# E-commerce Review Analysis

This is a BERT-based sentiment analysis model for Indonesian language, capable of classifying text into positive or negative sentiments.

A lightweight sentence embedding model based on DistilBERT, suitable for various text processing tasks

This is a Japanese sentiment analysis model based on the BERT architecture, fine-tuned on the JGLUE MARC-ja dataset.

A sentiment analysis model fine-tuned on multiple Chinese-English sentiment analysis datasets based on Randeng-T5-784M-Chinese-Multi-Task-Model, supporting various sentiment analysis task output formats.

E5-base is a general-purpose text embedding model suitable for various natural language processing tasks such as classification, retrieval, clustering, and semantic similarity calculation.

A RoBERTa-based Indonesian sentiment text classification model, fine-tuned on the SmSA dataset with an accuracy of 94.36%

A sentiment analysis model fine-tuned based on the Turkish BERTurk model, used to determine the sentiment polarity (positive/negative) of Turkish texts

BERT model post-trained on Yelp restaurant review data, specializing in sentiment analysis and multi-dimensional understanding of e-commerce reviews