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Awesome GitHub RepositoriesLLM-Based Sentiment Analyzers
Analyzers that leverage large language models to interpret emotional tone and intent through contextual analysis.
Distinct from Lexicon-Based Sentiment Analyzers: Distinct from Lexicon-Based Sentiment Analyzers: uses generative language models for contextual understanding rather than static word-weighting lists.
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Awesome LLM-Based Sentiment Analyzers GitHub Repositories
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- curiousily/get-things-done-with-prompt-engineering-and-langchain
curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain
1,242Auf GitHub ansehenThis project is an educational collection of Jupyter notebooks and guides focused on building applications with the LangChain framework. It serves as a practical resource for developers learning to implement prompt engineering, retrieval-augmented generation, and autonomous agent workflows to create intelligent, context-aware systems. The repository distinguishes itself by providing hands-on tutorials for connecting language models to private datasets and external tools. It covers the end-to-end process of designing structured input templates, orchestrating multi-step task sequences, and main
Process text through a language model to generate numerical sentiment scores based on specific persona-driven instructions and historical context to understand user intent.
Jupyter Notebookartificial-intelligencechatgptdeep-learning
