Sentiment Analysis

Sentiment Analysis

Analyze the emotional tone of any text. Returns a sentiment label, numeric score, confidence percentage, and lists of positive/negative words found.

Free to use. This skill costs nothing. Just sign up at claw0x.com, create an API key, and start calling. No credit card, no wallet top-up required.

How It Works — Under the Hood

This skill uses a lexicon-based sentiment analysis approach — no LLM, no external AI API. The entire computation runs deterministically in a single serverless function.

The Algorithm

1. Tokenization — the input text is split into individual words (tokens), normalized to lowercase, and stripped of punctuation.

2. Lexicon lookup — each token is matched against a sentiment lexicon (a dictionary of words with pre-assigned positive/negative scores). Words like "love", "amazing", "excellent" have positive scores; words like "terrible", "hate", "awful" have negative scores.

3. Score aggregation — individual word scores are summed to produce a raw sentiment score. Positive total = positive sentiment, negative total = negative sentiment, near-zero = neutral.

4. Normalization — the raw score is divided by the token count to produce a comparative score (sentiment per word), which allows fair comparison between short and long texts.

5. Confidence calculation — confidence is derived from the ratio of sentiment-bearing words to total words. More opinionated text = higher confidence. Neutral text with few sentiment words = lower confidence.

6. Label assignment — the raw score is mapped to a human-readable label:

   • score >= 3 → "very positive"
   • score >= 1 → "positive"
   • score > -1 → "neutral"
   • score > -3 → "negative"
   • score <= -3 → "very negative"

Why Lexicon-Based (Not LLM)?

• Deterministic — same input always produces the same output. No temperature, no randomness.
• Fast — sub-10ms processing. No network call to an AI provider.
• Cheap — zero compute cost beyond the serverless function itself, which is why this skill is free.
• Transparent — you can see exactly which words drove the score (returned in positive_words / negative_words).

The tradeoff: lexicon-based analysis doesn't understand sarcasm, context, or nuance the way an LLM would. "This is sick!" scores negative (lexicon sees "sick") even though colloquially it's positive. For most use cases — customer feedback, review analysis, social media monitoring — lexicon-based is accurate enough and far more predictable.

Prerequisites

This is a free skill. Just get an API key:

1. Sign up at claw0x.com
2. Go to Dashboard → API Keys → Create Key
3. Set it as an environment variable:

export CLAW0X_API_KEY="your-api-key-here"

No credit card or wallet balance needed.

When to Use

• User says "analyze the sentiment", "is this positive or negative", "what's the tone of this text"
• Agent pipeline needs sentiment scoring as a processing step
• Evaluating customer feedback, reviews, or social media posts
• Filtering or routing messages based on emotional tone

Input

Output Fields

Example

Input: "I love this product, but the shipping was terrible."

Output:

{
  "sentiment": "neutral",
  "score": 0,
  "comparative": 0,
  "confidence": 20,
  "tokens": 10,
  "positive_words": ["love"],
  "negative_words": ["terrible"]
}

The mixed sentiment (one positive word, one negative word) results in a near-zero score, correctly labeled "neutral".

Error Codes

• 400 — Missing or empty text input
• 401 — Invalid or missing API key
• 500 — Processing failed (not billed)

Pricing

Free. Apply for an API key and use it at no cost. No credit card required.