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?– user4293Commented Nov 14, 2016 at 11:28
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like positive ,negative sentiment– Nurul Akter TowhidCommented Nov 15, 2016 at 11:30
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1 Answer
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
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 |
answered Aug 12, 2021 at 14:10
