Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations (D18-1)
Copied to clipboard
| Challenge: | Existing models for finding aspects and sentiments in opinionated texts ignore sentiments and are not supervised. |
| Approach: | They propose a probabilistic model that finds aspects and sentiments in opinionated texts . they use authors, discourse relations, and word embeddings to capture regularities . |
| Outcome: | The proposed model outperforms state-of-the-art models in topic cohesion and sentiment classification. |
Similar Papers
Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for aspect-based sentiment analysis of review text use only a few keywords describing each aspect/sentiment without using any labeled examples. |
| Approach: | They propose a weakly-supervised approach for aspect-based sentiment analysis which uses only a few keywords describing each aspect/sentiment without using any labeled examples. |
| Outcome: | The proposed method generates quality joint topics and outperforms baselines significantly on benchmark datasets. |
Leveraging Structural and Semantic Correspondence for Attribute-Oriented Aspect Sentiment Discovery (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to inference opinionated text do not capture attributes in a one-off manner. |
| Approach: | They propose a probabilistic model that discovers aspects and sentiments from text and associates them with different attributes. |
| Outcome: | The proposed model outperforms state-of-the-art models and yields intuitive topics. |
Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Aspect-based sentiment analysis (ABSA) is a task of analyzing people's sentiments at the aspect level. |
| Approach: | They propose a unified bidirectional generative framework to tackle cross-domain ABSA tasks . the framework trains a model in both text-to-label and label-totext directions . |
| Outcome: | The proposed framework trains a model in both label-to-label and label- to-text directions to learn domain-agnostic features. |
Aspect-based Sentiment Analysis in Question Answering Forums (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on aspects-based sentiment analysis focus on a single opinionated sentence. |
| Approach: | They propose a model to combine aspects and their sentiments for QA forums . they use cross-sentence aspect-opinion interaction modeling to align the aspect mentioned in the question and associated opinion clues in the answer. |
| Outcome: | The proposed model outperforms baseline models on three real-world datasets. |
From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Models (2025.naacl-srw)
Copied to clipboard
| Challenge: | Using a synthetic sports feedback dataset, we evaluate open-weight LLMs’ ability to extract aspect-polarity pairs. |
| Approach: | They propose a metric to facilitate the evaluation of aspect extraction with generative models. |
| Outcome: | The proposed metric improves the performance of open-weight LLMs in the Aspect-Based Sentiment Analysis task. |
Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis (N18-2)
Copied to clipboard
Devamanyu Hazarika, Soujanya Poria, Prateek Vij, Gangeshwar Krishnamurthy, Erik Cambria, Roger Zimmermann
| Challenge: | Present neural-based models exploit aspect and its contextual information in the sentence but ignore inter-aspect dependencies. |
| Approach: | They propose to combine aspect-based sentiment analysis with temporal dependency processing to incorporate this pattern into a sentence. |
| Outcome: | The proposed approach is based on the SemEval 2014 dataset and shows that it is effective for predicting sentiments of aspects in sentences with multiple aspects. |
GCNet: Global-and-Context Collaborative Learning for Aspect-Based Sentiment Analysis (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for analyzing aspect terms are focused on extracting semantic information inherent within the sentence. |
| Approach: | They propose a GCNet that explicitly leverages global semantic information to guide context encoding. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets. |
Aspect Sentiment Quad Prediction as Paraphrase Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies focus on predicting the four elements in one shot, instead of predicting them all. |
| Approach: | They propose a task to jointly detect all sentiment elements in quads for a given opinionated sentence. |
| Outcome: | The proposed method can generate the semantics of the sentiment elements in the natural language form. |
Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings (P18-1)
Copied to clipboard
| Challenge: | Existing word embedding methods for natural language processing are limited in their ability to produce dense word embeds. |
| Approach: | They propose a word embedding SentiVec which is infused with sentiment information from a lexical resource and outperforms baselines on subjectivity-sensitive tasks. |
| Outcome: | The proposed word embedding SentiVec outperforms baselines on subjectivity-sensitive tasks. |
Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing ABSA models do not pay attention to aspect terms and their contexts . a discriminator is introduced to improve ABSA, allowing for better understanding of aspect terms . |
| Approach: | They propose to improve ABSA by complementary learning of aspect terms . they explicitly recover aspect terms from each input sentence to better understand aspects . |
| Outcome: | The proposed approach improves ABSA on five widely used English benchmark datasets. |