TopicImpact: Improving Customer Feedback Analysis with Opinion Units for Topic Modeling and Star-Rating Prediction

• URL: http://arxiv.org/abs/2507.13392v1
• Date: Wed, 16 Jul 2025 09:19:26 GMT
• Title: TopicImpact: Improving Customer Feedback Analysis with Opinion Units for Topic Modeling and Star-Rating Prediction
• Authors: Emil Häglund, Johanna Björklund,
• Abstract summary: We improve the extraction of insights from customer reviews by restructuring the topic modelling pipeline to operate on opinion units.<n>The result is a heightened performance of the subsequent topic modeling, leading to coherent and interpretable topics.<n>By correlating the topics and sentiments with business metrics, such as star ratings, we can gain insights on how specific customer concerns impact business outcomes.
• Score: 0.29021840487584505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We improve the extraction of insights from customer reviews by restructuring the topic modelling pipeline to operate on opinion units - distinct statements that include relevant text excerpts and associated sentiment scores. Prior work has demonstrated that such units can be reliably extracted using large language models. The result is a heightened performance of the subsequent topic modeling, leading to coherent and interpretable topics while also capturing the sentiment associated with each topic. By correlating the topics and sentiments with business metrics, such as star ratings, we can gain insights on how specific customer concerns impact business outcomes. We present our system's implementation, use cases, and advantages over other topic modeling and classification solutions. We also evaluate its effectiveness in creating coherent topics and assess methods for integrating topic and sentiment modalities for accurate star-rating prediction.

Related papers

• Holistic Evaluations of Topic Models [0.0]
  This article evaluates topic models from a database perspective, drawing insights from 1140 BERTopic model runs.<n>The goal is to identify trade-offs in optimizing model parameters and to reflect on what these findings mean for the interpretation and responsible use of topic models.
  arXiv  Detail & Related papers  (2025-07-31T09:20:04Z)
• Decomposed Opinion Summarization with Verified Aspect-Aware Modules [82.38097397662436]
  We propose a domain-agnostic modular approach guided by review aspects.<n>We conduct experiments across datasets representing scientific research, business, and product domains.
  arXiv  Detail & Related papers  (2025-01-27T09:29:55Z)
• Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models [53.337728969143086]
  Recommendation systems harness user-item interactions like clicks and reviews to learn their representations. Previous studies improve recommendation accuracy and interpretability by modeling user preferences across various aspects and intents. We introduce a chain-based prompting approach to uncover semantic aspect-aware interactions.
  arXiv  Detail & Related papers  (2023-12-26T15:44:09Z)
• How Well Do Text Embedding Models Understand Syntax? [50.440590035493074]
  The ability of text embedding models to generalize across a wide range of syntactic contexts remains under-explored. Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges. We propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios.
  arXiv  Detail & Related papers  (2023-11-14T08:51:00Z)
• Exploring the Power of Topic Modeling Techniques in Analyzing Customer Reviews: A Comparative Analysis [0.0]
  Machine learning and natural language processing algorithms have been deployed to analyze the vast amount of textual data available online. In this study, we examine and compare five frequently used topic modeling methods specifically applied to customer reviews. Our findings reveal that BERTopic consistently yield more meaningful extracted topics and achieve favorable results.
  arXiv  Detail & Related papers  (2023-08-19T08:18:04Z)
• SummIt: Iterative Text Summarization via ChatGPT [12.966825834765814]
  We propose SummIt, an iterative text summarization framework based on large language models like ChatGPT. Our framework enables the model to refine the generated summary iteratively through self-evaluation and feedback. We also conduct a human evaluation to validate the effectiveness of the iterative refinements and identify a potential issue of over-correction.
  arXiv  Detail & Related papers  (2023-05-24T07:40:06Z)
• Topic-Selective Graph Network for Topic-Focused Summarization [0.0]
  We propose a topic-arc recognition objective and topic-selective graph network. First, the topic-arc recognition objective is used to model training, which endows the capability to discriminate topics for the model. The topic-selective graph network can conduct topic-guided cross-interaction on sentences based on the results of topic-arc recognition.
  arXiv  Detail & Related papers  (2023-02-25T15:56:06Z)
• Aspect-Controllable Opinion Summarization [58.5308638148329]
  We propose an approach that allows the generation of customized summaries based on aspect queries. Using a review corpus, we create a synthetic training dataset of (review, summary) pairs enriched with aspect controllers. We fine-tune a pretrained model using our synthetic dataset and generate aspect-specific summaries by modifying the aspect controllers.
  arXiv  Detail & Related papers  (2021-09-07T16:09:17Z)
• ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining [61.82562838486632]
  We crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads. We benchmark state-of-the-art models on our datasets and analyze characteristics associated with the data.
  arXiv  Detail & Related papers  (2021-06-01T22:17:13Z)
• A Topic Coverage Approach to Evaluation of Topic Models [0.0]
  We investigate an approach to topic model evaluation based on measuring topic coverage. We demonstrate the benefits of the approach by evaluating, in a series of experiments, different types of topic models. The contributions of the paper include the measures of coverage and the recommendations for the use of topic models for topic discovery.
  arXiv  Detail & Related papers  (2020-12-11T12:08:27Z)
• A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews [35.802290746473524]
  We propose a neural topic model combined with adversarial training to disentangle opinion topics from plot and neutral ones. We conduct an experimental assessment introducing a new collection of movie and book reviews paired with their plots. Showing an improved coherence and variety of topics, a consistent disentanglement rate, and sentiment classification performance superior to other supervised topic models.
  arXiv  Detail & Related papers  (2020-10-22T02:15:13Z)
• 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)

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.