README.md

metadata

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

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

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")