Datasets:

lewoniewski
/

wikipedia-sentiment

license: apache-2.0
language:
  - en
tags:
  - sentiment
  - wikipedia
  - vader
  - textblob
  - distilbert
  - roberta
  - cvader
size_categories:
  - 1M<n<10M

This dataset provides sentiment annotations for English Wikipedia articles using five different sentiment analysis models, as described in the study “Cross-Topic Sentiment Analysis of Wikipedia Articles: A Comparative Study of AI Models” (see also the supplementary materials for the study).

Dataset Structure

Each record corresponds to a single Wikipedia article and contains sentiment labels produced by multiple models.

Field	Description
`page_id`	Unique identifier of the Wikipedia article
`title`	Title of the Wikipedia article
`model1`	Sentiment label from cVADER (lexicon-based, compound score variant)
`model2`	Sentiment label from DistilBERT (transformer-based)
`model3`	Sentiment label from RoBERTa (transformer-based)
`model4`	Sentiment label from TextBlob (lexicon-based)
`model5`	Sentiment label from VADER (lexicon-based)

Each model assigns one of the following labels:

pos – positive
neu – neutral
neg – negative

Data Collection and Processing

Source Data

English Wikipedia dump (March 2025)
Only main namespace articles (no redirects)

Preprocessing

Articles were cleaned and extracted using WikiExtractor
Long articles were split into smaller text segments due to model input limits

Sentiment Inference

Each segment was analyzed independently by all models
Transformer models output class probabilities
Lexicon-based models output polarity scores mapped to classes

Aggregation Method

Similarly to our previous work, we used the advanced aggregation method to obtain the overall sentiment assessment for whole Wikipedia article. Segment-level predictions were aggregated into article-level sentiment using a length-weighted averaging scheme. The final label for each model corresponds to the dominant sentiment class.

Citation

If you use this dataset, please cite:

@InProceedings{10.1007/978-3-032-18920-2_34,
author="Lewoniewski, W{\l}odzimierz and Str{\'o}{\.{z}}yna, Milena and Czuma{\l}owska, Izabela and Wojewoda, Aleksandra and W{\k{e}}cel, Krzysztof",
title="Cross-Topic Sentiment Analysis of Wikipedia Articles: A Comparative Study of AI Models",
booktitle="Artificial Intelligence for Knowledge Acquisition and Management",
year="2026",
publisher="Springer Nature Switzerland",
address="Cham",
pages="364--370",
isbn="978-3-032-18920-2"
}