DOI: 10.5281/zenodo.4533301
title: Dataset of Video Comments of a Vision Video Classified by Their Relevance, Polarity, Intention, and Topic

description: This dataset contains all comments (comments and replies) of the YouTube vision video "Tunnels" by "The Boring Company" fetched on 2020-10-13 using YouTube API. The comments are classified manually by three persons. We performed a single-class labeling of the video comments regarding their relevance for requirement engineering (RE) (ham/spam), their polarity (positive/neutral/negative). Furthermore, we performed a multi-class labeling of the comments regarding their intention (feature request and problem report) and their topic (efficiency and safety). While a comment can only be relevant or not relevant and have only one polarity, a comment can have one or more intentions and also one or more topics. For the replies, one person also classified them regarding their relevance for RE. However, the investigation of the replies is ongoing and future work. Remark: For 126 comments and 26 replies, we could not determine the date and time since they were no longer accessible on YouTube at the time this data set was created. In the case of a missing date and time, we inserted "NULL" in the corresponding cell. This data set includes the following files: Dataset.xlsx contains the raw and labeled video comments and replies: For each comment, the data set contains: ID: An identification number generated by YouTube for the comment Date: The date and time of the creation of the comment Author: The username of the author of the comment Likes: The number of likes of the comment Replies: The number of replies to the comment Comment: The written comment Relevance: Label indicating the relevance of the comment for RE (ham = relevant, spam = irrelevant) Polarity: Label indicating the polarity of the comment Feature request: Label indicating that the comment request a feature Problem report: Label indicating that the comment reports a problem Efficiency: Label indicating that the comment deals with the topic efficiency Safety: Label indicating that the comment deals with the topic safety For each reply, the data set contains: ID: The identification number of the comment to which the reply belongs Date: The date and time of the creation of the reply Author: The username of the author of the reply Likes: The number of likes of the reply Comment: The written reply Relevance: Label indicating the relevance of the reply for RE (ham = relevant, spam = irrelevant) Detailed analysis results.xlsx contains the detailed results of all ten times repeated 10-fold cross validation analyses for each of all considered combinations of machine learning algorithms and features Guide Sheet - Multi-class labeling.pdf describes the coding task, defines the categories, and lists examples to reduce inconsistencies and increase the quality of manual multi-class labeling Guide Sheet - Single-class labeling.pdf describes the coding task, defines the categories, and lists examples to reduce inconsistencies and increase the quality of manual single-class labeling Python scripts for analysis.zip contains the scripts (as jupyter notebooks) and prepared data (as csv-files) for the analyses