Twitter Search and Analysis

Overview

Search Twitter for keywords using advanced search syntax, fetch up to 1000 relevant tweets, and analyze the data to produce professional reports with insights, statistics, and actionable recommendations.

Prerequisites

API Key Required: Users must configure their Twitter API key from https://twitterapi.io

The API key can be provided in three ways:

Environment variable (recommended): Set

TWITTER_API_KEY

in your

~/.bashrc

~/.zshrc

echo 'export TWITTER_API_KEY="your_key_here"' >> ~/.bashrc
source ~/.bashrc

As an argument: Use
```
--api-key YOUR_KEY
```
with the wrapper script
Passed directly: As first argument to the Python script

Quick Start

Using the Wrapper Script (Recommended)

The wrapper script automatically handles environment variable loading and dependency checks:

# Basic search (uses TWITTER_API_KEY from shell config)
./scripts/run_search.sh "AI"
With custom API key
./scripts/run_search.sh "AI" --api-key YOUR_KEY
With options
./scripts/run_search.sh "&quot;Claude AI&quot;" --max-results 100 --format summary
Advanced query

./scripts/run_search.sh "from:elonmusk since:2024-01-01" --query-type Latest

Direct Python Script Usage

# Search for a keyword
scripts/twitter_search.py "$API_KEY" "AI"
Search with multiple keywords
scripts/twitter_search.py "$API_KEY" "&quot;ChatGPT&quot; OR &quot;Claude AI&quot;"
Search from specific user
scripts/twitter_search.py "$API_KEY" "from:elonmusk"
Search with date range

scripts/twitter_search.py "$API_KEY" "Bitcoin since:2024-01-01"

Advanced Queries

# Complex query: AI tweets from verified users, English only
scripts/twitter_search.py "$API_KEY" "AI OR \"machine learning\" lang:en filter:verified"
Recent crypto tweets with minimum engagement
scripts/twitter_search.py "$API_KEY" "Bitcoin min_retweets:10 lang:en"
From specific influencers

scripts/twitter_search.py "$API_KEY" "from:elonmusk OR from:VitalikButerin since:2024-01-01"

Output Format

# Full JSON with all tweets
scripts/twitter_search.py "$API_KEY" "AI" --format json
Summary with statistics (default)

scripts/twitter_search.py "$API_KEY" "AI" --format summary

Options

```
--max-results N
```
: Maximum tweets to fetch (default: 1000)
```
--query-type Latest|Top
```
: Sort order (default: Top for relevance)
```
--format json|summary
```
: Output format (default: summary)

Workflow

1. Understand User Requirements

Clarify the analysis goal:

What topic/keyword to search?
Date range preference?
Specific users to include/exclude?
Language preference?
Type of insights needed (trends, sentiment, influencers)?

2. Build the Search Query

Use Twitter Advanced Search syntax:

Syntax	Example	Description
`keyword`	`AI`	Single keyword
`"phrase"`	`"machine learning"`	Exact phrase
`OR`	`AI OR ChatGPT`	Either term
`from:user`	`from:elonmusk`	From specific user
`to:user`	`to:elonmusk`	Reply to user
`since:DATE`	`since:2024-01-01`	After date
`until:DATE`	`until:2024-12-31`	Before date
`lang:xx`	`lang:en`	Language code
`#hashtag`	`#AI`	Hashtag
`filter:links`	`filter:links`	Tweets with links
`min_retweets:N`	`min_retweets:100`	Minimum retweets

3. Fetch Data

Execute the search script:

scripts/twitter_search.py "$API_KEY" "YOUR_QUERY" --max-results 1000 --query-type Top

Important: Default is 1000 tweets maximum. The script automatically:

Paginates through all available results
Stops at 1000 tweets (API limit consideration)
Handles errors gracefully

4. Analyze and Generate Report

After fetching data, produce a comprehensive professional report with:

Report Structure

Executive Summary (2-3 sentences)
- What was searched
- Key findings overview
Data Overview
- Total tweets analyzed
- Date range of data
- Query parameters used
Key Metrics
- Total engagement (likes, retweets, replies, quotes, views)
- Average engagement per tweet
- Language distribution
- Reply vs. original tweet ratio
Top Content Analysis
- Most retweeted tweets (with URL links to original tweets)
- Most liked tweets (with URL links to original tweets)
- Top hashtags with frequency
- Most mentioned users
- Selected tweet examples with full URL references
Influencer Analysis
- Top users by follower count
- Most active users
- Verified user percentage
Trend Insights (based on data patterns)
- Emerging themes
- Sentiment indicators
- Temporal patterns
- Conversation drivers
Key Takeaways
- 3-5 bullet points of core insights
- Data-backed conclusions
Actionable Recommendations
- Specific, implementable suggestions
- Based on the data findings
- Prioritized by impact

Analysis Guidelines

Be data-driven: Every claim should reference actual metrics
Provide context: Explain why metrics matter
Identify patterns: Look for trends across the dataset
Stay objective: Present facts, avoid speculation
Be specific: Recommendations should be concrete and actionable
Consider external context: Use web search for background when relevant

5. Output Format

Present the report in clear markdown with:

Headers for each section
Tables for structured data
Bullet points for lists
Bold for key metrics
Code blocks for tweet examples

Clickable URLs for all referenced tweets (format:

[@username](https://x.com/username/status/tweet_id)

)

Tweet URL Format

Always include clickable links to tweets:

| Author | Tweet | URL |
|--------|-------|-----|
| @user | Summary of tweet content | [View](https://x.com/user/status/123456) |

Or inline format:

- **@username**: Tweet summary - [View Tweet](https://x.com/username/status/123456)

Query Examples by Use Case

Trend Analysis

"AI" OR "artificial intelligence" lang:en min_retweets:50

Competitor Monitoring

from:competitor1 OR from:competitor2 since:2024-01-01

Product Launch Tracking

#ProductName OR "Product Name" lang:en filter:verified

Crisis Monitoring

#BrandName OR "Brand Name" lang:en --query-type Latest

Influencer Discovery

#Topic lang:en min_retweets:100 min_faves:500

Sentiment Analysis

"brand name" OR #BrandName lang:en --max-results 1000

Resources

scripts/run_search.sh (Wrapper Script)

Convenience wrapper that handles environment variable loading and dependency checks:

Automatically loads
```
TWITTER_API_KEY
```
from
```
~/.bashrc
```
or
```
~/.zshrc
```
Checks Python availability and installs missing dependencies
Provides user-friendly error messages
Supports all command-line options from the Python script

Usage:

./scripts/run_search.sh <query> [options]

Options:

```
--api-key KEY
```
: Override environment variable API key
```
--max-results N
```
: Maximum tweets to fetch (default: 1000)
```
--query-type Latest|Top
```
: Sort order (default: Top)
```
--format json|summary
```
: Output format (default: json)

scripts/twitter_search.py

Executable Python script that:

Fetches tweets from Twitter API
Handles pagination automatically
Extracts key tweet metrics
Calculates aggregate statistics
Outputs structured JSON data

Usage:

scripts/twitter_search.py <api_key> <query> [options]

references/twitter_api.md

Comprehensive API documentation including:

Complete parameter reference
Query syntax guide
Response structure details
Pagination instructions
Best practices for analysis
Error handling guide

Read this when: Building complex queries or understanding data structure.

Tips for Better Analysis

Use Top query type for trend analysis (more relevant results)
Set date filters for timely insights
Filter by language for accurate text analysis
Include minimum engagement to filter noise
Combine with web search to validate trends
Look beyond metrics - analyze content themes
Track hashtags to identify sub-conversations
Identify influencers by combining followers + engagement

Error Handling

If the script fails:

Check API key validity
Verify query syntax
Ensure network connectivity
Check rate limits (if applicable)
Review error messages for specific issues

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Twitter Search and Analysis

Overview

Prerequisites

Quick Start

Using the Wrapper Script (Recommended)

With custom API key

With options

Advanced query

Direct Python Script Usage

Search with multiple keywords

Search from specific user

Search with date range

Advanced Queries

Recent crypto tweets with minimum engagement

From specific influencers

Output Format

Summary with statistics (default)

Options

Workflow

1. Understand User Requirements

2. Build the Search Query

3. Fetch Data

4. Analyze and Generate Report

Report Structure

Analysis Guidelines

5. Output Format

Tweet URL Format

Query Examples by Use Case

Trend Analysis

Competitor Monitoring

Product Launch Tracking

Crisis Monitoring

Influencer Discovery

Sentiment Analysis

Resources

scripts/run_search.sh (Wrapper Script)

scripts/twitter_search.py

references/twitter_api.md

Tips for Better Analysis

Error Handling

Quick Start

Manual Installation

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