reddit-sentiment-analyzer

name	reddit-sentiment-analyzer
description	Analyze Reddit sentiment for any product, brand, or topic. Scrapes subreddit posts and comments, classifies sentiment, extracts recurring themes, and generates an actionable report with direct links to source threads.

Reddit Sentiment Analyzer

Turn Reddit's unfiltered user opinions into structured market intelligence. Sai browses target subreddits, reads posts and top comments, classifies sentiment (positive/negative/neutral), identifies recurring themes, and compiles a report with quantified insights and direct source links.

Sai's unfair advantage: Most sentiment tools use Reddit's API (which has strict rate limits and costs money since the 2023 API changes). Sai uses a real browser session — it can read any public subreddit, see full comment threads, and follow cross-posts. No API key needed, no per-request costs.

When to Use

• "What does Reddit think about [product/brand]?"
• "Analyze sentiment in r/[subreddit] about [topic]"
• "Find common complaints about [competitor] on Reddit"
• "What features do Reddit users wish [product category] had?"
• "Monitor Reddit mentions of [brand] from the last 30 days"
• "Compare Reddit sentiment: [product A] vs [product B]"

Required Context (ask if not provided)

• Target: Product name, brand, or topic to analyze
• Subreddits: Specific subreddits or let Sai discover relevant ones
• Time window: Last 7 days, 30 days, 90 days, or all time
• Focus: General sentiment, feature requests, complaints, comparisons, or all?
• Competitor comparison: Any competitors to compare against?
• Output: In-chat summary, Google Sheet with raw data, Google Doc report, or all?

Workflow

Phase 1: Discover Relevant Subreddits

// Search Google for relevant subreddits
var discoveryPage = await browser.newtab("https://www.google.com/search?q=" + encodeURIComponent("[product] site:reddit.com"))
await discoveryPage.wait({ waitTime: 2 })
var discSnap = await discoveryPage.snapshot()

// Extract subreddit names from results
var subreddits = [] // e.g., ["r/product", "r/industry", "r/reviews"]

// Also try Reddit's own search
var redditSearch = await browser.newtab("https://www.reddit.com/search/?q=" + encodeURIComponent("[product]") + "&type=link&sort=relevance&t=month")
await redditSearch.wait({ waitTime: 3 })

Phase 2: Scrape Posts & Comments

// For each subreddit, search for the topic
var allPosts = []

for (var sub of subreddits) {
  var subPage = await browser.newtab("https://www.reddit.com/" + sub + "/search/?q=" + encodeURIComponent("[topic]") + "&restrict_sr=1&sort=relevance&t=month")
  await subPage.wait({ waitTime: 3 + Math.random() * 3 })
  var subSnap = await subPage.snapshot()
  
  // Extract post titles, scores, comment counts, dates
  // Click into top 5-10 posts to read comments
  
  // For each post, collect:
  var post = {
    title: "",
    subreddit: sub,
    score: 0,
    commentCount: 0,
    date: "",
    url: "",
    topComments: [], // top 5-10 comments with scores
    sentiment: "",   // will be classified
    themes: []       // will be extracted
  }
  
  allPosts.push(post)
  await wait({ waitTime: 2 + Math.random() * 3 }) // rate limiting
}

Phase 3: Sentiment Classification

// Classify each post and comment
var sentimentResults = {
  positive: [],
  negative: [],
  neutral: [],
  mixed: []
}

for (var post of allPosts) {
  // Analyze post title + top comments to determine sentiment
  // Look for signal words:
  // Positive: love, amazing, best, recommend, switched to, game changer
  // Negative: hate, terrible, avoid, switched from, waste, broken
  // Neutral: question, how to, comparison, alternative
  
  // Classify and tag
  post.sentiment = "positive" // or negative/neutral/mixed
  post.themes = [] // "pricing", "features", "support", "UX", etc.
  
  sentimentResults[post.sentiment].push(post)
}

Phase 4: Theme Extraction & Quantification

// Group by recurring themes
var themes = {}
for (var post of allPosts) {
  for (var theme of post.themes) {
    if (!themes[theme]) themes[theme] = { positive: 0, negative: 0, neutral: 0, posts: [] }
    themes[theme][post.sentiment]++
    themes[theme].posts.push(post.url)
  }
}

// Sort themes by frequency
var sortedThemes = Object.entries(themes)
  .sort((a, b) => (b[1].positive + b[1].negative + b[1].neutral) - (a[1].positive + a[1].negative + a[1].neutral))

Phase 5: Generate Report

// Compile findings
var report = {
  summary: {
    totalPosts: allPosts.length,
    sentimentBreakdown: {
      positive: sentimentResults.positive.length,
      negative: sentimentResults.negative.length,
      neutral: sentimentResults.neutral.length,
      mixed: sentimentResults.mixed.length
    },
    overallSentiment: "", // "Mostly Positive" / "Mixed" / etc.
    topPositiveTheme: "",
    topNegativeTheme: ""
  },
  themes: sortedThemes,
  topPositivePosts: sentimentResults.positive.slice(0, 5),
  topNegativePosts: sentimentResults.negative.slice(0, 5),
  actionableInsights: [],
  subredditsAnalyzed: subreddits,
  timeWindow: "Last 30 days",
  sourcesCount: allPosts.length
}

Phase 6: Output to Google Sheet (Optional)

var sheet = await google.sheets.createSpreadsheet({
  title: "Reddit Sentiment: [Product] - " + new Date().toLocaleDateString(),
  sheets: ["Summary", "Raw Posts", "Theme Analysis"]
})

// Summary tab
await google.sheets.updateValues({
  spreadsheetId: sheet.id,
  range: "Summary!A1",
  values: [
    ["Reddit Sentiment Analysis Report"],
    ["Target", "[product]"],
    ["Time Window", "Last 30 days"],
    ["Total Posts Analyzed", allPosts.length.toString()],
    [""],
    ["Sentiment", "Count", "Percentage"],
    ["Positive", report.summary.sentimentBreakdown.positive.toString(), ""],
    ["Negative", report.summary.sentimentBreakdown.negative.toString(), ""],
    ["Neutral", report.summary.sentimentBreakdown.neutral.toString(), ""]
  ]
})

// Raw Posts tab - all posts with sentiment tags and URLs
// Theme Analysis tab - theme breakdown with evidence

Safety & Anti-Ban Rules

• Random delays: Wait 2-5 seconds between page loads (randomized)
• Max pages per session: 40 Reddit pages
• Never interact: Read only — no voting, commenting, or posting
• Respect private subreddits: Skip if access denied
• If CAPTCHA appears: STOP immediately, report to user
• Old Reddit fallback: If new Reddit is slow/blocked, try old.reddit.com

Output Checklist

• Every sentiment claim backed by specific post/comment URL
• Theme percentages based on actual counted data
• No fabricated quotes — only paraphrase or direct attribution
• Actionable insights tied to evidence
• Time window and subreddits clearly documented