arXiv AI

Dual-Model Sentiment Analysis of Consumer Reviews in the Retail Coffee Sector Using Machine Learning and Deep Learning Approaches

arXiv:2608. 12007v1 Announce Type: cross Abstract: Consumer reviews play an important role in shaping brand perception and business strategies, particularly in service-driven industries such as retail coffee.

Hugging Face Trending Papers

Aug 12

Dual-Model Sentiment Analysis of Consumer Reviews in the Retail Coffee Sector Using Machine Learning and Deep Learning Approaches

Consumer reviews play an important role in shaping brand perception and business strategies, particularly in service-driven industries such as retail coffee. This study presents a comparative sentiment analysis framework for Starbucks customer reviews using classical machine learning and deep learning approaches.

nlp

arXiv AI

Jun 24

Evaluating LLM Usage for Efficient and Explainable Numerical and Classified Implicit Sentiment Analysis of Product Desirability

arXiv:2606. 23701v1 Announce Type: cross Abstract: Qualitative product feedback can reveal nuanced user experiences, but its implicit sentiment is difficult to measure.

By Sherri Weitl-Harms, John Hastings

llms nlp safety

arXiv Machine Learning

Jun 17

Predictive Analytics in E-Commerce for CustomerBehavior Forecasting using hybrid Ret-DNN withXGBoost Model

arXiv:2606. 17931v1 Announce Type: new Abstract: In recent years, electronic (E) commerce services have rapidly increased in the daily lives of people, which helpsthem to purchase products online.

By Degala Pushpa Sri, Mayank Atreya, Lakshmi. H, Navin Chhibber, Mukesh Soni

arXiv AI

Jun 9

Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management

arXiv:2606. 08314v1 Announce Type: new Abstract: The coffee supply chain is one of the most complex agri-food networks, marked by geographically dispersed production, multi-tier coordination, and high sensitivity to quality and freshness.

By Ger\c{c}ek Budak (Department of Industrial Engineering, Ankara Y{\i}ld{\i}r{\i}m Beyaz{\i}t University, Ke\c{c}i\"oren, Ankara 06010, T\"urkiye), Faraz Gholamzadeh Gharehgheshlaghi (Department of Industrial Engineering, Ankara Y{\i}ld{\i}r{\i}m Beyaz{\i}t University, Ke\c{c}i\"oren, Ankara 06010, T\"urkiye), Melika Barjesteh Vaezi (Department of Kinesiology and Sport Management, Texas Tech University, Lubbock, TX, United States), Ahmad Gholizadeh Lonbar (Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA)

multimodal benchmarks

arXiv Machine Learning

Jun 2

Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling

arXiv:2606. 02004v1 Announce Type: cross Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data.

By Vladimir Beskorovainyi

nlp fine-tuning

arXiv AI

Jun 9

Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles

arXiv:2606. 07582v1 Announce Type: cross Abstract: Customer churn prediction is essential across data-driven industries such as insurance, digital banking, eCommerce, and subscription platforms, where retaining existing customers is typically more cost-effective than acquiring new ones.

By Joyjit Roy, Samaresh Kumar Singh, Laxmi Shaw

llms benchmarks

arXiv Machine Learning

23h ago

Deep Learning for Cross-Border Electricity Price Forecasting: A Comparative Study

arXiv:2608. 17091v1 Announce Type: new Abstract: While publicly available electricity market data presents a valuable resource for forecasting research, the field lacks established benchmark datasets for standardized comparison.

By Hadeer Elashhab, Sai Srijan Papineni, Marvin Dorn, Veit Hagenmeyer, Benjamin Sch\"afer

llms benchmarks

arXiv AI

Jun 2

ChurnNet: A Optimized Modern AI for Churn Prediction

arXiv:2606. 00169v1 Announce Type: cross Abstract: Increased competition and the growing similarity of products and services offered by retailers have lowered the barriers for customers to switch to competitors.

By Syed Saad Saif, Giulio Maggiore, Paolo Russo, Damiano Distante

arXiv AI

Jun 30

Do We Still Need Fine Tuning? Turkish Sentiment Analysis in the Era of Large Language Model

arXiv:2606. 29614v1 Announce Type: cross Abstract: This study examines whether supervised fine-tuning remains necessary for Turkish sentiment analysis in the era of large language models.

By Sercan Karaka\c{s}, Yusuf \c{S}im\c{s}ek

llms nlp fine-tuning

arXiv AI

Jun 2

SentimentLens: Reconciling Sentiment and Ratings via Dual-Modality in the Hospitality Sector

arXiv:2606. 00084v1 Announce Type: cross Abstract: Online travel platforms generate vast volumes of user-generated hotel reviews, offering rich opportunities to understand traveler experiences at scale.

By Dineth Jayakody, Pasindu Thenahandi, Sampath Jayarathna

nlp multimodal

arXiv AI

Jul 9

Large Behavior Model: A Promptable Digital Twin of the Retail Customer

arXiv:2607. 06993v1 Announce Type: new Abstract: Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy without explaining decisions or simulate users without grounding them in real behavioral data.

By Wachiravit Modecrua, Krittin Pachtrachai, Touchapon Kraisingkorn

llms rag reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 15

So Many Opinions, So Many LLMs: Comparing Large Language Models to Traditional Machine Learning for Open- Ended Survey Analysis

arXiv:2607. 11890v1 Announce Type: cross Abstract: Open-ended surveys offer valuable insights, but they are notoriously difficult to analyze at scale.

By Abdullah Akinde, Mariam Akinde, Rasheedat Emiola, Ahmed Akinsola

llms nlp safety

Related stories