Bayesian game model based unsupervised sentiment analysis of product reviews

Punetha, Neha; Jain, Goonjan

doi:10.1016/j.eswa.2022.119128

Cited by 19 publications

(8 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Price perception, derived from the customer value perception theory, assesses the effectiveness of knowledge payment products by considering the interest of potential users on the platform and the content of reviews. Customer acceptance of a product's effectiveness is analyzed through review sentiment analysis (Punetha & Jain, 2023). The product category serves as a label for statistical purposes, tracking the number of interested individuals on the platform.…”

Section: Methodsmentioning

confidence: 99%

Dynamic pricing strategies for multi‐type knowledge payment products: A system dynamics approach

Guo,

Bian,

et al. 2023

Manage Decis Econ

View full text Add to dashboard Cite

With the emergence of knowledge economy, knowledge payment has become an essential means of knowledge circulation. This study employs a system dynamics approach and integrates signal theory and customer value perception to investigate the dynamic pricing strategies for various knowledge payment products from the perspective of knowledge producers. First, this study analyzes the categories of knowledge payment products and proposes a theoretical research framework for designing dynamic pricing strategies for different products categories. Second, this study identifies the key factors that impact the profitability of knowledge payment platforms and construct a system dynamics model of product pricing strategies for knowledge payment platforms; next, three typical knowledge payment products on Zhihu Live are chosen as research objects, and system dynamics equations are constructed to simulate the profitability changes of different types of products in different periods. Finally, the results of simulation and sensitivity analysis are combined to select dynamic pricing strategies for different products types. This study offers an innovative exploration of multi‐stage pricing strategies for different types of knowledge payment products, which fills the gap in existing research and provides more scientific suggestions for knowledge producers and consumers in terms of knowledge transactions.

show abstract

Section: Methodsmentioning

confidence: 99%

Dynamic pricing strategies for multi‐type knowledge payment products: A system dynamics approach

Guo,

Bian,

et al. 2023

Manage Decis Econ

View full text Add to dashboard Cite

show abstract

“…Notably, In Li et al [13], researchers combined Word2vec, Bi-GRU, and Attention methods to construct a sentiment analysis model for online store reviews, scrutinizing over 130,000 reviews from DianPing. Punetha and Jain [14] developed an innovative framework using unsupervised learning for sentiment analysis of TripAdvisor and Yelp restaurant reviews. In Li et al [15], researchers employed a Conditional Survival Forest (CSF) model in machine learning to categorize the overall sentiment of online reviews.…”

Section: Sentiment Analysis In Restaurant Reviewsmentioning

confidence: 99%

Opinion distribution: spatial sentiment analysis of online restaurant reviews through BERT model and GIS

Niu,

Xing

2024

Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024)

View full text Add to dashboard Cite

As the impact of online reviews on consumer decision-making grows more pronounced, this study is dedicated to identifying service-related issues by analyzing online restaurant customer reviews. We utilized user reviews from the "Beijing Must-Eat List" on DianPing as the data source, employing artificial intelligence alongside spatial geographic analysis methods. Reviews were categorized for sentiment using the Bidirectional Encoder Representations from Transformers (BERT) model, with word clouds created for a visual display. The study also integrates hotspot estimation and kernel density estimation from spatial geographic analysis to delve into the geographic characteristics of sentiments in reviews. The model's effectiveness was assessed using metrics such as Precision, Recall, and F-Measure. Results indicated that our model excelled, demonstrating a precision of 98.73%, recall rate of 91.06%, and an F-Measure of 94.74%. This research offers insightful contributions towards a more nuanced understanding of consumer preferences and enhancing the marketing strategies in the food and beverage sector.

show abstract

“…The lexicon-based approaches do not need information labeling but depend upon the domain, need powerful linguistic resources, and have low precision [36,37]. Various studies used MCDM and game theoretic models to propose recommender systems [42,80]. But till now, none of the studies employ the application of a mathematical optimization model to perform sentiment analysis of reviews.…”

Section: Research Gapmentioning

confidence: 99%

Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews

Punetha

Jain

2023

Appl Intell

Self Cite

View full text Add to dashboard Cite

Sentiment Analysis is a method to identify, extract, and quantify people's feelings, opinions, or attitudes. The wealth of online data motivates organizations to keep tabs on customers' opinions and feelings by turning to sentiment analysis tasks. Along with the sentiment analysis, the emotion analysis of written reviews is also essential to improve customer satisfaction with restaurant service. Due to the availability of massive online data, various computerized methods are proposed in the literature to decipher text sentiments. The majority of current methods rely on machine learning, which necessitates the pre-training of large datasets and incurs substantial space and time complexity. To address this issue, we propose a novel unsupervised sentiment classification model. This study presents an unsupervised mathematical optimization framework to perform sentiment and emotion analysis of reviews. The proposed model performs two tasks. First, it identifies a review's positive and negative sentiment polarities, and second, it determines customer satisfaction as either satisfactory or unsatisfactory based on a review. The framework consists of two stages. In the first stage, each review's context, rating, and emotion scores are combined to generate performance scores. In the second stage, we apply a non-cooperative game on performance scores and achieve Nash Equilibrium. The output from this step is the deduced sentiment of the review and the customer's satisfaction feedback. The experiments were performed on two restaurant review datasets and achieved state-of-the-art results. We validated and established the significance of the results through statistical analysis. The proposed model is domain and language-independent. The proposed model ensures rational and consistent results.

show abstract

Bayesian game model based unsupervised sentiment analysis of product reviews

Cited by 19 publications

References 74 publications

Dynamic pricing strategies for multi‐type knowledge payment products: A system dynamics approach

Dynamic pricing strategies for multi‐type knowledge payment products: A system dynamics approach

Opinion distribution: spatial sentiment analysis of online restaurant reviews through BERT model and GIS

Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews

Contact Info

Product

Resources

About