Advanced sentiment analysis in online shopping: Implementing LSTM models analyzing E-commerce user sentiments
This study addresses the accuracy challenges in e-commerce sentiment classification and thus provides valuable insight for businesses to enrich strategies toward the interpretation of customer feedback and improvement of product development. This article elaborately contrasts long short-term memory...
Saved in:
| Main Author: | Lu Liyuan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2025-07-01
|
| Series: | Nonlinear Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nleng-2025-0110 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Examining Customer Satisfaction Through Transformer-Based Sentiment Analysis for Improving Bilingual E-Commerce Experiences
by: Shizhong Shan, et al.
Published: (2025-01-01) -
Comparison of Deep Learning Sentiment Analysis Methods, Including LSTM and Machine Learning
by: Jean Max T. Habib, et al.
Published: (2023-11-01) -
Sentiment analysis of telecom official micro-blog users based on LSTM deep learning model
by: Xin CAI, et al.
Published: (2017-12-01) -
Sentiment Analysis and Trend Mapping of Hotel Reviews Using LSTM and GRU
by: Yerik Afrianto Singgalen
Published: (2024-12-01) -
Enhancing Sentiment and Emotion Classification with LSTM-Based Semi-Supervised Learning
by: Rochmat Husaini, et al.
Published: (2025-06-01)