| # DynaSent: Dynamic Sentiment Analysis Dataset | |
| DynaSent is an English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. This dataset card is forked from the original [DynaSent Repository](https://github.com/cgpotts/dynasent). | |
| ## Contents | |
| * [Citation](#Citation) | |
| * [Dataset files](#dataset-files) | |
| * [Quick start](#quick-start) | |
| * [Data format](#data-format) | |
| * [Models](#models) | |
| * [Other files](#other-files) | |
| * [License](#license) | |
| ## Citation | |
| [Christopher Potts](http://web.stanford.edu/~cgpotts/), [Zhengxuan Wu](http://zen-wu.social), Atticus Geiger, and [Douwe Kiela](https://douwekiela.github.io). 2020. [DynaSent: A dynamic benchmark for sentiment analysis](https://arxiv.org/abs/2012.15349). Ms., Stanford University and Facebook AI Research. | |
| ```stex | |
| @article{potts-etal-2020-dynasent, | |
| title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis}, | |
| author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus and Kiela, Douwe}, | |
| journal={arXiv preprint arXiv:2012.15349}, | |
| url={https://arxiv.org/abs/2012.15349}, | |
| year={2020}} | |
| ``` | |
| ## Dataset files | |
| The dataset is [dynasent-v1.1.zip](dynasent-v1.1.zip), which is included in this repository. `v1.1` differs from `v1` only in that `v1.1` has proper unique ids for Round 1 and corrects a bug that led to some non-unique ids in Round 2. There are no changes to the examples or other metadata. | |
| The dataset consists of two rounds, each with a train/dev/test split: | |
| ### Round 1: Naturally occurring sentences | |
| * `dynasent-v1.1-round01-yelp-train.jsonl` | |
| * `dynasent-v1.1-round01-yelp-dev.jsonl` | |
| * `dynasent-v1.1-round01-yelp-test.jsonl` | |
| ### Round 1: Sentences crowdsourced using Dynabench | |
| * `dynasent-v1.1-round02-dynabench-train.jsonl` | |
| * `dynasent-v1.1-round02-dynabench-dev.jsonl` | |
| * `dynasent-v1.1-round02-dynabench-test.jsonl` | |
| ### SST-dev revalidation | |
| The dataset also contains a version of the [Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/) dev set in our format with labels from our validation task: | |
| * `sst-dev-validated.jsonl` | |
| ## Quick start | |
| This function can be used to load any subset of the files: | |
| ```python | |
| import json | |
| def load_dataset(*src_filenames, labels=None): | |
| data = [] | |
| for filename in src_filenames: | |
| with open(filename) as f: | |
| for line in f: | |
| d = json.loads(line) | |
| if labels is None or d['gold_label'] in labels: | |
| data.append(d) | |
| return data | |
| ``` | |
| For example, to create a Round 1 train set restricting to examples with ternary gold labels: | |
| ```python | |
| import os | |
| r1_train_filename = os.path.join('dynasent-v1.1', 'dynasent-v1.1-round01-yelp-train.jsonl') | |
| ternary_labels = ('positive', 'negative', 'neutral') | |
| r1_train = load_dataset(r1_train_filename, labels=ternary_labels) | |
| X_train, y_train = zip(*[(d['sentence'], d['gold_label']) for d in r1_train]) | |
| ``` | |
| ## Data format | |
| ### Round 1 format | |
| ```python | |
| {'hit_ids': ['y5238'], | |
| 'sentence': 'Roto-Rooter is always good when you need someone right away.', | |
| 'indices_into_review_text': [0, 60], | |
| 'model_0_label': 'positive', | |
| 'model_0_probs': {'negative': 0.01173639390617609, | |
| 'positive': 0.7473671436309814, | |
| 'neutral': 0.24089649319648743}, | |
| 'text_id': 'r1-0000001', | |
| 'review_id': 'IDHkeGo-nxhqX4Exkdr08A', | |
| 'review_rating': 1, | |
| 'label_distribution': {'positive': ['w130', 'w186', 'w207', 'w264', 'w54'], | |
| 'negative': [], | |
| 'neutral': [], | |
| 'mixed': []}, | |
| 'gold_label': 'positive'} | |
| ``` | |
| Details: | |
| * `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset. | |
| * `'sentence'`: The example text. | |
| * `'indices_into_review_text':` indices of `'sentence'` into the original review in the [Yelp Academic Dataset](https://www.yelp.com/dataset). | |
| * `'model_0_label'`: prediction of Model 0 as described in the paper. The possible values are `'positive'`, `'negative'`, and `'neutral'`. | |
| * `'model_0_probs'`: probability distribution predicted by Model 0. The keys are `('positive', 'negative', 'neutral')` and the values are floats. | |
| * `'text_id'`: unique identifier for this entry. | |
| * `'review_id'`: review-level identifier for the review from the [Yelp Academic Dataset](https://www.yelp.com/dataset) containing `'sentence'`. | |
| * `'review_rating'`: review-level star-rating for the review containing `'sentence'` in the [Yelp Academic Dataset](https://www.yelp.com/dataset). The possible values are `1`, `2`, `3`, `4`, and `5`. | |
| * `'label_distribution':` response distribution from the MTurk validation task. The keys are `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset. | |
| * `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`. | |
| Here is some code one could use to augment a dataset, as loaded by `load_dataset`, with a field giving the full review text from the [Yelp Academic Dataset](https://www.yelp.com/dataset): | |
| ```python | |
| import json | |
| def index_yelp_reviews(yelp_src_filename='yelp_academic_dataset_review.json'): | |
| index = {} | |
| with open(yelp_src_filename) as f: | |
| for line in f: | |
| d = json.loads(line) | |
| index[d['review_id']] = d['text'] | |
| return index | |
| yelp_index = index_yelp_reviews() | |
| def add_review_text_round1(dataset, yelp_index): | |
| for d in dataset: | |
| review_text = yelp_index[d['text_id']] | |
| # Check that we can find the sentence as expected: | |
| start, end = d['indices_into_review_text'] | |
| assert review_text[start: end] == d['sentence'] | |
| d['review_text'] = review_text | |
| return dataset | |
| ``` | |
| ### Round 2 format | |
| ```python | |
| {'hit_ids': ['y22661'], | |
| 'sentence': "We enjoyed our first and last meal in Toronto at Bombay Palace, and I can't think of a better way to book our journey.", | |
| 'sentence_author': 'w250', | |
| 'has_prompt': True, | |
| 'prompt_data': {'indices_into_review_text': [2093, 2213], | |
| 'review_rating': 5, | |
| 'prompt_sentence': "Our first and last meals in Toronto were enjoyed at Bombay Palace and I can't think of a better way to bookend our trip.", | |
| 'review_id': 'Krm4kSIb06BDHternF4_pA'}, | |
| 'model_1_label': 'positive', | |
| 'model_1_probs': {'negative': 0.29140257835388184, | |
| 'positive': 0.6788994669914246, | |
| 'neutral': 0.029697999358177185}, | |
| 'text_id': 'r2-0000001', | |
| 'label_distribution': {'positive': ['w43', 'w26', 'w155', 'w23'], | |
| 'negative': [], | |
| 'neutral': [], | |
| 'mixed': ['w174']}, | |
| 'gold_label': 'positive'} | |
| ``` | |
| Details: | |
| * `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset. | |
| * `'sentence'`: The example text. | |
| * `'sentence_author'`: Anonymized MTurk id of the worker who wrote `'sentence'`. These are from the same family of ids as used in `'label_distribution'`, but this id is never one of the ids in `'label_distribution'` for this example. | |
| * `'has_prompt'`: `True` if the `'sentence'` was written with a Prompt else `False`. | |
| * `'prompt_data'`: None if `'has_prompt'` is False, else: | |
| * `'indices_into_review_text'`: indices of `'prompt_sentence'` into the original review in the [Yelp Academic Dataset](https://www.yelp.com/dataset). | |
| * `'review_rating'`: review-level star-rating for the review containing `'sentence'` in the [Yelp Academic Dataset](https://www.yelp.com/dataset). | |
| * `'prompt_sentence'`: The prompt text. | |
| * `'review_id'`: review-level identifier for the review from the [Yelp Academic Dataset](https://www.yelp.com/dataset) containing `'prompt_sentence'`. | |
| * `'model_1_label'`: prediction of Model 1 as described in the paper. The possible values are `'positive'`, `'negative'`, and '`neutral'`. | |
| * `'model_1_probs'`: probability distribution predicted by Model 1. The keys are `('positive', 'negative', 'neutral')` and the values are floats. | |
| * `'text_id'`: unique identifier for this entry. | |
| * `'label_distribution'`: response distribution from the MTurk validation task. The keys are `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset. | |
| * `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`. | |
| To add the review texts to the `'prompt_data'` field, one can extend the code above for Round 1 with the following function: | |
| ```python | |
| def add_review_text_round2(dataset, yelp_index): | |
| for d in dataset: | |
| if d['has_prompt']: | |
| prompt_data = d['prompt_data'] | |
| review_text = yelp_index[prompt_data['review_id']] | |
| # Check that we can find the sentence as expected: | |
| start, end = prompt_data['indices_into_review_text'] | |
| assert review_text[start: end] == prompt_data['prompt_sentence'] | |
| prompt_data['review_text'] = review_text | |
| return dataset | |
| ``` | |
| ### SST-dev format | |
| ```python | |
| {'hit_ids': ['s20533'], | |
| 'sentence': '-LRB- A -RRB- n utterly charming and hilarious film that reminded me of the best of the Disney comedies from the 60s.', | |
| 'tree': '(4 (2 (1 -LRB-) (2 (2 A) (3 -RRB-))) (4 (4 (2 n) (4 (3 (2 utterly) (4 (3 (4 charming) (2 and)) (4 hilarious))) (3 (2 film) (3 (2 that) (4 (4 (2 (2 reminded) (3 me)) (4 (2 of) (4 (4 (2 the) (4 best)) (2 (2 of) (3 (2 the) (3 (3 Disney) (2 comedies))))))) (2 (2 from) (2 (2 the) (2 60s)))))))) (2 .)))', | |
| 'text_id': 'sst-dev-validate-0000437', | |
| 'sst_label': '4', | |
| 'label_distribution': {'positive': ['w207', 'w3', 'w840', 'w135', 'w26'], | |
| 'negative': [], | |
| 'neutral': [], | |
| 'mixed': []}, | |
| 'gold_label': 'positive'} | |
| ``` | |
| Details: | |
| * `'hit_ids'`: List of Amazon Mechanical Turk Human Interface Tasks (HITs) in which this example appeared during validation. The values are anonymized but used consistently throughout the dataset. | |
| * `'sentence'`: The example text. | |
| * `'tree'`: The parsetree for the example as given in the SST distribution. | |
| * `'text_id'`: A new identifier for this example. | |
| * `'sst_label'`: The root-node label from the SST. Possible values `'0'`, `'1'` `'2'`, `'3'`, and `'4'`. | |
| * `'label_distribution':` response distribution from the MTurk validation task. The keys are `('positive', 'negative', 'neutral')` and the values are lists of anonymized MTurk ids, which are used consistently throughout the dataset. | |
| * `'gold_label'`: the label chosen by at least three of the five workers if there is one (possible values: `'positive'`, `'negative'`, '`neutral'`, and `'mixed'`), else `None`. | |
| ## Models | |
| Model 0 and Model 1 from the paper are available here: | |
| https://drive.google.com/drive/folders/1dpKrjNJfAILUQcJPAFc5YOXUT51VEjKQ?usp=sharing | |
| This repository includes a Python module `dynasent_models.py` that provides a [Hugging Face](https://huggingface.co.)-based wrapper around these ([PyTorch](https://pytorch.org)) models. Simple examples: | |
| ```python | |
| import os | |
| from dynasent_models import DynaSentModel | |
| # `dynasent_model0` should be downloaded from the above Google Drive link and | |
| # placed in the `models` directory. `dynasent_model1` works the same way. | |
| model = DynaSentModel(os.path.join('models', 'dynasent_model0.bin')) | |
| examples = [ | |
| "superb", | |
| "They said the experience would be amazing, and they were right!", | |
| "They said the experience would be amazing, and they were wrong!"] | |
| model.predict(examples) | |
| ``` | |
| This should return the list `['positive', 'positive', 'negative']`. | |
| The `predict_proba` method provides access to the predicted distribution over the class labels; see the demo at the bottom of `dynasent_models.py` for details. | |
| The following code uses `load_dataset` from above to reproduce the Round 2 dev-set report on Model 0 from the paper: | |
| ```python | |
| import os | |
| from sklearn.metrics import classification_report | |
| from dynasent_models import DynaSentModel | |
| dev_filename = os.path.join('dynasent-v1.1', 'dynasent-v1.1-round02-dynabench-dev.jsonl') | |
| dev = load_dataset(dev_filename) | |
| X_dev, y_dev = zip(*[(d['sentence'], d['gold_label']) for d in dev]) | |
| model = DynaSentModel(os.path.join('models', 'dynasent_model0.bin')) | |
| preds = model.predict(X_dev) | |
| print(classification_report(y_dev, preds, digits=3)) | |
| ``` | |
| For a fuller report on these models, see our paper and [our model card](dynasent_modelcard.md). | |
| ## Other files | |
| ### Analysis notebooks | |
| The following notebooks reproduce the dataset statistics, figures, and random example selections from the paper: | |
| * `analyses_comparative.ipynb` | |
| * `analysis_round1.ipynb` | |
| * `analysis_round2.ipynb` | |
| * `analysis_sst_dev_revalidate.ipynb` | |
| The Python module `dynasent_utils.py` contains functions that support those notebooks, and `dynasent.mplstyle` helps with styling the plots. | |
| ### Datasheet | |
| The [Datasheet](https://arxiv.org/abs/1803.09010) for our dataset: | |
| * [dynasent_datasheet.md](dynasent_datasheet.md) | |
| ### Model Card | |
| The [Model Card](https://arxiv.org/pdf/1810.03993.pdf) for our models: | |
| * [dynasent_modelcard.md](dynasent_modelcard.md) | |
| ### Tests | |
| The module `test_dataset.py` contains PyTest tests for the dataset. To use it, run | |
| ``` | |
| py.test -vv test_dataset.py | |
| ``` | |
| in the root directory of this repository. | |
| ### Validation HIT code | |
| The file `validation-hit-contents.html` contains the HTML/Javascript used in the validation task. It could be used directly on Amazon Mechanical Turk, by simply pasting its contents into the usual HIT creation window. | |
| ## License | |
| DynaSent has a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). |