Using LLM for Sentiment Analysis in Customer Feedback

Customer feedback rarely fits a single label. A user might praise onboarding while criticizing performance, or express frustration sarcastically. Large language models can parse this nuance, but production sentiment pipelines face a hidden tax: token-based pricing that scales with the length of every rant, transcript, and threaded support ticket. For teams processing thousands of detailed reviews monthly, that tax determines whether a project is viable.

Beyond Polarity: Why LLMs for Sentiment Analysis {#beyond-polarity}

Traditional lexicon-based classifiers force sentiment into binary buckets. LLMs capture sarcasm, mixed sentiment, and aspect-based nuance. For product teams, distinguishing between "great UI but terrible latency" and outright negative feedback changes prioritization. The goal is structured signal, not just a score.

The Cost Context for High-Volume Feedback {#cost-context}

Most inference providers meter by the token. Together AI, Fireworks AI, OpenRouter, Replicate, and Anyscale all scale costs with input length. A long support ticket or multi-page survey response inflates the bill before the model emits a single classification token.

Oxlo.ai uses request-based pricing: one flat cost per API call regardless of prompt length. For sentiment analysis on long-form reviews, transcripts, or threaded tickets, this removes the penalty for context. You can include full conversation history, product documentation, or few-shot examples without watching token meters spin. For long-context workloads, request-based pricing can be 10-100x cheaper than token-based alternatives. See https://oxlo.ai/pricing for plan details.

A Minimal Sentiment Pipeline {#pipeline-code}

Oxlo.ai exposes an OpenAI-compatible chat/completions endpoint at https://api.oxlo.ai/v1. You can drop it into existing code by changing the base URL and API key.

import openai
import json

client = openai.OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

def analyze_sentiment(feedback_text):
    response = client.chat.completions.create(
        model="qwen3-32b",
        messages=[
            {
                "role": "system",
                "content": (
                    "You are a sentiment analysis engine. "
                    "Classify the input into overall_sentiment "
                    "(positive, negative, mixed, neutral) and list specific aspects "
                    "with their own sentiment scores. Respond in valid JSON only."
                )
            },
            {"role": "user", "content": feedback_text}
        ],
        response_format={"type": "json_object"},
        temperature=0.1
    )
    return json.loads(response.choices[0].message.content)

feedback = (
    "The onboarding wizard was smooth and the design looks modern, "
    "but the API latency is unacceptable for our peak traffic. "
    "We are considering switching unless this improves in Q3."
)

print(analyze_sentiment(feedback))

Selecting a Model for Classification {#model-selection}

Not every sentiment job requires frontier reasoning. Oxlo.ai hosts 45+ models across 7 categories with no cold starts on popular options.

• Qwen 3 32B: strong multilingual reasoning for global support tickets.
• Llama 3.3 70B: a general-purpose flagship for mixed English feedback.
• DeepSeek R1 671B MoE: useful when feedback requires deep reasoning, such as parsing complex contractual complaints or layered technical critiques.
• DeepSeek V4 Flash: an efficient MoE with 1M context, ideal for analyzing entire ticket threads in a single request.
• Kimi K2.6: advanced reasoning with 131K context for agentic coding and vision tasks if your feedback includes screenshots.

Because Oxlo.ai charges per request, you can send long contexts to larger models without the typical token-based surcharge.

Batching and Long-Context Strategies {#batching-context}

With token-based billing, concatenating multiple comments into one prompt saves money only if it reduces total tokens. With request-based pricing, the incentive is simpler: one call costs one flat fee. You can batch related comments or full chat transcripts into a single prompt up to the model's context limit.

For historical analysis, pair embeddings with LLM classification. Oxlo.ai offers BGE-Large and E5-Large through its embeddings endpoint. Cluster similar tickets, then route each cluster to a chat model for nuanced sentiment labeling.

Enforcing Structured Output {#structured-output}

Sentiment data is only useful if it feeds into a dashboard or database. Oxlo.ai supports JSON mode and function calling, so you can constrain outputs to a strict schema.

Example target schema:

{
  "overall_sentiment": "mixed",
  "confidence": 0.91,
  "aspects": [
    {
      "aspect": "api_latency",
      "sentiment": "negative",
      "quote": "unacceptable for our peak traffic"
    },
    {
      "aspect": "onboarding",
      "sentiment": "positive",
      "quote": "smooth"
    }
  ]
}

Evaluation and Iteration {#evaluation}

No model is perfect on the first prompt. Reserve a held-out validation set of labeled feedback and compare model outputs. Oxlo.ai offers a free tier with 60 requests per day and a 7-day full-access trial, so you can benchmark several models against your ground truth before choosing a paid plan. The Pro plan provides 1,000 requests per day, while Premium offers 5,000 requests per day with priority queue access.

Conclusion {#conclusion}

LLM-based sentiment analysis gives product and support teams granular, actionable signal. The infrastructure choice matters as much as the prompt. Token-based providers make long-form feedback expensive, which forces teams to truncate context or shrink prompts. Oxlo.ai's flat per-request pricing removes that friction, letting you feed complete customer conversations to powerful open-source models without cost scaling by the word. If you are building feedback pipelines that process detailed reviews, transcripts, or tickets, Oxlo.ai is a relevant, cost-predictable option.