fiqa-sentiment-classification

# 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")
```

# 数据集名称

## 数据集描述
本数据集基于野生金融情感分析（Financial Sentiment Analysis in the Wild, FiQA）挑战赛的任务1，遵循论文《面向金融微博客与新闻的基于方面情感分析的基线方法》中所述的相同设置。数据集被划分为训练集、验证集、测试集三个子集，样本量分别为822、117和234。

## 数据集结构
- `_id`：数据点的唯一标识符
- `sentence`：目标语句
- `target`：情感指向对象
- `aspect`：情感方面
- `score`：情感得分
- `type`：数据点类型（标题或帖子）

## 附加信息
- 项目主页：[FiQA挑战赛](https://sites.google.com/view/fiqa/home)
- 引用文献：[面向金融微博客与新闻的基于方面情感分析的基线方法](https://arxiv.org/pdf/2211.00083.pdf)

## CSV下载方法
python
from datasets import load_dataset

# 从数据集枢纽加载本数据集
dataset = load_dataset("ChanceFocus/fiqa-sentiment-classification")

# 将训练集保存为CSV文件
dataset["train"].to_csv("train.csv")
# 将验证集保存为CSV文件
dataset["valid"].to_csv("valid.csv")
# 将测试集保存为CSV文件
dataset["test"].to_csv("test.csv")