Papers Sentiment Classification

Is syntax structure modeling worth? Leveraging pattern-driven modeling to enable affordable sentiment dependency learning

Is structure information modeling really worth in Aspect-based sentiment classification (ABSC)? Recent popular works tend to exploit syntactic information guiding sentiment dependency parsing, i.e., structure-based senti…

Sentiment Analysis on Brazilian Portuguese User Reviews

Sentiment Analysis is one of the most classical and primarily studied natural language processing tasks. This problem had a notable advance with the proposition of more complex and scalable machine learning models. Despi…

Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks

This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks. Although some CL techniques have been proposed for document sentiment classification, we are not aware of any CL wo…

CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks

This paper studies continual learning (CL) of a sequence of aspect sentiment classification(ASC) tasks in a particular CL setting called domain incremental learning (DIL). Each task is from a different domain or product.…

Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification

State-of-the-art brain-to-text systems have achieved great success in decoding language directly from brain signals using neural networks. However, current approaches are limited to small closed vocabularies which are fa…

A Pretrained YouTuber Embeddings for Improving Sentiment Classification of YouTube Comments

Sentiment Analysis For Bengali Using Transformer Based Models

Sentiment analysis is one of the key Natural Language Processing (NLP) tasks that has been attempted by researchers extensively for resource-rich languages like English. But for low resource languages like Bengali very f…

Sentiment Analysis and Effect of COVID-19 Pandemic using College SubReddit Data

Background: The COVID-19 pandemic has affected our society and human well-being in various ways. In this study, we investigate how the pandemic has influenced people's emotions and psychological states compared to a pre-…

Topic Driven Adaptive Network for Cross-Domain Sentiment Classification

Cross-domain sentiment classification has been a hot spot these years, which aims to learn a reliable classifier using labeled data from a source domain and evaluate it on a target domain. In this vein, most approaches u…

Isomer: Transfer enhanced Dual-Channel Heterogeneous Dependency Attention Network for Aspect-based Sentiment Classification

Aspect-based sentiment classification aims to predict the sentiment polarity of a specific aspect in a sentence. However, most existing methods attempt to construct dependency relations into a homogeneous dependency grap…

AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation

The smartphone apps market is growing rapidly which challenges apps owners to continue improving their products and to compete in the market. The analysis of users feedback is a key enabler for improvements as stakeholde…

Pre-training Pre-trained Models with Auxiliary Labels and Fine-tuning for Text Classification

With the development of pre-trained models, the performance of text classification has been continuously improved. However, we argue that directly employing features generated by pre-trained models for text classificatio…

Prompt-Learning for Fine-Grained Entity Typing

As an effective approach to tune pre-trained language models (PLMs) for specific tasks, prompt-learning has recently attracted much attention from researchers. By using cloze-style language prompts to stimulate the versa…

Can Pre-trained Language Models Interpret Similes as Smart as Human?

Simile interpretation is a crucial task in natural language processing. Nowadays, pre-trained language models (PLMs) have achieved state-of-the-art performance on many tasks. However, it remains under-explored whether PL…

Contrastive Clustering: Toward Unsupervised Bias Reduction for Emotion and Sentiment Classification

Background: When neural network emotion and sentiment classifiers are used in public health informatics studies, biases present in the classifiers could produce inadvertently misleading results. Objective: This study ass…

TaskDrop: A Competitive Baseline for Continual Learning of Sentiment Classification

In this paper, we study the multi-task sentiment classification problem in the continual learning setting, i.e., a model is sequentially trained to classifier the sentiment of reviews of products in a particular category…

Casting the Same Sentiment Classification Problem

We introduce and study a problem variant of sentiment analysis, namely the “same sentiment classification problem”, where, given a pair of texts, the task is to determine if they have the same sentiment, disregarding the…

Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification

Recent multilingual pre-trained models, like XLM-RoBERTa (XLM-R), have been demonstrated effective in many cross-lingual tasks. However, there are still gaps between the contextualized representations of similar words in…

Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification

Data augmentation and adversarial perturbation approaches have recently achieved promising results in solving the over-fitting problem in many natural language processing (NLP) tasks including sentiment classification. H…

Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection

Aspect terms extraction (ATE) and aspect sentiment classification (ASC) are two fundamental and fine-grained sub-tasks in aspect-level sentiment analysis (ALSA). In the textual analysis, joint extracting both aspect term…