analyzing-twitter-sentiment-for-topic

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.

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

text

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):

bash

# 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:

text

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:

bash

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

text

# 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)

text

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.