julep-ai/openai-community-posts

Name: julep-ai/openai-community-posts
Creator: julep-ai
Published: 2024-03-08 02:02:43
License: 暂无描述

Hugging Face2024-03-08 更新2024-06-22 收录

下载链接：

https://hf-mirror.com/datasets/julep-ai/openai-community-posts

下载链接

链接失效反馈

官方服务：

资源简介：

--- dataset_info: features: - name: post_discussion_id dtype: int64 - name: post_discussion_tags sequence: string - name: post_discussion_title dtype: string - name: post_discussion_created_at dtype: timestamp[ns, tz=UTC] - name: post_category_id dtype: int64 - name: post_discussion_views dtype: int64 - name: post_discussion_reply_count dtype: int64 - name: post_discussion_like_count dtype: int64 - name: post_discussion_participant_count dtype: int64 - name: post_discussion_word_count dtype: float64 - name: post_id dtype: int64 - name: post_created_at dtype: string - name: post_content dtype: string - name: post_read_count dtype: int64 - name: post_reply_count dtype: int64 - name: post_author_id dtype: string - name: post_number dtype: int64 - name: post_discussion_related_topics sequence: int64 - name: accepted_answer_post dtype: float64 - name: post_content_raw dtype: string - name: post_category_name dtype: string - name: post_sentiment dtype: string - name: post_sentiment_score dtype: float64 - name: post_content_cluster_embedding sequence: float64 - name: post_content_classification_embedding sequence: float64 - name: post_content_search_document_embedding sequence: float64 - name: tag1 dtype: string - name: tag2 dtype: string - name: tag3 dtype: string - name: tag4 dtype: string - name: post_discussion_url dtype: string - name: post_url dtype: string - name: topic_model_medium dtype: string - name: topic_model_broad dtype: string splits: - name: train num_bytes: 1959958888 num_examples: 97033 download_size: 1928991796 dataset_size: 1959958888 configs: - config_name: default data_files: - split: train path: data/train-* --- # OpenAI Community Posts This dataset is curated from the posts of the OpenAI Community Forum (https://community.openai.com). ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a3efa56866210ffc6f83f1/C7FF2hLRHO6A-PDxni-Dw.png) ## Dataset Details ### Dataset Description The OpenAI Community Posts dataset comprises discussions, posts, and metadata from the OpenAI Community Forum. It includes details such as discussion titles, tags, views, reply counts, post content, sentiment scores, vector embeddings for content analysis, and identifiers linking posts to discussions. The dataset aims to facilitate analysis on community engagement, content sentiment, and discussion dynamics. _The dataset includes post from the creation of the forum till Feb 28th, 2024_ The dataset was primarily gathered to understand the sentiment of different OpenAI products amongst the users as well as to gather feedback, complaints and common problems users faced. Posts from the following [categories](https://community.openai.com/categories) and their relevant sub-categories are included: - [API](https://community.openai.com/c/api/7) - API/Bugs - API/Deprecations - API/Feedback - [GPT Builders](https://community.openai.com/c/gpts-builders/33) - GPT Builders/Chat-Plugins - GPT Builders/Plugin-Store - [Prompting](https://community.openai.com/c/prompting/8) - [Community](https://community.openai.com/c/community/21) - [Documentation](https://community.openai.com/c/documentation/14) - **Curated by:** Julep AI - **Language(s) (NLP):** English ### Dataset Sources [optional] - **Forum:** https://community.openai.com --- ## Dataset Structure The OpenAI Community Posts dataset is structured around two primary entities: discussions and posts. Each discussion comprises multiple posts, including an initiating post and subsequent replies. The dataset includes various features capturing the characteristics and metrics of both discussions and posts, as well as sentiment analyses and vector embeddings for advanced content analysis. ### Fields Description - **Discussion-Level Features**: - `post_discussion_id`: Unique identifier for the discussion. - `post_discussion_tags`: Tags or keywords associated with the discussion. - `post_discussion_title`: Title of the discussion. - `post_discussion_created_at`: Timestamp indicating when the discussion was created. - `post_category_id`: Identifier for the category under which the discussion falls. - `post_discussion_views`: Number of views the discussion has received. - `post_discussion_reply_count`: Count of replies or posts within the discussion. - `post_discussion_like_count`: Number of likes the discussion has accumulated. - `post_discussion_participant_count`: Number of unique participants in the discussion. - `post_discussion_word_count`: Total word count of all posts within the discussion. - `post_discussion_related_topics`: Related topics or discussions. - `post_discussion_url`: Web URL of the discussion. - **Post-Level Features**: - `post_id`: Unique identifier for the post. - `post_author`: Name or identifier of the post's author. - `post_created_at`: Timestamp indicating when the post was created. - `post_content`: HTML content of the post. - `post_read_count`: Number of times the post has been read. - `post_reply_count`: Number of replies to the post. - `post_author_id`: Unique identifier for the post's author. - `post_number`: Sequential number of the post within the discussion. - `accepted_answer_post`: Boolean indicating if the post is marked as the accepted answer to the discussion. - `post_content_raw`: Markdown formatted content of the post. - `post_category_name`: Name of the category to which the post/discussion belongs. - `post_sentiment`: Sentiment of the post (e.g., positive, negative, neutral). - `post_sentiment_score`: Numerical score representing the sentiment of the post. - `post_content_cluster_embedding`: Vector embedding for clustering purposes. - `post_content_classification_embedding`: Vector embedding for classification. - `post_content_search_document_embedding`: Vector embedding intended for enhancing search functionalities. - `post_url`: Web URL of the post. ### Additional Notes - **Relationships**: Each post is linked to a discussion through `post_discussion_id`, facilitating analyses that require context from the discussion level or aggregations at the discussion level. - **Vector Embeddings**: The inclusion of vector embeddings (`post_content_cluster_embedding`, `post_content_classification_embedding`, `post_content_search_document_embedding`) enables advanced NLP tasks, including but not limited to clustering, classification, and enhanced search capabilities within the dataset. - **Sentiment Analysis**: Sentiment scores (`post_sentiment`, `post_sentiment_score`) provide insights into the emotional tone of posts, useful for content analysis, community mood tracking, and identifying discussions that may require moderator attention. This structure supports a wide range of analyses, from basic statistical summaries to complex machine learning models, by providing comprehensive metadata, content, and derived metrics for each post and discussion in the OpenAI Community Forum. ## Dataset Creation ### Curation Rationale The OpenAI Community Posts dataset consists of discussions and posts from the OpenAI Community Forum, specifically curated to analyze developer sentiment, identify common problems, and gather feedback on OpenAI products. It includes detailed metadata for discussions and posts, sentiment scores, and vector embeddings for content, facilitating a comprehensive analysis of community engagement and response to OpenAI's offerings. This dataset serves as a valuable resource for understanding the needs, challenges, and perceptions of developers using OpenAI technologies, contributing to product improvement and community support. #### Personal and Sensitive Information Efforts were made to anonymize personal information where possible, excluding direct identifiers but including publicly shared content and metadata for analysis. Specifically, `post_author` field was dropped and `post_author_id` was converted to a SHA256 hash to preserve user identification.

提供机构：

julep-ai

原始信息汇总

OpenAI Community Posts 数据集概述

数据集描述

OpenAI Community Posts 数据集包含来自 OpenAI 社区论坛的讨论、帖子和元数据。数据集包括讨论标题、标签、浏览量、回复数量、帖子内容、情感分数、内容分析的向量嵌入以及将帖子与讨论关联的标识符。该数据集旨在促进对社区参与度、内容情感和讨论动态的分析。

数据集包括从论坛创建到2024年2月28日的帖子。

数据集结构

OpenAI Community Posts 数据集围绕两个主要实体构建：讨论和帖子。每个讨论包含多个帖子，包括发起帖和后续回复。数据集包括各种特征，捕捉讨论和帖子的特性和指标，以及情感分析和高级内容分析的向量嵌入。

字段描述

讨论级特征

post_discussion_id: 讨论的唯一标识符。
post_discussion_tags: 与讨论相关的标签或关键词。
post_discussion_title: 讨论的标题。
post_discussion_created_at: 讨论创建的时间戳。
post_category_id: 讨论所属类别的标识符。
post_discussion_views: 讨论的浏览量。
post_discussion_reply_count: 讨论中的回复或帖子数量。
post_discussion_like_count: 讨论获得的点赞数量。
post_discussion_participant_count: 讨论中的唯一参与者数量。
post_discussion_word_count: 讨论中所有帖子的总字数。
post_discussion_related_topics: 相关主题或讨论。
post_discussion_url: 讨论的网页URL。

帖子级特征

post_id: 帖子的唯一标识符。
post_author_id: 帖子作者的唯一标识符。
post_created_at: 帖子创建的时间戳。
post_content: 帖子的HTML内容。
post_read_count: 帖子的阅读次数。
post_reply_count: 帖子的回复数量。
post_number: 帖子在讨论中的顺序编号。
accepted_answer_post: 布尔值，指示帖子是否被标记为讨论的接受答案。
post_content_raw: 帖子的Markdown格式内容。
post_category_name: 帖子/讨论所属类别的名称。
post_sentiment: 帖子的情感（例如，正面、负面、中性）。
post_sentiment_score: 表示帖子情感的数值分数。
post_content_cluster_embedding: 用于聚类的向量嵌入。
post_content_classification_embedding: 用于分类的向量嵌入。
post_content_search_document_embedding: 用于增强搜索功能的向量嵌入。
post_url: 帖子的网页URL。

附加说明

关系: 每个帖子通过 post_discussion_id 链接到一个讨论，便于需要讨论级别上下文或讨论级别聚合的分析。
向量嵌入: 向量嵌入（post_content_cluster_embedding, post_content_classification_embedding, post_content_search_document_embedding）的包含使得高级NLP任务成为可能，包括但不限于聚类、分类和增强搜索功能。
情感分析: 情感分数（post_sentiment, post_sentiment_score）提供了帖子情感色调的洞察，有助于内容分析、社区情绪跟踪和识别可能需要版主关注的讨论。

该结构支持广泛的分析，从基本的统计摘要到复杂的机器学习模型，通过提供每个帖子及其所属讨论的全面元数据、内容和派生指标。

数据集创建

筛选理由

OpenAI Community Posts 数据集由 OpenAI 社区论坛的讨论和帖子组成，专门筛选以分析开发者情感、识别常见问题并收集对 OpenAI 产品的反馈。它包括讨论和帖子的详细元数据、情感分数和内容向量嵌入，便于对社区参与度和对 OpenAI 产品响应的全面分析。该数据集是了解使用 OpenAI 技术的开发者的需求、挑战和认知的宝贵资源，有助于产品改进和社区支持。

个人和敏感信息

已尽力对个人信息进行匿名化处理，排除直接标识符，但包括公开分享的内容和元数据以供分析。具体来说，post_author 字段已被删除，post_author_id 已转换为 SHA256 哈希以保留用户标识。

搜集汇总

数据集介绍

构建方式

在自然语言处理领域，社区论坛数据为理解用户互动与产品反馈提供了丰富资源。本数据集通过系统化爬取OpenAI社区论坛的公开讨论内容构建而成，涵盖了从论坛创立至2024年2月28日期间发布的帖子。数据采集聚焦于API、GPT构建工具、提示工程、社区交流及文档等核心板块，确保覆盖开发者关注的主要议题。构建过程中，原始数据经过结构化处理，将讨论与回帖关联，并剔除了作者姓名等直接标识符，对用户ID进行哈希转换以保护隐私，同时保留了完整的元数据与内容字段。

使用方法

针对社区数据分析与自然语言处理研究，该数据集提供了灵活的应用路径。研究者可直接利用其丰富的元数据字段进行描述性统计分析，例如探究讨论热度与参与度的关联。对于机器学习任务，预生成的情感标签与向量嵌入可作为特征输入，用于训练情感分类模型或构建语义搜索系统。在实践层面，数据集的层级结构允许以讨论为单位进行上下文分析，或跨帖子进行主题聚合。通过关联讨论ID与帖子内容，能够深入追踪特定议题的演变轨迹，为社区管理与产品反馈分析提供实证依据。

背景与挑战

背景概述

在人工智能技术迅猛发展的浪潮中，社区论坛作为开发者交流与反馈的核心平台，其数据蕴含了丰富的用户行为模式与产品认知信息。由Julep AI于2024年2月28日前收集并构建的OpenAI社区帖子数据集，正是基于OpenAI官方社区论坛的公开讨论内容精心整理而成。该数据集旨在深入解析开发者对OpenAI系列产品的使用体验、情感倾向及常见问题，涵盖了API、GPT构建工具、提示工程等多个技术板块的讨论帖与元数据。通过整合帖子内容、互动指标、情感分析得分及多维向量嵌入等特征，该资源为研究社区参与度、内容动态及技术反馈提供了结构化基础，对优化人工智能产品生态与用户支持策略具有显著参考价值。

当前挑战

该数据集致力于解决社区内容分析与情感挖掘领域的核心挑战，即如何从海量非结构化的论坛讨论中，精准提取用户反馈并量化社区情感演变。在构建过程中，面临多重技术难题：首先，论坛数据的异构性要求对帖子、回复及元数据进行复杂的关系建模与清洗，以确保数据的一致性与完整性；其次，在保护用户隐私的前提下进行有效的数据匿名化处理，例如将作者标识符转换为哈希值，同时保留必要的分析维度，这需要在隐私保护与数据效用间取得微妙平衡。此外，为帖子内容生成高质量的向量嵌入以支持聚类、分类等高级自然语言处理任务，也对计算资源与算法设计提出了较高要求。

常用场景

经典使用场景

在自然语言处理与社区分析领域，该数据集为研究在线技术社区动态提供了丰富的语料。其经典使用场景聚焦于开发者社区的内容挖掘与情感追踪，通过整合讨论标题、标签、浏览量、回复数及情感评分等多维度特征，支持对OpenAI产品用户反馈的深度解析。研究人员可借助向量嵌入技术，对海量帖子进行聚类与分类，从而识别社区中的热门议题、技术痛点及用户情绪演变趋势，为理解人工智能开发者社群的互动模式奠定数据基础。

解决学术问题

该数据集有效解决了社区驱动型研究中数据稀缺与结构复杂的问题。通过提供详尽的元数据与情感标注，它助力学者探究在线讨论中的参与度动力学、内容传播机制以及用户反馈的情感极性。其嵌入向量特征更推动了文本表示学习的发展，使得基于深度学习的主题建模、异常检测及多模态分析成为可能，从而深化了对技术社区知识构建与信息扩散规律的理论认识。

实际应用

在实际应用层面，该数据集为产品团队与社区管理者提供了宝贵的洞察工具。企业可依据情感评分与讨论热度，实时监测用户对API、GPT构建工具等产品的满意度，快速定位常见故障与功能需求。同时，嵌入向量支持构建智能推荐系统，自动关联相似问题与解决方案，提升社区支持效率。这些应用不仅优化了用户体验，也为产品迭代与市场策略调整提供了数据驱动的决策依据。

数据集最近研究