An easy guide to Chinese Sentiment analysis with hotel review data


For source code and dataset used in this tutorial, check out my github repo.


Python 3.5, numpy, pickle, keras, tensorflow, jieba

About the data

Customer hotel reviews, including

2916 positive reviews and 3000 negative reviews

Optional for plotting

pylab, scipy

Key difference compared to English dataset

File Encoding

Some data files contain abnormal encoding characters which encoding GB2312 will complain about. Solution: read as bytes then decode as GB2312 line by line, skip lines with abnormal encodings. We also convert any traditional Chinese characters to simplified Chinese characters.

documents = []
for filename in positiveFiles:
    text = ""
    with, "rb") as doc_file:
        for line in doc_file:
                line = line.decode("GB2312")
            text = text.replace("\n", "")
            text = text.replace("\r", "")
    documents.append((text, "pos"))

Convert from traditional to simplified Chinese (繁体转简体)

Have those two files download from

those two lines below will convert string "line" from traditional to simplified Chinese.

from langconv import *


Use jieba to tokenize chinese sentences, then join the list of tokens seperated by spaces.

We then feed the string to Keras Tokenizer which expect each sentence with words tokens seperated by spaces.

from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
import jieba
seg_list = jieba.cut(text, cut_all=False)
text = " ".join(seg_list)
# totalX = [text , .....]
# maxLength is the sentence words length to keep
input_tokenizer = Tokenizer(30000)
input_vocab_size = len(input_tokenizer.word_index) + 1
totalX = np.array(pad_sequences(input_tokenizer.texts_to_sequences(totalX), maxlen=maxLength))

Chinese stop words

First get a list of stop words from the file chinese_stop_words.txt , then check each tokenized Chinese words against this list

stopwords = [ line.rstrip() for line in open('./data/chinese_stop_words.txt',"r", encoding="utf-8") ]
for doc in documents:
    seg_list = jieba.cut(doc[0], cut_all=False)
    final =[]
    seg_list = list(seg_list)
    for seg in seg_list:
        if seg not in stopwords:


Keras trained for 20 epochs, takes 7 minutes 14 seconds with GPU (GTX 1070)



Try some new comments


For the Python Jupyter notebook source code and dataset, check out my github repo.

For an updated word-level English model, check out my other blog: Simple Stock Sentiment Analysis with news data in Keras.

Current rating: 3.2