Papers Sentiment Classification
“Sentiment Classification” 태그가 달린 논문 949편 · 필터 해제
Geo-located Aspect Based Sentiment Analysis (ABSA) for Crowdsourced Evaluation of Urban Environments
Sentiment analysis methods are rapidly being adopted by the field of Urban Design and Planning, for the crowdsourced evaluation of urban environments. However, most models used within this domain are able to identify pos…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Sentiment AnalysisSentiment Classification+1Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations
Aspect-based sentiment analysis (ABSA), a fine-grained sentiment classification task, has received much attention recently. Many works investigate sentiment information through opinion words, such as ''good'' and ''bad''…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)SentenceSentiment Analysis+1A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis
Few-shot text classification has attracted great interest in both academia and industry due to the lack of labeled data in many fields. Different from general text classification (e.g., topic classification), few-shot se…
ClassificationContrastive LearningFew-Shot Text ClassificationSentiment Analysis+4Deep Learning-based Sentiment Classification: A Comparative Survey
Recently, Deep Learning (DL) approaches have been applied to solve the Sentiment Classification (SC) problem, which is a core task in reviews mining or Sentiment Analysis (SA). The performances of these approaches are af…
ClassificationDeep LearningSentiment AnalysisSentiment Classification+1Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification
Label smoothing is a widely used technique in various domains, such as text classification, image classification and speech recognition, known for effectively combating model overfitting. However, there is little fine-gr…
Classificationimage-classificationImage ClassificationSentiment Analysis+5User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models
While there is significant interest in using generative AI tools as general-purpose models for specific ML applications, discriminative models are much more widely deployed currently. One of the key shortcomings of these…
Image Generationobject-detectionObject DetectionSentiment Analysis+1Perceiving University Student's Opinions from Google App Reviews
Google app market captures the school of thought of users from every corner of the globe via ratings and text reviews, in a multilinguistic arena. The potential information from the reviews cannot be extracted manually, …
Ensemble LearningSentiment AnalysisSentiment ClassificationKnowledge Graph Enhanced Aspect-Level Sentiment Analysis
In this paper, we propose a novel method to enhance sentiment analysis by addressing the challenge of context-specific word meanings. It combines the advantages of a BERT model with a knowledge graph based synonym data. …
Sentiment AnalysisSentiment ClassificationTurkishBERTweet: Fast and Reliable Large Language Model for Social Media Analysis
Turkish is one of the most popular languages in the world. Wide us of this language on social media platforms such as Twitter, Instagram, or Tiktok and strategic position of the country in the world politics makes it app…
Hate Speech DetectionLanguage ModelingLanguage ModellingLarge Language Model+4Natural Language Processing Through Transfer Learning: A Case Study on Sentiment Analysis
Artificial intelligence and machine learning have significantly bolstered the technological world. This paper explores the potential of transfer learning in natural language processing focusing mainly on sentiment analys…
Experimental DesignSentiment AnalysisSentiment ClassificationTransfer LearningSyntactic Fusion: Enhancing Aspect-Level Sentiment Analysis Through Multi-Tree Graph Integration
Recent progress in aspect-level sentiment classification has been propelled by the incorporation of graph neural networks (GNNs) leveraging syntactic structures, particularly dependency trees. Nevertheless, the performan…
Sentiment AnalysisSentiment ClassificationBERT Goes Off-Topic: Investigating the Domain Transfer Challenge using Genre Classification
While performance of many text classification tasks has been recently improved due to Pre-trained Language Models (PLMs), in this paper we show that they still suffer from a performance gap when the underlying distributi…
ClassificationGenre classificationSentiment AnalysisSentiment Classification+2Benchmarking Large Language Model Volatility
The impact of non-deterministic outputs from Large Language Models (LLMs) is not well examined for financial text understanding tasks. Through a compelling case study on investing in the US equity market via news sentime…
BenchmarkingDecision MakingDecoderLanguage Modeling+6A BERT based Ensemble Approach for Sentiment Classification of Customer Reviews and its Application to Nudge Marketing in e-Commerce
According to the literature, Product reviews are an important source of information for customers to support their buying decision. Product reviews improve customer trust and loyalty. Reviews help customers in understand…
MarketingSentiment AnalysisSentiment ClassificationHow good are Large Language Models on African Languages?
Recent advancements in natural language processing have led to the proliferation of large language models (LLMs). These models have been shown to yield good performance, using in-context learning, even on tasks and langu…
In-Context LearningLanguage ModellingMachine Translationnamed-entity-recognition+6Zero-Shot Cross-Lingual Sentiment Classification under Distribution Shift: an Exploratory Study
The brittleness of finetuned language model performance on out-of-distribution (OOD) test samples in unseen domains has been well-studied for English, yet is unexplored for multi-lingual models. Therefore, we study gener…
Cross-Lingual Sentiment ClassificationCross-Lingual TransferLanguage ModellingSentiment Analysis+3RankAug: Augmented data ranking for text classification
Research on data generation and augmentation has been focused majorly on enhancing generation models, leaving a notable gap in the exploration and refinement of methods for evaluating synthetic data. There are several te…
ClassificationDiversityNatural Language UnderstandingSentiment Analysis+4Modelling Sentiment Analysis: LLMs and data augmentation techniques
This paper provides different approaches for a binary sentiment classification on a small training dataset. LLMs that provided state-of-the-art results in sentiment analysis and similar domains are being used, such as BE…
Data AugmentationSentiment AnalysisSentiment ClassificationJoint Learning of Local and Global Features for Aspect-based Sentiment Classification
Aspect-based sentiment classification (ASC) aims to judge the sentiment polarity conveyed by the given aspect term in a sentence. The sentiment polarity is not only determined by the local context but also related to the…
Graph AttentionRepresentation LearningSentenceSentiment Analysis+2Causal Interpretation of Self-Attention in Pre-Trained Transformers
We propose a causal interpretation of self-attention in the Transformer neural network architecture. We interpret self-attention as a mechanism that estimates a structural equation model for a given input sequence of sym…
Causal DiscoverySentiment AnalysisSentiment Classificationvalid