Aiera/aiera-transcript-sentiment

---
dataset_info:
features:
- name: transcript
dtype: string
- name: sentiment
dtype: string
splits:
- name: test
num_bytes: 182442
num_examples: 700
download_size: 98661
dataset_size: 182442
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
license: mit
task_categories:
- text-generation
language:
- en
tags:
- finance
- financial sentiment
size_categories:
- n<1K
---

# Aiera Financial Sentiment Analysis Dataset

## Description

This dataset focuses on the sentiment analysis of earnings call transcript segments. It provides pre-segmented extracts from earnings calls, transcribed by Aiera, paired with sentiment labels. Each segment in the `transcript` column is annotated with a sentiment label (`sentiment`), which can be "positive", "negative", or "neutral". This dataset is intended for training and evaluating models on their ability to discern the underlying sentiment in financial communications.

## Dataset Structure

### Columns

- `transcript`: A segment of the earnings call transcript.
- `sentiment`: The sentiment label for the transcript segment, with possible values being "positive", "negative", or "neutral".

### Data Format

The dataset is structured in a tabular format, with each row representing a unique segment of an earnings call transcript alongside its corresponding sentiment label.

## Use Cases

This dataset is particularly suited for applications such as:
- Training machine learning models to perform sentiment analysis specifically in financial contexts.
- Developing algorithms to assist financial analysts and investors by providing quick sentiment assessments of earnings calls.
- Enhancing natural language processing systems used in finance for better understanding of market mood and company performance.

## Accessing the Dataset

To access this dataset, you can load it using the HuggingFace Datasets library with the following Python code:

```python
from datasets import load_dataset

dataset = load_dataset("Aiera/aiera-transcript-sentiment")
```

A guide for evaluating using EleutherAI's [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) is available on [github](https://github.com/aiera-inc/aiera-benchmark-tasks).