Exploring the Distribution Regularities of User Attention and Sentiment
toward Product Aspects in Online Reviews
- URL: http://arxiv.org/abs/2209.03690v1
- Date: Thu, 8 Sep 2022 10:23:16 GMT
- Title: Exploring the Distribution Regularities of User Attention and Sentiment
toward Product Aspects in Online Reviews
- Authors: Chenglei Qin, Chengzhi Zhang, Yi Bu
- Abstract summary: This paper explores the distribution regularities of user attention and sentiment toward product aspects from the temporal perspective of online reviews.
The empirical results show that a power-law distribution can fit user attention to product aspects, and the reviews posted in short time intervals contain more product aspects.
- Score: 7.23135508361981
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: [Purpose] To better understand the online reviews and help potential
consumers, businessmen, and product manufacturers effectively obtain users'
evaluation on product aspects, this paper explores the distribution
regularities of user attention and sentiment toward product aspects from the
temporal perspective of online reviews. [Design/methodology/approach] Temporal
characteristics of online reviews (purchase time, review time, and time
intervals between purchase time and review time), similar attributes
clustering, and attribute-level sentiment computing technologies are employed
based on more than 340k smartphone reviews of three products from JD.COM (a
famous online shopping platform in China) to explore the distribution
regularities of user attention and sentiment toward product aspects in this
article. [Findings] The empirical results show that a power-law distribution
can fit user attention to product aspects, and the reviews posted in short time
intervals contain more product aspects. Besides, the results show that the
values of user sentiment of product aspects are significantly higher/lower in
short time intervals which contribute to judging the advantages and weaknesses
of a product. [Research limitations] The paper can't acquire online reviews for
more products with temporal characteristics to verify the findings because of
the restriction on reviews crawling by the shopping platforms.
[Originality/value] This work reveals the distribution regularities of user
attention and sentiment toward product aspects, which is of great significance
in assisting decision-making, optimizing review presentation, and improving the
shopping experience.
Related papers
- Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs [57.16442740983528]
In ad-hoc retrieval, evaluation relies heavily on user actions, including implicit feedback.
The role of user feedback in annotators' assessment of turns in a conversational perception has been little studied.
We focus on how the evaluation of task-oriented dialogue systems ( TDSs) is affected by considering user feedback, explicit or implicit, as provided through the follow-up utterance of a turn being evaluated.
arXiv Detail & Related papers (2024-04-19T16:45:50Z) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews.
The newly emerging AI-generated literature reviews are also appraised.
This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - Exploiting Correlated Auxiliary Feedback in Parameterized Bandits [56.84649080789685]
We study a novel variant of the parameterized bandits problem in which the learner can observe additional auxiliary feedback that is correlated with the observed reward.
The auxiliary feedback is readily available in many real-life applications, e.g., an online platform that wants to recommend the best-rated services to its users can observe the user's rating of service (rewards) and collect additional information like service delivery time (auxiliary feedback)
arXiv Detail & Related papers (2023-11-05T17:27:06Z) - ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding [25.421723193835895]
Foreseer offers a novel framework for review forecasting by effectively integrating review text, product network, and temporal information.
We evaluate ForeSeer on a real-world product review system containing 11,536,382 reviews and 11,000 products over 3 years.
arXiv Detail & Related papers (2023-10-07T16:21:04Z) - Can Online Customer Reviews Help Design More Sustainable Products? A
Preliminary Study on Amazon Climate Pledge Friendly Products [0.0]
This paper investigates and analyzes Amazon product reviews to bring new light on the following question: What sustainable design insights can be identified or interpreted from online product reviews?''
The top 100 reviews, evenly distributed by star ratings, for three product categories are collected, manually annotated, analyzed and interpreted.
Between 12% and 20% of the reviews mentioned directly or indirectly aspects or attributes that could be exploited to improve the design of these products from a sustainability perspective.
arXiv Detail & Related papers (2021-12-20T08:57:37Z) - Characterization of Frequent Online Shoppers using Statistical Learning
with Sparsity [54.26540039514418]
This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity.
arXiv Detail & Related papers (2021-11-11T05:36:39Z) - Impact of review valence and perceived uncertainty on purchase of
time-constrained and discounted search goods [0.0]
This study investigates how purchase decisions for new products are affected by past customer aggregate ratings when a soon-to-expire discount is being offered.
Considering review credibility, diagnosticity, and effectiveness as determinants of consumer attitude in a time-constrained search and purchase environment, we follow the approach-avoidance conflict theory.
arXiv Detail & Related papers (2021-10-09T19:50:08Z) - 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) - User-Inspired Posterior Network for Recommendation Reason Generation [53.035224183349385]
Recommendation reason generation plays a vital role in attracting customers' attention as well as improving user experience.
We propose a user-inspired multi-source posterior transformer (MSPT), which induces the model reflecting the users' interests.
Experimental results show that our model is superior to traditional generative models.
arXiv Detail & Related papers (2021-02-16T02:08:52Z) - Improving Opinion Spam Detection by Cumulative Relative Frequency
Distribution [0.9176056742068814]
Various approaches have been proposed for detecting opinion spam in online reviews.
We re-engineered a set of effective features used for classifying opinion spam.
We show that the use of the distributional features is able to improve the performances of classifiers.
arXiv Detail & Related papers (2020-12-27T10:23:44Z) - Mining Changes in User Expectation Over Time From Online Reviews [0.7742297876120561]
We propose an approach for capturing changes of user expectation on product affordances based on the online reviews for two generations of products.
First, the approach uses a rule-based natural language processing method to automatically identify and structure product affordances from review text.
Finally, changes of user expectation can be found by applying the conjoint analysis on the online reviews posted for two successive generations of products.
arXiv Detail & Related papers (2020-01-13T12:57:06Z)
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.