# Dataset Name

## Dataset Description

This dataset is based on the task 1 of the Financial Sentiment Analysis in the Wild (FiQA) challenge. It follows the same settings as described in the paper 'A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News'. The dataset is split into three subsets: train, valid, test with sizes 822, 117, 234 respectively.

## Dataset Structure

- `_id`: ID of the data point
- `sentence`: The sentence
- `target`: The target of the sentiment
- `aspect`: The aspect of the sentiment
- `score`: The sentiment score
- `type`: The type of the data point (headline or post)

## Additional Information

- Homepage: [FiQA Challenge](https://sites.google.com/view/fiqa/home)
- Citation: [A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News](https://arxiv.org/pdf/2211.00083.pdf)

## Downloading CSV
```python
from datasets import load_dataset

# Load the dataset from the hub
dataset = load_dataset("ChanceFocus/fiqa-sentiment-classification")

# Save the dataset to a CSV file
dataset["train"].to_csv("train.csv")
dataset["valid"].to_csv("valid.csv")
dataset["test"].to_csv("test.csv")
```

language:en license:mit arxiv:2211.00083 region:us
{"language": "en", "license": "mit", "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "target", "dtype": "string"}, {"name": "aspect", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 119567, "num_examples": 822}, {"name": "valid", "num_bytes": 17184, "num_examples": 117}, {"name": "test", "num_bytes": 33728, "num_examples": 234}], "download_size": 102225, "dataset_size": 170479}}
TAGS
#language-English #license-mit #arxiv-2211.00083 #region-us 

# Dataset Name

## Dataset Description

This dataset is based on the task 1 of the Financial Sentiment Analysis in the Wild (FiQA) challenge. It follows the same settings as described in the paper 'A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News'. The dataset is split into three subsets: train, valid, test with sizes 822, 117, 234 respectively.

## Dataset Structure

- '_id': ID of the data point
- 'sentence': The sentence
- 'target': The target of the sentiment
- 'aspect': The aspect of the sentiment
- 'score': The sentiment score
- 'type': The type of the data point (headline or post)

## Additional Information

- Homepage: FiQA Challenge
- Citation: A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News

## Downloading CSV


# Dataset Name ## Dataset Description

This dataset is based on the task 1 of the Financial Sentiment Analysis in the Wild (FiQA) challenge. It follows the same settings as described in the paper 'A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News'. The dataset is split into three subsets: train, valid, test with sizes 822, 117, 234 respectively. ## Dataset Structure

- '_id': ID of the data point
- 'sentence': The sentence
- 'target': The target of the sentiment
- 'aspect': The aspect of the sentiment
- 'score': The sentiment score
- 'type': The type of the data point (headline or post) ## Additional Information

- Homepage: FiQA Challenge
- Citation: A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News ## Downloading CSV
TAGS
#language-English #license-mit #arxiv-2211.00083 #region-us 
 # Dataset Name ## Dataset Description

This dataset is based on the task 1 of the Financial Sentiment Analysis in the Wild (FiQA) challenge. It follows the same settings as described in the paper 'A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News'. The dataset is split into three subsets: train, valid, test with sizes 822, 117, 234 respectively. ## Dataset Structure

- '_id': ID of the data point
- 'sentence': The sentence
- 'target': The target of the sentiment
- 'aspect': The aspect of the sentiment
- 'score': The sentiment score
- 'type': The type of the data point (headline or post) ## Additional Information

- Homepage: FiQA Challenge
- Citation: A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News ## Downloading CSV
passage: TAGS
#language-English #license-mit #arxiv-2211.00083 #region-us 
# Dataset Name## Dataset Description

This dataset is based on the task 1 of the Financial Sentiment Analysis in the Wild (FiQA) challenge. It follows the same settings as described in the paper 'A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News'. The dataset is split into three subsets: train, valid, test with sizes 822, 117, 234 respectively.## Dataset Structure

- '_id': ID of the data point
- 'sentence': The sentence
- 'target': The target of the sentiment
- 'aspect': The aspect of the sentiment
- 'score': The sentiment score
- 'type': The type of the data point (headline or post)## Additional Information

- Homepage: FiQA Challenge
- Citation: A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News## Downloading CSV