# An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding

arXiv: https://arxiv.org/abs/1511.01665 · HTML: https://mlsanity.com/paper/1511.01665/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2015.

## Built on by (most cited first, 2 of 2)

- [Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark](https://mlsanity.com/paper/2306.07902/) (2023)
- [Assessment of Massively Multilingual Sentiment Classifiers](https://mlsanity.com/paper/2204.04937/) (2022)

## Abstract (arXiv)

In this article, how word embeddings can be used as features in Chinese sentiment classification is presented. Firstly, a Chinese opinion corpus is built with a million comments from hotel review websites. Then the word embeddings which represent each comment are used as input in different machine learning methods for sentiment classification, including SVM, Logistic Regression, Convolutional Neural Network (CNN) and ensemble methods. These methods get better performance compared with N-gram models using Naive Bayes (NB) and Maximum Entropy (ME). Finally, a combination of machine learning methods is proposed which presents an outstanding performance in precision, recall and F1 score. After selecting the most useful methods to construct the combinational model and testing over the corpus, the final F1 score is 0.920.