What is the best way to set up automated sentiment analysis on customer Slack channels using lightweight open-source models?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What is the best way to set up automated sentiment analysis on customer Slack channels using lightweight open-source models?
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
best lightweight open source sentiment analysis models DistilBERT Twitter RoBERTa multilingual 2025 Hugging Face Google ↗
Slack Events API message events scopes rate limits official documentation Google ↗
- DistilBERT · huggingface.co
- CardiffNLP · huggingface.co
- Multilingual BERT · huggingface.co
- Text Classification · hf-proxy.cmyr.dev
- blog/sentiment-analysis-python.md at main · huggingface/blog · github.com
- Slack Events API · docs.slack.dev
- Hugging Face Transformers · huggingface.co
- ONNX Runtime · onnxruntime.ai
- message event · docs.slack.dev
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
best lightweight open source sentiment analysis models DistilBERT Twitter RoBERTa multilingual 2025 Hugging Face OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Slack Events API message events scopes rate limits official documentation OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |