrate the embedded representations and BiLSTM plus CRF to learn both tree-structured and sequential features to solve the aspect term extraction problem. Experimental results demonstrate that the proposed model outperforms state-of-the-art baseline models on four benchmark SemEval datasets.

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DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News

May 07, 2014
Jacopo Staiano, Marco Guerini

While many lexica annotated with words polarity are available for sentiment analysis, very few tackle the harder task of emotion analysis and are usually quite limited in coverage. In this paper, we present a novel approach for extracting - in a totally automated way - a high-coverage and high-precision lexicon of roughly 37 thousand terms annotated with emotion scores, called DepecheMood. Our approach exploits in an original way 'crowd-sourced' affective annotation implicitly provided by readers of news articles from By providing new state-of-the-art performances in unsupervised settings for regression and classification tasks, even using a na\"{\i}ve approach, our experiments show the beneficial impact of harvesting social media data for affective lexicon building.

* To appear at ACL 2014. 7 pages 

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