are there any plugins for sentiment analysis
# ask-aim
magnificent-monitor-48407
03/05/2026, 1:29 PMare there any plugins for sentiment analysis
t
tall-airline-16756
03/05/2026, 1:29 PMThanks for reaching out, arijit. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
tall-airline-16756
03/05/2026, 1:30 PMThanks — I’m happy to help. You’re asking whether there are any plugins for sentiment analysis.
I don’t have information here that lists a built-in LiveKit plugin for sentiment analysis. To help you best, could you tell me a bit more about what you want to do so I can recommend the right approach or integration?
• Do you want real-time sentiment analysis of voice audio (live speech), text chat messages, or post-call recordings?
• If real-time voice, do you need on-device/local analysis or cloud-based services?
• Which language(s) do you need to analyze?
• Are you using a specific client (web, iOS, Android) or server-side pipeline where you’d like to attach sentiment analysis?
• Do you have any preferred third-party providers (Google Cloud, AWS, Azure, OpenAI, or others)?
With those details I can:
• Suggest integration patterns (e.g., streaming audio -> speech-to-text -> sentiment API; or send chat text to sentiment API).
• Outline which third-party services are commonly used and how they typically connect to a LiveKit flow.
• Provide sample architecture or code snippets for the option you prefer.
m
magnificent-monitor-48407
03/05/2026, 1:30 PMpost-call recordings?
t
tall-airline-16756
03/05/2026, 1:30 PMThanks — you asked about sentiment analysis for post-call recordings. I checked what's available: LiveKit does not offer a built-in sentiment-analysis plugin for post-call recordings. However, you can implement sentiment analysis by exporting recordings or transcripts and sending them to third‑party sentiment APIs or local models.
Here are practical approaches you can take (pick the one that fits your stack):
• Transcript-first (recommended)
a. Export or generate a transcript of the recording using an STT service (LiveKit supports STT integrations).
b. Send the transcript text to a sentiment analysis API or model (examples below).
c. Store the sentiment result alongside the recording/transcript in your database.
• Audio-first (if you need prosodic cues)
a. Export the recorded audio file.
b. Option A: Run speech-to-text, then apply text sentiment analysis.
c. Option B: Use models that analyze audio emotion/sentiment directly (research or specialized vendors).
d. Combine text + audio signals for richer sentiment scoring.
Common third-party services and models to consider
• Cloud APIs: Google Cloud Natural Language, AWS Comprehend, Azure Text Analytics — all provide sentiment analysis for text.
• LLMs: OpenAI, Claude, or other LLMs can return sentiment labels or scores when prompted.
• Open-source models: Hugging Face sentiment models (for on-prem or custom pipelines).
• Audio/emotion models: Specialized vendors or open models that analyze voice emotion (if you need tone/prosody).
Questions to help me give a tailored integration plan
• Do you already generate transcripts for your recordings, or do you only have raw audio files?
• Where are recordings stored (S3, other cloud storage, or your servers)?
• Do you prefer a cloud API (managed) or an on-prem/open-source model?
• Which languages do you need to analyze?
• Do you want per-call overall sentiment, per-speaker sentiment, or time-segmented sentiment?
If you tell me your preferred storage and whether transcripts exist, I can give a concrete step-by-step integration (including example API calls, how to map speaker labels, and storage/metadata suggestions).
Sources: examples/frontdesk | livekit/livekit_composite | DeepWiki | Speech-to-text (STT) models overview | LiveKit Documentation | examples/voice_agents | livekit/livekit_composite | DeepWiki | Agent Builder | LiveKit Documentation
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