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--- |
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dataset_info: |
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features: |
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- name: transcript |
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dtype: string |
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- name: sentiment |
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dtype: string |
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splits: |
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- name: test |
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num_bytes: 182442 |
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num_examples: 700 |
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download_size: 98661 |
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dataset_size: 182442 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
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license: mit |
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task_categories: |
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- text-generation |
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language: |
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- en |
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tags: |
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- finance |
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- financial sentiment |
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size_categories: |
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- n<1K |
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--- |
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# Aiera Financial Sentiment Analysis Dataset |
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## Description |
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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. |
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## Dataset Structure |
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### Columns |
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- `transcript`: A segment of the earnings call transcript. |
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- `sentiment`: The sentiment label for the transcript segment, with possible values being "positive", "negative", or "neutral". |
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### Data Format |
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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. |
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## Use Cases |
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This dataset is particularly suited for applications such as: |
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- Training machine learning models to perform sentiment analysis specifically in financial contexts. |
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- Developing algorithms to assist financial analysts and investors by providing quick sentiment assessments of earnings calls. |
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- Enhancing natural language processing systems used in finance for better understanding of market mood and company performance. |
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## Accessing the Dataset |
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To access this dataset, you can load it using the HuggingFace Datasets library with the following Python code: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("Aiera/aiera-transcript-sentiment") |
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``` |
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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). |
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