Extract tweets, replies, and mentions programmatically and feed them into your NLP pipeline for real-time sentiment classification.
Understanding public opinion about your brand, product, or topic requires analyzing thousands of tweets. Manual monitoring is impossible at scale, and the official Twitter API is too expensive and rate-limited for continuous sentiment tracking.
ScrapeBadger lets you pull tweets by keyword, user mention, or hashtag with no rate limits. Feed the structured JSON directly into sentiment analysis models (OpenAI, HuggingFace, or custom NLP) for real-time brand health monitoring.
import ScrapeBadger from 'scrapebadger';
const sb = new ScrapeBadger({ apiKey: 'YOUR_API_KEY' });
// Search for brand mentions
const results = await sb.twitter.tweets.advancedSearch({
query: '@yourbrand OR "your brand" lang:en',
queryType: 'Latest',
});
// Analyze sentiment of each tweet
for (const tweet of results.data) {
const sentiment = await analyzeSentiment(tweet.text);
console.log(`[${sentiment}] @${tweet.author.username}: ${tweet.text}`);
}There are no daily limits. With 1,000 req/s throughput, you can analyze millions of tweets per day.
ScrapeBadger provides raw tweet data. Pair it with OpenAI, HuggingFace, or any NLP service for sentiment classification.
Yes. Schedule periodic searches and store results to build sentiment trend dashboards.
Use the lang: operator in search queries to filter by language. ScrapeBadger supports all languages Twitter does.
Track every mention of your brand on Twitter. Get real-time alerts via WebSocket or webhooks and respond to customers before issues escalate.
Monitor competitor accounts, analyze their content strategy, track engagement rates, and benchmark your performance against theirs.
Find potential customers by monitoring purchase-intent keywords, competitor complaints, and industry discussions on Twitter.