Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews

• URL: http://arxiv.org/abs/2306.02043v1
• Date: Sat, 3 Jun 2023 07:51:57 GMT
• Title: Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews
• Authors: Yukyung Lee, Jaehee Kim, Doyoon Kim, Yookyung Kho, Younsun Kim, Pilsung Kang
• Abstract summary: We propose Painsight, an unsupervised framework for extracting dissatisfaction factors from customer reviews. Painsight employs pre-trained language models to construct sentiment analysis and topic models. It successfully identified and categorized dissatisfaction factors within each group, as well as isolated factors for each type.
• Score: 7.897859138153238
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers. Previous studies have endeavored to identify key aspects of customer reviews through the development of sentiment analysis models and topic models. However, extracting specific dissatisfaction factors remains a challenging task. In this study, we delineate the pain point detection problem and propose Painsight, an unsupervised framework for automatically extracting distinct dissatisfaction factors from customer reviews without relying on ground truth labels. Painsight employs pre-trained language models to construct sentiment analysis and topic models, leveraging attribution scores derived from model gradients to extract dissatisfaction factors. Upon application of the proposed methodology to customer review data spanning five product categories, we successfully identified and categorized dissatisfaction factors within each group, as well as isolated factors for each type. Notably, Painsight outperformed benchmark methods, achieving substantial performance enhancements and exceptional results in human evaluations.

Related papers

• Aspect-Aware Decomposition for Opinion Summarization [82.38097397662436]
  We propose a modular approach guided by review aspects which separates the tasks of aspect identification, opinion consolidation, and meta-review synthesis. We conduct experiments across datasets representing scientific research, business, and product domains. Results show that our method generates more grounded summaries compared to strong baseline models.
  arXiv  Detail & Related papers  (2025-01-27T09:29:55Z)
• Were You Helpful -- Predicting Helpful Votes from Amazon Reviews [0.0]
  This project investigates factors that influence the perceived helpfulness of Amazon product reviews through machine learning techniques. We identify key metadata characteristics that serve as strong predictors of review helpfulness. This insight suggests that contextual and user-behavioral factors may be more indicative of review helpfulness than the linguistic content itself.
  arXiv  Detail & Related papers  (2024-12-03T22:38:58Z)
• Sentiment Analysis Based on RoBERTa for Amazon Review: An Empirical Study on Decision Making [0.0]
  We leverage state-of-the-art Natural Language Processing (NLP) techniques to perform sentiment analysis on Amazon product reviews. We employ transformer-based models, RoBERTa, to derive sentiment scores that accurately reflect the emotional tones of the reviews.
  arXiv  Detail & Related papers  (2024-10-18T22:46:27Z)
• Unveiling the Achilles' Heel of NLG Evaluators: A Unified Adversarial Framework Driven by Large Language Models [52.368110271614285]
  We introduce AdvEval, a novel black-box adversarial framework against NLG evaluators. AdvEval is specially tailored to generate data that yield strong disagreements between human and victim evaluators. We conduct experiments on 12 victim evaluators and 11 NLG datasets, spanning tasks including dialogue, summarization, and question evaluation.
  arXiv  Detail & Related papers  (2024-05-23T14:48:15Z)
• Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform [64.76104135495576]
  We propose a novel anonymous crowd-sourcing evaluation platform, BingJian, for large language models. Through this platform, users have the opportunity to submit their questions, testing the models on a personalized and potentially broader range of capabilities.
  arXiv  Detail & Related papers  (2024-03-13T07:31:20Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• Deep Analysis of Visual Product Reviews [3.120478415450056]
  In the past, the researchers worked on analyzing language feedback, but here we do not take any assistance from linguistic reviews that may be absent. We propose a hierarchical architecture, where the higher-level model engages in product categorization, and the lower-level model pays attention to predicting the review score from a customer-provided product image. The proposed hierarchical architecture attained a 57.48% performance improvement over the single-level best comparable architecture.
  arXiv  Detail & Related papers  (2022-07-19T18:10:43Z)
• Latent Aspect Detection from Online Unsolicited Customer Reviews [3.622430080512776]
  Aspect detection helps product owners and service providers to identify shortcomings and prioritize customers' needs. Existing methods focus on detecting the surface form of an aspect by training supervised learning methods that fall short when aspects are latent in reviews. We propose an unsupervised method to extract latent occurrences of aspects.
  arXiv  Detail & Related papers  (2022-04-14T13:46:25Z)
• Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach [131.20444904674494]
  We tackle the problem of measuring group fairness under unawareness of sensitive attributes. We show that quantification approaches are particularly suited to tackle the fairness-under-unawareness problem.
  arXiv  Detail & Related papers  (2021-09-17T13:45:46Z)
• SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation [48.1799451277808]
  We propose a Sentiment-aware Interactive Fusion Network (SIFN) for review-based item recommendation. We first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review. Then, we propose a sentiment prediction task that guides the sentiment learner to extract sentiment-aware features via explicit sentiment labels.
  arXiv  Detail & Related papers  (2021-08-18T08:04:38Z)
• Mining customer product reviews for product development: A summarization process [0.7742297876120561]
  This research set out to identify and structure from online reviews the words and expressions related to customers' likes and dislikes to guide product development. The authors propose a summarization model containing multiples aspects of user preference, such as product affordances, emotions, usage conditions. A case study demonstrates that with the proposed model and the annotation guidelines, human annotators can structure the online reviews with high inter-agreement.
  arXiv  Detail & Related papers  (2020-01-13T13:01:14Z)

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.