I am doing my research on sentiment analysis for Standard Mandarin. I don't have any benchmark dataset. I need usable labeled dataset for my research.

  • No limitations as to the kind of sentiments?
    – user4293
    Commented Nov 14, 2016 at 11:28
  • like positive ,negative sentiment Commented Nov 15, 2016 at 11:30

1 Answer 1


See https://github.com/didi/ChineseNLP/blob/master/docs/sentiment_analysis.md:

SemEval-2016 Task 5 contains 2 test sets with over 5000 reviews in total from digital camera and mobile phone area.

Source Genre # Classes Size(sentences) Size(words)
SemEval 2016 Task 5 – CAM Test Digital Camera reviews (Chinese) 3 2256 ~25k
SemEval 2016 Task 5 – PHNS Test Mobile Phone reviews (Chinese) 3 3191 ~34k
Source Genre # Classes Size(sentences) Size(words)
SemEval 2016 Task 5 – CAM Train Digital Camera reviews (Chinese) 3 5784 ~61k
SemEval 2016 Task 5 – PHNS Train Mobile Phone reviews (Chinese) 3 6330 ~62k

NLP&CC 2012 Test: Chinese Weibo sentiment analysis evaluation data.

Source Genre # Classes Size(sentences) Topics
NLP&CC 2012 Test Weibo reviews 2 1908 10

ChnSentiCorp: It contains 1021 documents in three domains: education, movie and house.

Source Genre # Classes Size(sentences) Size(words)
ChnSentiCorp Test Hotel reviews(Chinese) 2 1999 ~725k

IT168TEST: A product review dataset presented by Zagibalov and Carroll. This dataset contains over 20000 reviews, in which 78% were manually labeled as positive and 22% labeled as negative.

Source Genre # Classes Size(sentences)
IT168Test Product review 2 29531

Dianping: Chinese restaurant reviews were evenly split as follows: 4 and 5 star reviews were assigned to the positive class while 1-3 star reviews were in the negative class.

Source Genre # Classes Size(sentences)
Dianping restaurant reviews 2 2,000,000

JD Full: Chinese shopping reviews were evenly split for predicting full five stars.

Source Genre # Classes Size(sentences)
JD Full shopping reviews 5 3,000,000

JD Binary: Chinese shopping reviews are evenly split into positive (4-and-5 star reviews)and negative (1-and-2 star reviews) sentiments, ignoring 3-star reviews.

Source Genre # Classes Size(sentences)
JD Binary shopping reviews 2 4,000,000

Other Resources

Name Description Domain/ Source Size (positive/ negative where applicable) Accuracy F1 Link
Chinese Sarcasm Dataset Text manually labelled as sarcastic or not news 2500 / 90 000 0.7611 0.7368 Gong et al., 2020
CH-SIMS Individually labelled multi-modal (text, video, audio) movies, TV shows 2281 video segments - 0.827 Yu et al., 2020
FiTSA Aspect-based sentiment analysis for financial news news 8314 sentences, 647 000 characters - 0.798 Yuan et al., 2020
MPDD Emotion in multi-party dialogs TV shows 25 500 utterances 0.595 - Cheng et al., 2020
MIMN Multimodal (text, image) and aspect-based analysis zol.com (shopping site) 5200 reviews 0.616 0.605 github

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.