About this dashboard

Three corpus of public developer discussion, each analyzed for sentiment (posts from 2020 to 2025, 405,675 items in total):

• GitHub Discussions(the community forum) · ~133,900 items
• GitHub repositoriesfrom six non-AI projects (Godot, curl, Rust, Go, Flutter, Zig) · ~35,000 items. These capture AI mentions that come up organically during normal development work.
• Reddit(r/cursor and r/GithubCopilot) · ~236,800 items

Two transformer models, RoBERTa and DistilBERT, score each post. Their scores are combined into a polarity from -1 (very negative) to +1 (very positive), and mapped to a five-class label from very negative to very positive.

Each item is scored two ways. Flat uses the post text on its own. Context-augmented adds the thread around it (the original post title, the parent comment, then the comment). The Methods view compares them: adding context flips the label on 31 to 44 percent of items, and usually makes sentiment read more negative.

AI mentions are items that explicitly reference an AI coding tool (matched by keyword). The AI focus and Topics views zoom into these. Topics come from BERTopic run over the AI-mentioning items; each one is a cluster of related discussion (for example pricing and quotas, a specific model, or IDE integration). Humans vs bots separates genuine user posts from automated, bot, or template content (such as AutoModerator or code-review bots).

• r/cursor name reuse: before 2024, much of r/cursor is about mouse cursors, not the AI editor. Narrow the year range to focus on the reliable period.
• Model-inferred, not ground truth: sentiment is predicted by models, not labeled by humans. Read the scores as signals, not exact truth.
• Bot detection is heuristic and imperfect, especially in the GitHub Discussions corpus.
• Some multilingual noise: the corpora contain non-English text that the models handle unevenly.
• Explorer text is anonymized and PII-redacted, and truncated for size. Use "view original" to open the full source post.

The analyzed, PII-free data (sentiment and topics) and the redacted post text are published as a public Hugging Face dataset:

huggingface.co/datasets/mScuT/dev-sent-llm-use-analytics

The raw collection and the full analysis pipeline are kept private.

Built by Miguel Cut as part of a master's thesis at the University of Lisbon.

Supervised by Prof. Wellington Oliveira and Prof. Diana Costa.