Suivi du sentiment des avis produits

import requests
import json
from pathlib import Path

API_KEY = "your_scavio_api_key"

POSITIVE_WORDS = {"great", "love", "excellent", "perfect", "best", "amazing"}
NEGATIVE_WORDS = {"broken", "terrible", "worst", "waste", "awful", "bad", "poor"}

def search_product(query: str, platform: str) -> list[dict]:
    res = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": API_KEY},
        json={"platform": platform, "query": f"{query} reviews"},
        timeout=15,
    )
    res.raise_for_status()
    return res.json().get("organic", [])

def score_sentiment(text: str) -> str:
    words = set(text.lower().split())
    pos = len(words & POSITIVE_WORDS)
    neg = len(words & NEGATIVE_WORDS)
    if pos > neg:
        return "positive"
    if neg > pos:
        return "negative"
    return "neutral"

def run():
    products = ["your-product-name", "competitor-product"]
    results = []

    for product in products:
        for platform in ["amazon", "reddit"]:
            items = search_product(product, platform)
            for item in items:
                text = item.get("snippet", "") or item.get("title", "")
                sentiment = score_sentiment(text)
                results.append({
                    "product": product,
                    "platform": platform,
                    "title": item.get("title", ""),
                    "sentiment": sentiment,
                })

    counts = {"positive": 0, "neutral": 0, "negative": 0}
    for r in results:
        counts[r["sentiment"]] += 1

    print(f"Tracked {len(results)} mentions")
    print(f"Sentiment: {json.dumps(counts)}")

    neg_ratio = counts["negative"] / max(len(results), 1)
    if neg_ratio > 0.4:
        print("ALERT: Negative sentiment exceeds 40% threshold")

if __name__ == "__main__":
    run()

const API_KEY = "your_scavio_api_key";

const POSITIVE = new Set(["great", "love", "excellent", "perfect", "best", "amazing"]);
const NEGATIVE = new Set(["broken", "terrible", "worst", "waste", "awful", "bad", "poor"]);

async function searchProduct(query, platform) {
  const res = await fetch("https://api.scavio.dev/api/v1/search", {
    method: "POST",
    headers: {
      "x-api-key": API_KEY,
      "content-type": "application/json",
    },
    body: JSON.stringify({ platform, query: `${query} reviews` }),
  });
  if (!res.ok) throw new Error(`scavio ${res.status}`);
  const data = await res.json();
  return data.organic ?? [];
}

function scoreSentiment(text) {
  const words = new Set(text.toLowerCase().split(/\s+/));
  let pos = 0, neg = 0;
  for (const w of words) {
    if (POSITIVE.has(w)) pos++;
    if (NEGATIVE.has(w)) neg++;
  }
  if (pos > neg) return "positive";
  if (neg > pos) return "negative";
  return "neutral";
}

async function run() {
  const products = ["your-product-name", "competitor-product"];
  const results = [];

  for (const product of products) {
    for (const platform of ["amazon", "reddit"]) {
      const items = await searchProduct(product, platform);
      for (const item of items) {
        const text = item.snippet ?? item.title ?? "";
        results.push({
          product,
          platform,
          title: item.title ?? "",
          sentiment: scoreSentiment(text),
        });
      }
    }
  }

  const counts = { positive: 0, neutral: 0, negative: 0 };
  for (const r of results) counts[r.sentiment]++;

  console.log(`Tracked ${results.length} mentions`);
  console.log("Sentiment:", JSON.stringify(counts));

  const negRatio = counts.negative / Math.max(results.length, 1);
  if (negRatio > 0.4) {
    console.log("ALERT: Negative sentiment exceeds 40% threshold");
  }
}

run();