social-sentiment-analyzer

Social Sentiment Analyzer

Overview

Analyze public sentiment for any brand, product, or topic across Twitter/X, Reddit, and Instagram. Fetches real posts, classifies sentiment, extracts themes, and produces a structured report.

When to Use

Activate when the user asks:

• "What's the sentiment around [TOPIC]?"
• "Analyze sentiment for [BRAND] on Twitter"
• "What are people saying about [PRODUCT] on social media?"
• "Is the reaction to [EVENT] positive or negative?"
• "Social media opinion on [TOPIC]"

Setup & Authentication

Before fetching data, ensure Xpoz access is configured. Follow these checks in order.

Check 1: Already authenticated?

If you have MCP tools, try calling any Xpoz tool (e.g., checkAccessKeyStatus). If it works → skip to Step 1.

If you have the SDK, try:

from xpoz import XpozClient
client = XpozClient()  # reads XPOZ_API_KEY env var

If this succeeds without error → skip to Step 1.

If neither works, you need to authenticate. Choose the path that fits your environment:

---

Path A: MCP via mcporter (OpenClaw agents)

If mcporter is available:

mcporter call xpoz.checkAccessKeyStatus

If hasAccessKey: true → ready. If not:

mcporter config add xpoz https://mcp.xpoz.ai/mcp --auth oauth

Then authenticate — generate the OAuth URL and send it to the user:

Step 1: Generate authorization URL

import secrets, hashlib, base64, urllib.parse, json, urllib.request, os

verifier = secrets.token_urlsafe(64)
challenge = base64.urlsafe_b64encode(hashlib.sha256(verifier.encode()).digest()).rstrip(b'=').decode()
state = secrets.token_urlsafe(32)

# Dynamic client registration
reg_req = urllib.request.Request(
    'https://mcp.xpoz.ai/oauth/register',
    data=json.dumps({
        'client_name': 'Agent Skills',
        'redirect_uris': ['https://www.xpoz.ai/oauth/openclaw'],
        'grant_types': ['authorization_code'],
        'response_types': ['code'],
        'token_endpoint_auth_method': 'none',
    }).encode(),
    headers={'Content-Type': 'application/json'},
)
reg_resp = json.loads(urllib.request.urlopen(reg_req).read())

params = urllib.parse.urlencode({
    'response_type': 'code',
    'client_id': reg_resp['client_id'],
    'code_challenge': challenge,
    'code_challenge_method': 'S256',
    'redirect_uri': 'https://www.xpoz.ai/oauth/openclaw',
    'state': state,
    'scope': 'mcp:tools',
    'resource': 'https://mcp.xpoz.ai/',
})

auth_url = 'https://mcp.xpoz.ai/oauth/authorize?' + params

# Save state for token exchange
os.makedirs(os.path.expanduser('~/.cache/xpoz-oauth'), exist_ok=True)
with open(os.path.expanduser('~/.cache/xpoz-oauth/state.json'), 'w') as f:
    json.dump({'verifier': verifier, 'state': state, 'client_id': reg_resp['client_id'],
               'redirect_uri': 'https://www.xpoz.ai/oauth/openclaw'}, f)

print(auth_url)

Step 2: Send the URL to the user

Tell them:

"I need to connect to Xpoz for social media data. Please open this link and sign in:

[auth_url]

After authorizing, you'll see a code. Paste it back to me here."

Step 3: WAIT for the user to reply with the code. Do not proceed until they respond.

Step 4: Exchange the code for a token

Once the user provides the code (either a raw code or a URL containing ?code=...), extract the code and exchange it:

import json, urllib.request, urllib.parse, subprocess, os

with open(os.path.expanduser('~/.cache/xpoz-oauth/state.json')) as f:
    oauth = json.load(f)

code = "THE_CODE_FROM_USER"  # Extract from user's reply

data = urllib.parse.urlencode({
    'grant_type': 'authorization_code',
    'code': code,
    'redirect_uri': oauth['redirect_uri'],
    'client_id': oauth['client_id'],
    'code_verifier': oauth['verifier'],
}).encode()

req = urllib.request.Request(
    'https://mcp.xpoz.ai/oauth/token',
    data=data,
    headers={'Content-Type': 'application/x-www-form-urlencoded'},
)
resp = json.loads(urllib.request.urlopen(req).read())
token = resp['access_token']

# Configure mcporter with the token (token is never printed)
subprocess.run(['mcporter', 'config', 'remove', 'xpoz'], capture_output=True)
subprocess.run(['mcporter', 'config', 'add', 'xpoz', 'https://mcp.xpoz.ai/mcp',
                '--header', f'Authorization=Bearer {token}'], check=True)

# Clean up
os.remove(os.path.expanduser('~/.cache/xpoz-oauth/state.json'))
print("Xpoz configured successfully")

Step 5: Verify with mcporter call xpoz.checkAccessKeyStatus → should return hasAccessKey: true.

---

Path B: MCP via Claude Code config

For Claude Code users without mcporter, add to ~/.claude.json:

{
  "mcpServers": {
    "xpoz": {
      "url": "https://mcp.xpoz.ai/mcp",
      "transport": "http-stream"
    }
  }
}

Claude Code handles OAuth automatically on first tool call — the user just needs to authorize in their browser when prompted.

---

Path C: SDK (Python or TypeScript)

Ask the user:

"I need a Xpoz API key to access social media data. Please go to https://xpoz.ai/get-token (it's free, no credit card needed) and paste the key back to me."

WAIT for the user to reply with the key. Then:

Python:

pip install xpoz

from xpoz import XpozClient
client = XpozClient("THE_KEY_FROM_USER")

TypeScript:

npm install @xpoz/xpoz

import { XpozClient } from "@xpoz/xpoz";
const client = new XpozClient({ apiKey: "THE_KEY_FROM_USER" });
await client.connect();

Or set the environment variable and use the default constructor:

export XPOZ_API_KEY=THE_KEY_FROM_USER

---

Auth Errors

Problem	Solution
MCP: "Unauthorized"	Re-run the OAuth flow above
SDK: AuthenticationError	Verify key at xpoz.ai/settings
Token exchange fails	Ask user to re-authorize — codes are single-use

Step-by-Step Instructions

Step 1: Parse the Request

Extract from the user's message:

• Topic/brand to analyze
• Platforms to search (default: Twitter + Reddit; add Instagram if relevant)
• Time period (default: last 7 days)
• Language filter (default: English)

Expand the query for better coverage:

• Publicly traded companies → include ticker symbol: "Tesla" OR "$TSLA"
• Products → include common abbreviations: "ChatGPT" OR "GPT-4"
• Events → include hashtags: "CES 2026" OR "#CES2026"

Step 2: Fetch Posts

Via MCP (if xpoz MCP server is configured)

Twitter:

Call getTwitterPostsByKeywords:
  query: "<expanded query>"
  fields: ["id", "text", "authorUsername", "createdAtDate", "likeCount", "retweetCount", "impressionCount"]
  startDate: "<7 days ago, YYYY-MM-DD>"
  endDate: "<today, YYYY-MM-DD>"
  language: "en"

Reddit:

Call getRedditPostsByKeywords:
  query: "<expanded query>"
  fields: ["id", "title", "text", "authorUsername", "createdAtDate", "score", "numComments", "subreddit"]
  startDate: "<7 days ago>"
  endDate: "<today>"

Instagram (if requested):

Call getInstagramPostsByKeywords:
  query: "<expanded query>"
  fields: ["id", "text", "authorUsername", "createdAtDate", "likeCount", "commentCount"]
  startDate: "<7 days ago>"
  endDate: "<today>"

CRITICAL: Async Pattern — Each call returns an operationId. You MUST call checkOperationStatus with that ID and poll until status is "completed" (up to 8 retries, ~5 seconds apart).

Via Python SDK

from xpoz import XpozClient

client = XpozClient()  # Uses XPOZ_API_KEY env var

# Twitter
twitter_results = client.twitter.search_posts(
    '"Tesla" OR "$TSLA"',
    start_date="2026-02-16",
    end_date="2026-02-23",
    language="en",
    fields=["id", "text", "author_username", "created_at_date", "like_count", "retweet_count"]
)

# Reddit
reddit_results = client.reddit.search_posts(
    '"Tesla" OR "$TSLA"',
    start_date="2026-02-16",
    end_date="2026-02-23",
    fields=["id", "title", "text", "author_username", "created_at_date", "score", "num_comments", "subreddit"]
)

# Collect all posts
twitter_posts = twitter_results.data
reddit_posts = reddit_results.data

# Fetch additional pages if needed
while twitter_results.has_next_page():
    twitter_results = twitter_results.next_page()
    twitter_posts.extend(twitter_results.data)

client.close()

Via TypeScript SDK

import { XpozClient } from "@xpoz/xpoz";

const client = new XpozClient();
await client.connect();

const twitterResults = await client.twitter.searchPosts('"Tesla" OR "$TSLA"', {
  startDate: "2026-02-16",
  endDate: "2026-02-23",
  language: "en",
  fields: ["id", "text", "authorUsername", "createdAtDate", "likeCount", "retweetCount"],
});

const redditResults = await client.reddit.searchPosts('"Tesla" OR "$TSLA"', {
  startDate: "2026-02-16",
  endDate: "2026-02-23",
  fields: ["id", "title", "text", "authorUsername", "createdAtDate", "score", "numComments", "subreddit"],
});

await client.close();

Step 3: Classify Sentiment

For each post, classify into one of 5 levels:

Level	Indicators
Positive	"love", "amazing", "bullish", "great", "best", 🚀🔥💪, strong praise
Leaning Positive	"looking good", "solid", "promising", measured optimism
Neutral	Questions, factual statements, news without opinion, balanced takes
Leaning Negative	"worried", "not sure", "concerned", "some issues", cautious criticism
Negative	"terrible", "worst", "avoid", "bearish", 📉💀, strong criticism

Tips:

• Sarcasm detection: "Great, another outage" → Negative
• Retweets/quotes with no commentary → Neutral
• Engagement-weighted: high-engagement posts carry more signal

Step 4: Extract Themes

Identify 5-8 recurring themes from the posts. For each theme:

• Title: 3-5 word label
• Sentiment: overall lean of posts in this theme
• Key quotes: 2-3 representative posts
• Volume: approximate % of total posts

Step 5: Generate Report

Present results in this structure:

## Sentiment Report: [TOPIC]
**Period:** [start] to [end] | **Posts analyzed:** [count]

### Overall Sentiment
Score: [0-100, where 50=neutral, 100=max positive]
- Positive: X%
- Neutral: X%
- Negative: X%

### Platform Breakdown
| Platform | Posts | Sentiment Score | Top Theme |
|----------|-------|----------------|-----------|
| Twitter  | X     | X              | ...       |
| Reddit   | X     | X              | ...       |

### Key Themes
1. **[Theme Title]** (Positive/Neutral/Negative)
   [2-3 sentence explanation with example quotes]

2. **[Theme Title]** ...

### Notable Posts
[Top 5 highest-engagement posts with text, author, and metrics]

### Summary
[2-3 paragraph executive summary with actionable insights]

Example Prompts

• "Analyze sentiment around NVIDIA this week on Twitter and Reddit"
• "What's the social media reaction to the new iPhone?"
• "How are people feeling about Cursor IDE on Reddit?"
• "Sentiment analysis for Bitcoin in the last 30 days"

Notes

• Free tier: 100,000 results/month at xpoz.ai
• For large datasets, use CSV export (export_csv() / exportCsv()) and analyze locally
• Reddit tends to have longer, more nuanced opinions; Twitter has higher volume but shorter takes