From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision

• URL: http://arxiv.org/abs/2305.01710v4
• Date: Wed, 13 Aug 2025 16:58:53 GMT
• Title: From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision
• Authors: Wenchang Li, John P. Lalor, Yixing Chen, Vamsi K. Kanuri,
• Abstract summary: This paper introduces unified sentiment analysis, a novel learning paradigm that integrates the three aforementioned tasks into a coherent framework.<n>We propose the Distantly Supervised Pyramid Network (DSPN), which employs a pyramid structure to capture sentiment at word, aspect, and document levels in a hierarchical manner.<n>Our findings validate DSPN's effectiveness and efficiency, establishing a robust, resource-efficient, unified framework for sentiment analysis.
• Score: 1.940999549833078
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sentiment analysis is integral to understanding the voice of the customer and informing businesses' strategic decisions. Conventional sentiment analysis involves three separate tasks: aspect-category detection, aspect-category sentiment analysis, and rating prediction. However, independently tackling these tasks can overlook their interdependencies and often requires expensive, fine-grained annotations. This paper introduces unified sentiment analysis, a novel learning paradigm that integrates the three aforementioned tasks into a coherent framework. To achieve this, we propose the Distantly Supervised Pyramid Network (DSPN), which employs a pyramid structure to capture sentiment at word, aspect, and document levels in a hierarchical manner. Evaluations on multi-aspect review datasets in English and Chinese show that DSPN, using only star rating labels for supervision, demonstrates significant efficiency advantages while performing comparably well to a variety of benchmark models. Additionally, DSPN's pyramid structure enables the interpretability of its outputs. Our findings validate DSPN's effectiveness and efficiency, establishing a robust, resource-efficient, unified framework for sentiment analysis.

Related papers

• Dynamic Span Interaction and Graph-Aware Memory for Entity-Level Sentiment Classification [0.2099922236065961]
  SpanEIT is a novel framework for entity-level sentiment classification.<n>It integrates dynamic span interaction and graph-aware memory mechanisms for enhanced entity-sentiment relational modeling.<n>Experiments on FSAD, BARU, and IMDB datasets show SpanEIT outperforms state-of-the-art transformer and hybrid baselines in accuracy and F1 scores.
  arXiv  Detail & Related papers  (2025-09-15T05:47:57Z)
• Structured Attention Matters to Multimodal LLMs in Document Understanding [52.37530640460363]
  We investigate how input format influences document comprehension performance.<n>We discover that raw OCR text often impairs rather than improves MLLMs' performance.<n>We propose a novel structure-preserving approach that encodes document elements using the LaTex paradigm.
  arXiv  Detail & Related papers  (2025-06-19T07:16:18Z)
• Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models [4.604003661048267]
  Aspect-based sentiment analysis (ASBA) is a refined approach to sentiment analysis. It aims to extract and classify sentiments based on specific aspects or features of a product, service, or entity.
  arXiv  Detail & Related papers  (2025-01-15T17:36:56Z)
• PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis [74.41260927676747]
  This paper bridges the gaps by introducing a multimodal conversational Sentiment Analysis (ABSA) To benchmark the tasks, we construct PanoSent, a dataset annotated both manually and automatically, featuring high quality, large scale, multimodality, multilingualism, multi-scenarios, and covering both implicit and explicit sentiment elements. To effectively address the tasks, we devise a novel Chain-of-Sentiment reasoning framework, together with a novel multimodal large language model (namely Sentica) and a paraphrase-based verification mechanism.
  arXiv  Detail & Related papers  (2024-08-18T13:51:01Z)
• ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSA [50.90538760832107]
  This research presents a novel task, Review-Level Opinion Aspect Sentiment Target (ROAST) ROAST seeks to close the gap between sentence-level and text-level ABSA by identifying every ABSA constituent at the review level. We extend the available datasets to enable ROAST, addressing the drawbacks noted in previous research.
  arXiv  Detail & Related papers  (2024-05-30T17:29:15Z)
• Enhanced Coherence-Aware Network with Hierarchical Disentanglement for Aspect-Category Sentiment Analysis [12.024076910894417]
  We propose an enhanced coherence-aware network with hierarchical disentanglement (ECAN) for ACSA tasks. Our ECAN effectively decouples multiple categories and sentiments entangled in the coherence representations and achieves state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2024-03-15T11:32:44Z)
• A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [51.26815896167173]
  We present a comprehensive tertiary analysis of PAMI reviews along three complementary dimensions.<n>Our analyses reveal distinctive organizational patterns as well as persistent gaps in current review practices.<n>Finally, our evaluation of state-of-the-art AI-generated reviews indicates encouraging advances in coherence and organization.
  arXiv  Detail & Related papers  (2024-02-20T11:28:50Z)
• A Novel Energy based Model Mechanism for Multi-modal Aspect-Based Sentiment Analysis [85.77557381023617]
  We propose a novel framework called DQPSA for multi-modal sentiment analysis. PDQ module uses the prompt as both a visual query and a language query to extract prompt-aware visual information. EPE module models the boundaries pairing of the analysis target from the perspective of an Energy-based Model.
  arXiv  Detail & Related papers  (2023-12-13T12:00:46Z)
• Sentiment Analysis through LLM Negotiations [58.67939611291001]
  A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round. This paper introduces a multi-LLM negotiation framework for sentiment analysis.
  arXiv  Detail & Related papers  (2023-11-03T12:35:29Z)
• OATS: Opinion Aspect Target Sentiment Quadruple Extraction Dataset for Aspect-Based Sentiment Analysis [55.61047894397937]
  Aspect-based sentiment analysis (ABSA) delves into understanding sentiments specific to distinct elements within a user-generated review. We introduce the OATS dataset, which encompasses three fresh domains and consists of 27,470 sentence-level quadruples and 17,092 review-levels. Our initiative seeks to bridge specific observed gaps: the recurrent focus on familiar domains like restaurants and laptops, limited data for intricate quadruple extraction tasks, and an occasional oversight of the synergy between sentence and review-level sentiments.
  arXiv  Detail & Related papers  (2023-09-23T07:39:16Z)
• UniSA: Unified Generative Framework for Sentiment Analysis [48.78262926516856]
  Sentiment analysis aims to understand people's emotional states and predict emotional categories based on multimodal information. It consists of several subtasks, such as emotion recognition in conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal sentiment analysis (MSA)
  arXiv  Detail & Related papers  (2023-09-04T03:49:30Z)
• Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa [23.00810941211685]
  Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in the field of sentiment analysis. Recent DeBERTa model (Decoding-enhanced BERT with disentangled attention) to solve Aspect-Based Sentiment Analysis problem.
  arXiv  Detail & Related papers  (2022-07-06T03:50:31Z)
• BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis [4.522719296659495]
  This paper proposes a unified framework to address aspect categorization and aspect-based sentiment subtasks. We introduce a mechanism to construct an auxiliary-sentence for the implicit aspect using the corpus's semantic information. We then encourage BERT to learn aspect-specific representation in response to this auxiliary-sentence, not the aspect itself.
  arXiv  Detail & Related papers  (2022-03-22T13:12:27Z)
• A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis [58.124424775536326]
  We propose a simple but effective technique based on mutual information to extract their term. Experiment results show that our proposed method outperforms the state-of-the-art methods for cross-domain ABSA by 4.32% Micro-F1.
  arXiv  Detail & Related papers  (2022-01-29T10:18:07Z)
• Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution [7.289918297809611]
  We introduce a new framework called the sentiment-aspect attribution module (SAAM) The framework works by exploiting the correlations between sentence-level embedding features and variations of document-level aspect rating scores. Experiments on a hotel review dataset and a beer review dataset have shown SAAM can improve sentiment analysis performance.
  arXiv  Detail & Related papers  (2020-12-15T16:34:36Z)
• Discovering Airline-Specific Business Intelligence from Online Passenger Reviews: An Unsupervised Text Analytics Approach [3.2872586139884623]
  Airlines can capitalize on the abundantly available online customer reviews (OCR) This paper is to discover company- and competitor-specific intelligence from OCR using an unsupervised text analytics approach. A case study involving 99,147 airline reviews of a US-based target carrier and four of its competitors is used to validate the proposed approach.
  arXiv  Detail & Related papers  (2020-12-14T23:09:10Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)
• Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis [37.20068256769269]
  Document-level Sentiment Analysis (DSA) is more challenging due to vague semantic links and complicate sentiment information. We study how to effectively generate a discriminative representation with explicit subject patterns and sentiment contexts for DSA. We design a Sentiment-based Rethinking mechanism (SR) by refining the HIN with sentiment label information to learn a more sentiment-aware document representation.
  arXiv  Detail & Related papers  (2020-07-16T16:27:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.