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.
SemEval-2016 Task 5 contains 2 test sets with over 5000 reviews in total from digital camera and mobile phone area.
|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|
|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.
|NLP&CC 2012 Test||Weibo reviews||2||1908||10|
ChnSentiCorp: It contains 1021 documents in three domains: education, movie and house.
|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.
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.
JD Full: Chinese shopping reviews were evenly split for predicting full five stars.
|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.
|JD Binary||shopping reviews||2||4,000,000|
Overview paper in this area:
An incomplete list of new corpora (as of 2020):
|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|