Towards Quantifying the Distance between Opinions

• URL: http://arxiv.org/abs/2001.09879v1
• Date: Mon, 27 Jan 2020 16:01:10 GMT
• Title: Towards Quantifying the Distance between Opinions
• Authors: Saket Gurukar, Deepak Ajwani, Sourav Dutta, Juho Lauri, Srinivasan Parthasarathy, Alessandra Sala
• Abstract summary: We find that measures based solely on text similarity or on overall sentiment often fail to effectively capture the distance between opinions. We propose a new distance measure for capturing the similarity between opinions that leverages the nuanced observation. In an unsupervised setting, our distance measure achieves significantly better Adjusted Rand Index scores (up to 56x) and Silhouette coefficients (up to 21x) compared to existing approaches.
• Score: 66.29568619199074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Increasingly, critical decisions in public policy, governance, and business strategy rely on a deeper understanding of the needs and opinions of constituent members (e.g. citizens, shareholders). While it has become easier to collect a large number of opinions on a topic, there is a necessity for automated tools to help navigate the space of opinions. In such contexts understanding and quantifying the similarity between opinions is key. We find that measures based solely on text similarity or on overall sentiment often fail to effectively capture the distance between opinions. Thus, we propose a new distance measure for capturing the similarity between opinions that leverages the nuanced observation -- similar opinions express similar sentiment polarity on specific relevant entities-of-interest. Specifically, in an unsupervised setting, our distance measure achieves significantly better Adjusted Rand Index scores (up to 56x) and Silhouette coefficients (up to 21x) compared to existing approaches. Similarly, in a supervised setting, our opinion distance measure achieves considerably better accuracy (up to 20% increase) compared to extant approaches that rely on text similarity, stance similarity, and sentiment similarity

Related papers

• Rater Cohesion and Quality from a Vicarious Perspective [22.445283423317754]
  Vicarious annotation is a method for breaking down disagreement by asking raters how they think others would annotate the data. We employ rater cohesion metrics to study the potential influence of political affiliations and demographic backgrounds on raters' perceptions of offense. We study how the rater quality metrics influence the in-group and cross-group rater cohesion across the personal and vicarious levels.
  arXiv  Detail & Related papers  (2024-08-15T20:37:36Z)
• Predicting Text Preference Via Structured Comparative Reasoning [110.49560164568791]
  We introduce SC, a prompting approach that predicts text preferences by generating structured intermediate comparisons. We select consistent comparisons with a pairwise consistency comparator that ensures each aspect's comparisons clearly distinguish differences between texts. Our comprehensive evaluations across various NLP tasks, including summarization, retrieval, and automatic rating, demonstrate that SC equips LLMs to achieve state-of-the-art performance in text preference prediction.
  arXiv  Detail & Related papers  (2023-11-14T18:51:38Z)
• Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification [14.212306015270208]
  We propose a novel Aspect-oriented Opinion Alignment Network (AOAN) to capture the contextual association between opinion words and the corresponding aspect. In addition, we design a multi-perspective attention mechanism that align relevant opinion information with respect to the given aspect. Our model achieves state-of-the-art results on three benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-22T13:55:36Z)
• Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness [55.037030060643126]
  Intersectionality is a critical framework that allows us to examine how social inequalities persist. We argue that adopting intersectionality as an analytical framework is pivotal to effectively operationalizing fairness.
  arXiv  Detail & Related papers  (2023-03-16T21:02:09Z)
• Attributable Visual Similarity Learning [90.69718495533144]
  This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images. Motivated by the human semantic similarity cognition, we propose a generalized similarity learning paradigm to represent the similarity between two images with a graph. Experiments on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate significant improvements over existing deep similarity learning methods.
  arXiv  Detail & Related papers  (2022-03-28T17:35:31Z)
• On Quantitative Evaluations of Counterfactuals [88.42660013773647]
  This paper consolidates work on evaluating visual counterfactual examples through an analysis and experiments. We find that while most metrics behave as intended for sufficiently simple datasets, some fail to tell the difference between good and bad counterfactuals when the complexity increases. We propose two new metrics, the Label Variation Score and the Oracle score, which are both less vulnerable to such tiny changes.
  arXiv  Detail & Related papers  (2021-10-30T05:00:36Z)
• Fine-Grained Opinion Summarization with Minimal Supervision [48.43506393052212]
  FineSum aims to profile a target by extracting opinions from multiple documents. FineSum automatically identifies opinion phrases from the raw corpus, classifies them into different aspects and sentiments, and constructs multiple fine-grained opinion clusters under each aspect/sentiment. Both automatic evaluation on the benchmark and quantitative human evaluation validate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2021-10-17T15:16:34Z)
• CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability [15.342039156426843]
  We argue that the goal of a precise formulation of desiderata is an ill-posed one, as diverse applications may always warrant distinct scenarios. In addition to promoting compact specification in the spirit of recent replication trends, the CLEVA- compass provides an intuitive chart to understand the priorities of individual systems.
  arXiv  Detail & Related papers  (2021-10-07T10:53:26Z)
• A Variational Approach to Unsupervised Sentiment Analysis [8.87759101018566]
  We propose a variational approach to unsupervised sentiment analysis. We use target-opinion word pairs as a supervision signal. We apply our method to sentiment analysis on customer reviews and clinical narratives.
  arXiv  Detail & Related papers  (2020-08-21T09:52:35Z)
• Don't Let Me Be Misunderstood: Comparing Intentions and Perceptions in Online Discussions [17.430757860728733]
  We present a computational framework for exploring and comparing perspectives in online public discussions. We combine logged data about public comments on Facebook with a survey of over 16,000 people about their intentions in writing these comments. Our analysis focuses on judgments of whether a comment is stating a fact or an opinion, since these concepts were shown to be often confused.
  arXiv  Detail & Related papers  (2020-04-28T15:43:46Z)

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.