analyzing-twitter-sentiment-for-topic

Q: How do I install analyzing-twitter-sentiment-for-topic?

Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill analyzing-twitter-sentiment-for-topic in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/analyzing-twitter-sentiment-for-topic/ (personal) or .claude/skills/analyzing-twitter-sentiment-for-topic/ (project).

by apidojo-ioMarketing & SEO

Analyzes public sentiment on Twitter/X for any topic, brand, or event using apidojo's Tweet scrapers on Apify. Triggers when the user asks to: analyze Twitter sentiment about a topic, measure public opinion on Twitter, see if sentiment is positive or negative about a brand or issue on X, analyze the emotional tone of tweets about an event, research how Twitter reacts to a news story, measure brand or product sentiment from tweets, or compare sentiment between two competing topics or brands on Twitter. Returns sentiment classification, top positive and negative tweets, volume over time, and key themes. Ideal for PR teams, market researchers, political analysts, and social listening platforms.

Installs: 830
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/apidojo-io/apidojo-skills --skill analyzing-twitter-sentiment-for-topic

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads analyzing-twitter-sentiment-for-topic automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Analyzing Twitter Sentiment for a Topic

Collects a sample of tweets about any topic or keyword and performs sentiment analysis across the dataset. Identifies dominant emotional tone, key themes driving positive/negative sentiment, and volume patterns over time.

Prerequisites

APIFY_TOKEN environment variable set
Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
`searchTerms`	array	✅	`[]`	Twitter advanced search queries (e.g. `["#AI lang:en", "from:NASA"]`)
`sort`	string	Optional	`Top`	Sort order: `Latest`, `Top`, or `Latest+Top`
`tweetLanguage`	string	Optional	—	ISO 639-1 language code (e.g. `en`)
`maxItems`	number	Optional	Unlimited	Maximum tweets to return
`onlyVerifiedUsers`	boolean	Optional	`false`	Only tweets from verified users
`onlyTwitterBlue`	boolean	Optional	`false`	Only Twitter Blue subscribers
`onlyImage`	boolean	Optional	`false`	Only tweets with images
`onlyVideo`	boolean	Optional	`false`	Only tweets with videos
`onlyQuote`	boolean	Optional	`false`	Only quote tweets
`author`	string	Optional	—	Filter to a specific author handle
`inReplyTo`	string	Optional	—	Tweets replying to a specific handle
`mentioning`	string	Optional	—	Tweets mentioning a specific handle
`geotaggedNear`	string	Optional	—	Tweets near a location
`withinRadius`	string	Optional	—	Radius around geotaggedNear
`geocode`	string	Optional	—	Lat/lng + radius string
`placeObjectId`	string	Optional	—	Tweets tagged with a place
`minimumRetweets`	number	Optional	—	Minimum retweet count
`minimumFavorites`	number	Optional	—	Minimum like count
`minimumReplies`	number	Optional	—	Minimum reply count
`start`	string	Optional	—	Tweets after this date (YYYY-MM-DD)
`end`	string	Optional	—	Tweets before this date (YYYY-MM-DD)
`includeSearchTerms`	boolean	Optional	`false`	Add the matched search term to each tweet
`customMapFunction`	string	Optional	—	JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Define topic and sentiment scope
- [ ] Step 2: Collect tweets via search
- [ ] Step 3: Classify sentiment per tweet
- [ ] Step 4: Identify themes per sentiment bucket
- [ ] Step 5: Deliver sentiment report

Step 1: Clarify Parameters

Ask the user for:

Topic, keyword, or brand to analyze
Date range (default: last 7 days — Twitter sentiment data decays fast)
Language (default: English)
Sample size (default: 500 tweets — sufficient for reliable distribution)
Exclude retweets? (default: yes — reduces duplicated opinion signals)
Comparison topic (optional — for side-by-side sentiment comparison)

Step 2: Collect Tweets

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~tweet-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~tweet-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~tweet-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
  "searchTerms": ["[TOPIC_KEYWORD]"],
  "maxItems": 500,
  "tweetLanguage": "en",
  "since": "[YYYY-MM-DD]",
  "until": "[YYYY-MM-DD]"
}

REST API fallback:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchTerms": ["[TOPIC_KEYWORD]"],
    "maxItems": 500,
    "tweetLanguage": "en",
    "since": "[YYYY-MM-DD]"
  }'

Step 3: Classify Sentiment

For each tweet's text, classify as Positive, Negative, or Neutral using lexical signals:

Positive indicators: love, great, amazing, perfect, best, win, excited, congrats, excellent, recommend, beautiful, proud, happy, thank, awesome, incredible Negative indicators: hate, awful, worst, terrible, broken, scam, disappointed, angry, frustrated, disgusted, avoid, never again, shame, sad, fail, wrong, bad Strong negative amplifiers: "can't believe", "what a joke", "are you serious", "wtf", "this is ridiculous" Neutral default: Everything else

For ambiguous cases, use emoji signals:

😍🥰❤️🙌👏✨🔥 → lean Positive
😡🤬😤💀🗑️🤢👎 → lean Negative
🤔😐🤷 → lean Neutral

Weight tweets by engagement: a tweet with 1,000 likes carries more signal than one with 0.

Step 4: Theme Extraction

For Negative tweets: identify the top 3-5 recurring nouns/themes. What are people upset about specifically? For Positive tweets: identify the top 3-5 recurring praise themes.

Look for proper nouns (people, places, products), specific events, or feature names that appear repeatedly.

Step 5: Format Report

Output Format

# Twitter Sentiment Analysis: "[TOPIC]"
Period: [DATE_RANGE] | Tweets analyzed: [N] | Date: [DATE]

## Overall Sentiment

████████████░░░░░░░░ Positive: [X%] ([N] tweets) ████░░░░░░░░░░░░░░░░ Negative: [X%] ([N] tweets) ██████████░░░░░░░░░░ Neutral: [X%] ([N] tweets)


Weighted by engagement:
- Positive sentiment accounts for [X%] of total likes/RTs
- Negative sentiment accounts for [X%] of total likes/RTs

**Overall verdict:** [Mostly Positive / Mixed / Mostly Negative / Polarized]

## Top Negative Themes
1. "[Theme]" — [N] tweets, [N] total likes
   Example: "@[handle]: [tweet excerpt]"
2. "[Theme]" — [N] tweets
3. "[Theme]" — [N] tweets

## Top Positive Themes
1. "[Theme]" — [N] tweets, [N] total likes
   Example: "@[handle]: [tweet excerpt]"
2. "[Theme]" — [N] tweets

## Most Engaged Tweets
🔴 Most-liked negative: @[handle] ([N] likes): "[excerpt]"
🟢 Most-liked positive: @[handle] ([N] likes): "[excerpt]"

## Volume Over Time
[Day 1]: [N] tweets | [Day 2]: [N] tweets | [Day 3]: [N] tweets

## Notable Spikes
[Date with highest volume] — [N] tweets | Likely cause: [describe if detectable from tweet context]

Troubleshooting

Sentiment feels inaccurate: Lexical analysis misses sarcasm. For high-stakes decisions, manually review the top 20 tweets per bucket. Topic too broad: Narrow the search term. "Apple" returns tech and food — use "Apple iPhone" instead. Very low tweet volume: Topic may not be actively discussed on Twitter right now. Expand date range.

FAQ

What does the analyzing-twitter-sentiment-for-topic skill do?

How do I install analyzing-twitter-sentiment-for-topic?

Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill analyzing-twitter-sentiment-for-topic in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/analyzing-twitter-sentiment-for-topic/ (personal) or .claude/skills/analyzing-twitter-sentiment-for-topic/ (project).

Which AI agents can use analyzing-twitter-sentiment-for-topic?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is analyzing-twitter-sentiment-for-topic safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains analyzing-twitter-sentiment-for-topic?

It is published by apidojo-io in apidojo-io/apidojo-skills. It has 830 recorded installs.

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analyzing-twitter-sentiment-for-topic

Install

Install

Describe the task

Review the result

SKILL.md

Analyzing Twitter Sentiment for a Topic

Prerequisites

Inputs

Workflow

Step 1: Clarify Parameters

Step 2: Collect Tweets

Step 3: Classify Sentiment

Step 4: Theme Extraction

Step 5: Format Report

Output Format

Troubleshooting

FAQ

More from apidojo-io/apidojo-skills

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