This dataset contains tech product reviews collected from online tech forums. The data is stored in JSON Lines (.jsonl) format, where each line represents a single article.

License Information

We do not own any of the public texts from which these text data has been extracted. We license the actual packaging of these text data under the Creative Commons CC0 license ("no rights reserved").

📂 Dataset Structure

Each record includes the following fields:

id (string) – unique identifier of the article (UUID)
origin (string) – who crawled this data, e.g.: BUT, TAIPEITECH, LINGEA
url (string) – original article URL
title (string) – title of the article
html (string) – full HTML content of the article at the time of scraping in UTF-8 encoding
text (string) – plain text version of the article without HTML tags in UTF-8 encoding
dateCrawled (string, ISO 8601) – timestamp of article collection in ISO format
datePublished (string, ISO 8601) – date when the source page was published in ISO format
dateUpdated (string, ISO 8601) – date when the source page was updated in ISO format

Example record:

{
  "id": "36f194ce-fff9-55bd-aa2c-30ec6204bb5a",
  "origin": "TAIPEITECH",
  "url": "https://example.com/article",
  "title": "Here goes the article title...",
  "html": "<html> ... </html>",
  "text": "Here goes the article review...",
  "dateCrawled": "2025-10-05T03:25:48",
  "datePublished": "2024-01-14T19:07:58+0800",
  "dateUpdated": "2024-01-14T19:07:58+0800"
}

🔍 Inter-rater Reliability

The dataset consists of six distinct packages of review comments collected from four online forums. Each package contains approximately 500 comments, which were independently annotated by four annotators per package. Annotators were instructed to classify each comment according to a predefined annotation guideline, with an option to leave a comment undecided if the category was unclear.

To assess annotation reliability, we computed Cross-Replication Reliability (xRR). We additionally computed standard inter-annotator agreement metrics including Cohen’s Kappa, Fleiss’ Kappa, and Krippendorff’s Alpha to evaluate individual-level consistency.

This dataset is a work in progress, and future updates will include additional packages, expanded annotations, and refinements based on ongoing quality control.

The dataset includes comments in both English and Traditional Chinese, reflecting a bilingual annotation setting.

Inter-rater reliability (IRR) was assessed using various metrics. Krippendorff's nominal α ranged between -0.12 to 0.37 (mean 0.13), indicating a slight overall agreement. Cross-replication reliability (xRR; Wong et al., 2021), which measures chance-corrected agreement between majority labels of randomly split annotator groups, ranged between 0.05 to 0.32 (mean 0.19). High majority class prevalence (67–84%) across packages shows the importance of chance correction.

We observed the annotators who labeled the data if they actually agreed with each other, using several different scoring methods. Using Krippendorff's alpha metrics, we have a score ranged between -0.12 and 0.37 across the different packages, averaging 0.13. This means that the annotators showed an overall slight agreement.

Using Cross-replication reliability (xRR) metrics, we check by randomly splitting the annotators into two separate teams and let each team vote on the most common answer, how often would the two teams reach the same conclusion after taking random guesses into account. We have a score that ranging between 0.05 to 0.32, averaging 0.19. This means that there is slight agreement between what the two independent groups would decide.

One important trend that we observed is that there is a majority choice in what the annotators would annotate, ranging between 67% to 84% of the cases across the packages. This means that almost every review gets the same label in the package. Annotators seem to be in total agreement, but in actual fact, annotators agree by chance because the "popular" or "obvious" answer is selected most of the time.

🔗 For more details about the annotation guidelines, see this document.

Reference

Ka Wong, Praveen Paritosh, and Lora Aroyo. 2021. Cross-replication Reliability - An Empirical Approach to Interpreting Inter-rater Reliability. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7053–7065, Online. Association for Computational Linguistics.

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