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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.

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  • No limitations as to the kind of sentiments?
    – user4293
    Nov 14 '16 at 11:28
  • like positive ,negative sentiment Nov 15 '16 at 11:30
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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

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