Datasets:
language:
- en
tags:
- text-classification
- sentiment-analysis
task_categories:
- text-classification
configs:
- config_name: quality
data_files:
- split: train
path:
- quality/train.csv.gz
- split: test
path:
- quality/test.csv.gz
- config_name: readability
data_files:
- split: train
path:
- readability/train.csv.gz
- split: test
path:
- readability/test.csv.gz
- config_name: sentiment
data_files:
- split: train
path:
- sentiment/train.csv.gz
- split: test
path:
- sentiment/test.csv.gz
Text statistics
This dataset is a combination of the following datasets:
The main purpose is to collect the large data into one place for easy training and evaluation.
Data Preparation and Transformation
Quality Score Normalization
The dataset was enhanced with additional columns, and quality scores (n = 909 533) were normalized using Ordered Quantile normalization through the bestNormalize package in R. This transformation mapped original values to a standardized normal distribution, resulting in a new variable, transformed_quality, included in both training and test datasets to enhance statistical modeling capabilities.
Readability Score Calculation
U.S. reading grade levels were transformed using the Box-Cox method (bestNormalize package) with λ = 0.8766912. A custom function standardized the results and inverted the scale to generate 'readability' scores, where higher values indicate easier readability.
The standardized Box-Cox transformation was applied to 919 663 non-missing observations, yielding the following statistics:
- λ (lambda) = 0.8766912
- Mean (before standardization) = 7.908629
- Standard deviation (before standardization) = 3.339119
These transformations improved the dataset's suitability for subsequent statistical analyses.
Dataset size
The full datasets were shuffled and randomly split into train and test splits.
| Dataset | Train split | Test split |
|---|---|---|
| quality | 809 533 | 100 000 |
| readability | 869 663 | 50 000 |
| sentiment | 128 690 | 10 000 |