Information Types in Product Reviews
- URL: http://arxiv.org/abs/2502.14335v1
- Date: Thu, 20 Feb 2025 07:44:04 GMT
- Title: Information Types in Product Reviews
- Authors: Ori Shapira, Yuval Piniter,
- Abstract summary: We devise a typology of 24 communicative goals in sentences from the product review domain.
In experiments, we find that the combination of classes in the typology forecasts helpfulness and sentiment of reviews.
Characterizing the types of information in reviews unlocks many opportunities for more effective consumption of this genre.
- Score: 5.202085660445395
- License:
- Abstract: Information in text is communicated in a way that supports a goal for its reader. Product reviews, for example, contain opinions, tips, product descriptions, and many other types of information that provide both direct insights, as well as unexpected signals for downstream applications. We devise a typology of 24 communicative goals in sentences from the product review domain, and employ a zero-shot multi-label classifier that facilitates large-scale analyses of review data. In our experiments, we find that the combination of classes in the typology forecasts helpfulness and sentiment of reviews, while supplying explanations for these decisions. In addition, our typology enables analysis of review intent, effectiveness and rhetorical structure. Characterizing the types of information in reviews unlocks many opportunities for more effective consumption of this genre.
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) - Towards Personalized Review Summarization by Modeling Historical Reviews
from Customer and Product Separately [59.61932899841944]
Review summarization is a non-trivial task that aims to summarize the main idea of the product review in the E-commerce website.
We propose the Heterogeneous Historical Review aware Review Summarization Model (HHRRS)
We employ a multi-task framework that conducts the review sentiment classification and summarization jointly.
arXiv Detail & Related papers (2023-01-27T12:32:55Z) - An Exploratory Analysis of Feedback Types Used in Online Coding
Exercises [0.0]
This research aims at the identification of feedback types applied by CodingBat, Scratch and Blockly.
The study revealed difficulties in identifying clear-cut boundaries between feedback types.
arXiv Detail & Related papers (2022-06-07T07:52:17Z) - Learning Opinion Summarizers by Selecting Informative Reviews [81.47506952645564]
We collect a large dataset of summaries paired with user reviews for over 31,000 products, enabling supervised training.
The content of many reviews is not reflected in the human-written summaries, and, thus, the summarizer trained on random review subsets hallucinates.
We formulate the task as jointly learning to select informative subsets of reviews and summarizing the opinions expressed in these subsets.
arXiv Detail & Related papers (2021-09-09T15:01:43Z) - 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) - Generating Diversified Comments via Reader-Aware Topic Modeling and
Saliency Detection [25.16392119801612]
We propose a reader-aware topic modeling and saliency information detection framework to enhance the quality of generated comments.
For reader-aware topic modeling, we design a variational generative clustering algorithm for latent semantic learning and topic mining from reader comments.
For saliency information detection, we introduce Bernoulli distribution estimating on news content to select saliency information.
arXiv Detail & Related papers (2021-02-13T03:50:31Z) - Hierarchical Bi-Directional Self-Attention Networks for Paper Review
Rating Recommendation [81.55533657694016]
We propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation.
Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and inter-review encoder (level three)
We are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers.
arXiv Detail & Related papers (2020-11-02T08:07:50Z) - A Unified Dual-view Model for Review Summarization and Sentiment
Classification with Inconsistency Loss [51.448615489097236]
Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms.
We propose a novel dual-view model that jointly improves the performance of these two tasks.
Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.
arXiv Detail & Related papers (2020-06-02T13:34:11Z) - Topic Detection and Summarization of User Reviews [6.779855791259679]
We propose an effective new summarization method by analyzing both reviews and summaries.
A new dataset comprising product reviews and summaries about 1028 products are collected from Amazon and CNET.
arXiv Detail & Related papers (2020-05-30T02:19:08Z) - 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.