# Reddit & Forum Sentiment Analyzer (`ramsford/reddit-forum-sentiment-analyzer`) Actor Mine authentic brand/product/competitor sentiment from Reddit and Hacker News at scale. Per-post sentiment, theme clustering, competitor mention tracking, trend detection. Pay per post analyzed. - **URL**: https://apify.com/ramsford/reddit-forum-sentiment-analyzer.md - **Developed by:** [Don Johnson](https://apify.com/ramsford) (community) - **Categories:** Social media, Marketing, AI - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $40.00 / 1,000 post analyzeds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Reddit & Forum Sentiment Analyzer **Mine authentic brand, product, and competitor sentiment from Reddit and Hacker News at scale.** Built for product managers running user research, brand managers monitoring reputation, marketing agencies benchmarking clients against competitors, startups validating pre-launch demand, and investors doing market due diligence. Reddit has 70M+ daily active users and is the most unfiltered source of authentic product opinion on the internet. This actor turns a single brand or product keyword into a structured sentiment report: per-post classification, theme clusters, competitor comparison, and trend over time — pay per post analyzed. --- ### What you get per post Each Reddit or Hacker News post that matches your keyword is parsed into one structured record: ```json { "recordType": "post", "keyword": "Notion", "source": "reddit", "scope": "r/productivity", "id": "1ab2cd3", "permalink": "https://www.reddit.com/r/productivity/comments/1ab2cd3/...", "title": "Switched from Notion to Obsidian — best decision", "author": "user42", "score": 487, "upvoteRatio": 0.94, "commentCount": 156, "createdAt": "2026-05-04T14:22:11.000Z", "sentiment": { "post": { "score": -0.42, "positive": 1, "negative": 3, "label": "negative", "confidence": 0.4 }, "comments": { "positive": 4, "negative": 6, "neutral": 5 }, "overall": { "score": -0.23, "positive": 7, "negative": 11, "label": "negative", "confidence": 1 } }, "topics": ["alternatives", "performance", "pricing"], "competitorsMentioned": ["Obsidian"], "isComparativePost": true, "commentsAnalyzed": 15, "topComments": [ { "author": "user99", "score": 78, "preview": "I left Notion last year. It's slow and the pricing got nuts...", "sentiment": "negative" } ] } ```` And one rolled-up `recordType: "summary"` record per keyword: ```json { "recordType": "summary", "keyword": "Notion", "postCount": 48, "sentimentBreakdown": { "positive": 21, "neutral": 14, "negative": 13 }, "averageSentimentScore": 0.08, "topThemes": [ { "theme": "features", "count": 27 }, { "theme": "performance", "count": 19 }, { "theme": "pricing", "count": 15 }, { "theme": "alternatives", "count": 11 } ], "competitorMentions": { "Obsidian": 18, "Roam": 6 }, "competitorComparativeSentiment": { "Obsidian": { "mentions": 18, "comparativeAvgSentiment": -0.11, "breakdown": { "positive": 5, "neutral": 6, "negative": 7 } } }, "trendByDay": [ { "day": "2026-05-10", "posts": 8, "avgSentiment": 0.12 }, { "day": "2026-05-11", "posts": 11, "avgSentiment": -0.04 } ], "sources": { "reddit": 39, "hackernews": 9 } } ``` *** ### Input ```json { "keywords": ["Notion"], "competitors": ["Obsidian", "Roam"], "subreddits": ["productivity", "notion"], "sources": ["reddit", "hackernews"], "maxPosts": 50, "maxCommentsPerPost": 10, "dateRange": "month", "sortBy": "relevance" } ``` - **keywords** (required) — one or more brand, product, or topic terms. Each is searched and summarised separately. - **subreddits** — restrict Reddit to these subs. Leave empty for site-wide search. - **sources** — `reddit`, `hackernews`, or both. - **maxPosts** — cap per keyword. Each post is one billable record. - **maxCommentsPerPost** — top comments fetched per post and folded into the sentiment score. `0` skips comment fetch entirely (faster, cheaper, but less accurate). - **dateRange** — `hour`, `day`, `week`, `month`, `year`, `all`. - **sortBy** — `relevance` (default, best for brand monitoring), `new` (best for alerts), `top` (best for impact), `hot`, `comments`. - **competitors** — track these names in the same posts and compute comparative sentiment. - **minScore** — skip posts below this upvote/point threshold. - **includeRawText** — turn on to emit full post body + every comment body in each record (off by default for compact JSON). *** ### Sentiment & topics Sentiment is computed with a deterministic, transparent lexicon (no opaque AI calls — fast, cheap, auditable): - Per token, count positive vs. negative cues; negators (`not`, `never`, `no`, etc.) flip the sign. - Score = `(positive − negative) / (positive + negative)`, clipped to `[-1, 1]`. - Label = `positive`, `negative`, or `neutral`, with a `confidence` score `[0–1]`. Topics are classified into: - `pricing`, `features`, `performance`, `reliability`, `ux`, `support`, `integrations`, `security`, `mobile`, `collaboration`, `alternatives` Multiple topics per post is normal — Reddit threads rarely stay on one theme. *** ### Use cases - **Brand health snapshot.** Run `["MyBrand"]` weekly on `dateRange: "week"` → trend dashboard. - **Competitive intelligence.** Pass your competitors in `competitors`; the actor flags every post where your keyword is compared against them and reports comparative sentiment. - **Pre-launch demand validation.** Search a problem space (e.g. `"AI meeting notes"`) and read the `topThemes` and `topComments` for unmet needs. - **Investor due diligence.** Mine sentiment for a target company across `r/startups`, `r/SaaS`, and HN before a check. - **Show-HN tracking.** Set `sources: ["hackernews"]` and `dateRange: "week"` to catch new launches. *** ### Pricing Pay per post analyzed (`post-analyzed` event). Summary records are emitted for free. Comments are fetched and analysed in-place — no separate charge. *** ### Output destinations - **Default dataset:** every record (`post` and `summary`) is pushed there. - **Key-value store `SUMMARY`:** run-level counts and per-keyword breakdown. *** ### Notes - Reddit data is fetched from the public `*.json` endpoints. No login required. - Hacker News data is from the public Algolia search API (`hn.algolia.com`). - Apify proxy is used by default for rotation; residential is **not** required for these endpoints in normal volumes. - The sentiment lexicon is intentionally simple and visible inside `src/main.js` so you can extend it for your industry. *** ### Author Johnson AI Consulting — production data and intelligence actors on the Apify marketplace. # Actor input Schema ## `keywords` (type: `array`): Brand names, product names, or topics to mine sentiment for. Each is searched independently. Example: \['Notion', 'Obsidian']. ## `subreddits` (type: `array`): Limit Reddit search to these subreddits (without 'r/' prefix). Leave empty to search all of Reddit. Example: \['productivity', 'startups']. ## `sources` (type: `array`): Which platforms to pull from. ## `maxPosts` (type: `integer`): Cap the number of posts analyzed per keyword. Each post counts as one billable record. ## `maxCommentsPerPost` (type: `integer`): How many top comments per post to fetch and include in sentiment analysis. 0 disables comment fetching (saves time). ## `dateRange` (type: `string`): Restrict posts to this time window (Reddit search 't' parameter, mapped for HN too). ## `sortBy` (type: `string`): Reddit search sort. 'relevance' is best for brand monitoring; 'new' is best for trend alerts; 'top' is best for impact. ## `competitors` (type: `array`): Track mentions of these competitor names across the same posts. Comparative sentiment is computed when a post mentions both the keyword and a competitor. ## `minScore` (type: `integer`): Skip posts below this score threshold. 0 disables. ## `includeRawText` (type: `boolean`): Off by default for compact JSON. Turn on if you need the raw text for downstream NLP. ## `proxy` (type: `object`): Apify proxy. Reddit JSON endpoints are generally fine without residential proxy. ## Actor input object example ```json { "keywords": [ "Notion", "Obsidian" ], "subreddits": [ "productivity", "startups" ], "sources": [ "reddit", "hackernews" ], "maxPosts": 50, "maxCommentsPerPost": 10, "dateRange": "month", "sortBy": "relevance", "competitors": [ "Obsidian", "Roam" ], "minScore": 0, "includeRawText": false, "proxy": { "useApifyProxy": true, "apifyProxyGroups": [] } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "proxy": { "useApifyProxy": true, "apifyProxyGroups": [] } }; // Run the Actor and wait for it to finish const run = await client.actor("ramsford/reddit-forum-sentiment-analyzer").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "proxy": { "useApifyProxy": True, "apifyProxyGroups": [], } } # Run the Actor and wait for it to finish run = client.actor("ramsford/reddit-forum-sentiment-analyzer").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "proxy": { "useApifyProxy": true, "apifyProxyGroups": [] } }' | apify call ramsford/reddit-forum-sentiment-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ramsford/reddit-forum-sentiment-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit & Forum Sentiment Analyzer", "description": "Mine authentic brand/product/competitor sentiment from Reddit and Hacker News at scale. Per-post sentiment, theme clustering, competitor mention tracking, trend detection. Pay per post analyzed.", "version": "0.1", "x-build-id": "Mu1q9mRcZjJNZaLEK" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ramsford~reddit-forum-sentiment-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ramsford-reddit-forum-sentiment-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ramsford~reddit-forum-sentiment-analyzer/runs": { "post": { "operationId": "runs-sync-ramsford-reddit-forum-sentiment-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ramsford~reddit-forum-sentiment-analyzer/run-sync": { "post": { "operationId": "run-sync-ramsford-reddit-forum-sentiment-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "keywords" ], "properties": { "keywords": { "title": "Search keywords", "type": "array", "description": "Brand names, product names, or topics to mine sentiment for. Each is searched independently. Example: ['Notion', 'Obsidian'].", "items": { "type": "string" } }, "subreddits": { "title": "Subreddits (optional)", "type": "array", "description": "Limit Reddit search to these subreddits (without 'r/' prefix). Leave empty to search all of Reddit. Example: ['productivity', 'startups'].", "items": { "type": "string" } }, "sources": { "title": "Sources to mine", "uniqueItems": true, "type": "array", "description": "Which platforms to pull from.", "items": { "type": "string", "enum": [ "reddit", "hackernews" ], "enumTitles": [ "Reddit", "Hacker News" ] }, "default": [ "reddit", "hackernews" ] }, "maxPosts": { "title": "Max posts per keyword", "minimum": 1, "maximum": 500, "type": "integer", "description": "Cap the number of posts analyzed per keyword. Each post counts as one billable record.", "default": 50 }, "maxCommentsPerPost": { "title": "Max top comments per post", "minimum": 0, "maximum": 50, "type": "integer", "description": "How many top comments per post to fetch and include in sentiment analysis. 0 disables comment fetching (saves time).", "default": 10 }, "dateRange": { "title": "Date range", "enum": [ "hour", "day", "week", "month", "year", "all" ], "type": "string", "description": "Restrict posts to this time window (Reddit search 't' parameter, mapped for HN too).", "default": "month" }, "sortBy": { "title": "Sort order (Reddit)", "enum": [ "relevance", "new", "top", "hot", "comments" ], "type": "string", "description": "Reddit search sort. 'relevance' is best for brand monitoring; 'new' is best for trend alerts; 'top' is best for impact.", "default": "relevance" }, "competitors": { "title": "Competitor terms (optional)", "type": "array", "description": "Track mentions of these competitor names across the same posts. Comparative sentiment is computed when a post mentions both the keyword and a competitor.", "items": { "type": "string" } }, "minScore": { "title": "Min score (Reddit upvotes / HN points)", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Skip posts below this score threshold. 0 disables.", "default": 0 }, "includeRawText": { "title": "Include full post body & comments in output", "type": "boolean", "description": "Off by default for compact JSON. Turn on if you need the raw text for downstream NLP.", "default": false }, "proxy": { "title": "Proxy configuration", "type": "object", "description": "Apify proxy. Reddit JSON endpoints are generally fine without residential proxy.", "default": { "useApifyProxy": true, "apifyProxyGroups": [] } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```