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Topic Modeling Tool

This tool is designed to perform topic modeling and other various text analysis on textual data using R for core analysis and a Python-based user interface (UI) built with the Tkinter library. It is particularly effective with long textual responses and provides visual aids through word clouds for shorter surveys.

Very quick steps

python setup_env.py
venv\Scripts\activate
python topic_modeling_app.py

Quick steps

  1. First, run the following script: setup_env.py
  2. Second, activate your virtual environment with the following command: venv\Scripts\activate
  3. Third, run the last script: topic_modeling_app.py

Execution Policy Issues?

Are you running to any issues regarding Execution Policy? You can temporarily bypass the restriction for the current PowerShell session by running the following command in your terminal:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

Afterwards, try activating the virtual environment again:

venv\Scripts\activate

Features

  • Topic Modeling: Ideal for analyzing extensive text data.
  • Word Cloud: Visualizes the most frequent terms in datasets, best suited for shorter surveys.
  • More to follow

Further prerequisites

Before using this tool, please ensure the following steps are completed to set up your environment:

Install and Set Up Required Libraries

  1. R and Python: Ensure both R and Python are installed on your computer. Download them from their official websites if necessary.
  2. Library Installation:
    • R Libraries: Open your R console, navigate to the directory containing requirements.R, and execute source('requirements.R').
    • Python Libraries: Open a command prompt or terminal, navigate to the directory containing requirements.txt, and execute pip install -r requirements.txt.

Update Script Paths

  • Verify that the TreeTagger tool is correctly installed and its path is appropriately set in both R scripts for text processing. Search for the term "teamIR" in the scripts to identify and update these paths.

How to Run the Tool

Starting the Application

  • Open topic_modeling_app.py in your Python IDE (like IDLE or PyCharm) using the file browser.

Using the Application

  • The UI is designed to be user-friendly:
    • Use the Word Cloud option for shorter surveys to visualize key terms.
    • Use the Topic Modeling option for detailed analysis of more complex text data.
  • Once the analysis is complete, the tool automatically saves the results in an Excel file in the same directory as the script.

Getting Help

If you encounter any issues or need further assistance, please feel free to contact amir.khodaie@ru.nl

About

Text analysis

Resources

Readme
Activity

Stars

0 stars

Watchers

2 watching

Forks

4 forks
Report repository

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Used by

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Topic Modeling Tool
This tool is designed to perform topic modeling and other various text analysis on textual data using R for core analysis and a Python-based user interface (UI) built with the Tkinter library. It is particularly effective with long textual responses and provides visual aids through word clouds for shorter surveys.
Very quick steps
python setup_env.py
venv\Scripts\activate
python topic_modeling_app.py

Quick steps
  1. First, run the following script: setup_env.py
  2. Second, activate your virtual environment with the following command: venv\Scripts\activate
  3. Third, run the last script: topic_modeling_app.py
Execution Policy Issues?
Are you running to any issues regarding Execution Policy? You can temporarily bypass the restriction for the current PowerShell session by running the following command in your terminal:
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

Afterwards, try activating the virtual environment again:
venv\Scripts\activate

Features
  • Topic Modeling: Ideal for analyzing extensive text data.
  • Word Cloud: Visualizes the most frequent terms in datasets, best suited for shorter surveys.
  • More to follow
Further prerequisites
Before using this tool, please ensure the following steps are completed to set up your environment:
Install and Set Up Required Libraries
  1. R and Python: Ensure both R and Python are installed on your computer. Download them from their official websites if necessary.
  2. Library Installation:
    • R Libraries: Open your R console, navigate to the directory containing requirements.R, and execute source('requirements.R').
    • Python Libraries: Open a command prompt or terminal, navigate to the directory containing requirements.txt, and execute pip install -r requirements.txt.
Update Script Paths
  • Verify that the TreeTagger tool is correctly installed and its path is appropriately set in both R scripts for text processing. Search for the term "teamIR" in the scripts to identify and update these paths.
How to Run the Tool
Starting the Application
  • Open topic_modeling_app.py in your Python IDE (like IDLE or PyCharm) using the file browser.
Using the Application
  • The UI is designed to be user-friendly:

    • Use the Word Cloud option for shorter surveys to visualize key terms.
    • Use the Topic Modeling option for detailed analysis of more complex text data.
  • Once the analysis is complete, the tool automatically saves the results in an Excel file in the same directory as the script.
Getting Help
If you encounter any issues or need further assistance, please feel free to contact amir.khodaie@ru.nl
About
Text analysis
Resources
Readme
Activity
Stars
0 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Topic Modeling Tool
This tool is designed to perform topic modeling and other various text analysis on textual data using R for core analysis and a Python-based user interface (UI) built with the Tkinter library. It is particularly effective with long textual responses and provides visual aids through word clouds for shorter surveys.
Very quick steps
python setup_env.py
venv\Scripts\activate
python topic_modeling_app.py

Quick steps
  1. First, run the following script: setup_env.py
  2. Second, activate your virtual environment with the following command: venv\Scripts\activate
  3. Third, run the last script: topic_modeling_app.py
Execution Policy Issues?
Are you running to any issues regarding Execution Policy? You can temporarily bypass the restriction for the current PowerShell session by running the following command in your terminal:
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

Afterwards, try activating the virtual environment again:
venv\Scripts\activate

Features
  • Topic Modeling: Ideal for analyzing extensive text data.
  • Word Cloud: Visualizes the most frequent terms in datasets, best suited for shorter surveys.
  • More to follow
Further prerequisites
Before using this tool, please ensure the following steps are completed to set up your environment:
Install and Set Up Required Libraries
  1. R and Python: Ensure both R and Python are installed on your computer. Download them from their official websites if necessary.
  2. Library Installation:
    • R Libraries: Open your R console, navigate to the directory containing requirements.R, and execute source('requirements.R').
    • Python Libraries: Open a command prompt or terminal, navigate to the directory containing requirements.txt, and execute pip install -r requirements.txt.
Update Script Paths
  • Verify that the TreeTagger tool is correctly installed and its path is appropriately set in both R scripts for text processing. Search for the term "teamIR" in the scripts to identify and update these paths.
How to Run the Tool
Starting the Application
  • Open topic_modeling_app.py in your Python IDE (like IDLE or PyCharm) using the file browser.
Using the Application
  • The UI is designed to be user-friendly:

    • Use the Word Cloud option for shorter surveys to visualize key terms.
    • Use the Topic Modeling option for detailed analysis of more complex text data.
  • Once the analysis is complete, the tool automatically saves the results in an Excel file in the same directory as the script.
Getting Help
If you encounter any issues or need further assistance, please feel free to contact amir.khodaie@ru.nl
About
Text analysis
Resources
Readme
Activity
Stars
0 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Topic Modeling Tool
This tool is designed to perform topic modeling and other various text analysis on textual data using R for core analysis and a Python-based user interface (UI) built with the Tkinter library. It is particularly effective with long textual responses and provides visual aids through word clouds for shorter surveys.
Very quick steps
python setup_env.py
venv\Scripts\activate
python topic_modeling_app.py

Quick steps
  1. First, run the following script: setup_env.py
  2. Second, activate your virtual environment with the following command: venv\Scripts\activate
  3. Third, run the last script: topic_modeling_app.py
Execution Policy Issues?
Are you running to any issues regarding Execution Policy? You can temporarily bypass the restriction for the current PowerShell session by running the following command in your terminal:
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

Afterwards, try activating the virtual environment again:
venv\Scripts\activate

Features
  • Topic Modeling: Ideal for analyzing extensive text data.
  • Word Cloud: Visualizes the most frequent terms in datasets, best suited for shorter surveys.
  • More to follow
Further prerequisites
Before using this tool, please ensure the following steps are completed to set up your environment:
Install and Set Up Required Libraries
  1. R and Python: Ensure both R and Python are installed on your computer. Download them from their official websites if necessary.
  2. Library Installation:
    • R Libraries: Open your R console, navigate to the directory containing requirements.R, and execute source('requirements.R').
    • Python Libraries: Open a command prompt or terminal, navigate to the directory containing requirements.txt, and execute pip install -r requirements.txt.
Update Script Paths
  • Verify that the TreeTagger tool is correctly installed and its path is appropriately set in both R scripts for text processing. Search for the term "teamIR" in the scripts to identify and update these paths.
How to Run the Tool
Starting the Application
  • Open topic_modeling_app.py in your Python IDE (like IDLE or PyCharm) using the file browser.
Using the Application
  • The UI is designed to be user-friendly:

    • Use the Word Cloud option for shorter surveys to visualize key terms.
    • Use the Topic Modeling option for detailed analysis of more complex text data.
  • Once the analysis is complete, the tool automatically saves the results in an Excel file in the same directory as the script.
Getting Help
If you encounter any issues or need further assistance, please feel free to contact amir.khodaie@ru.nl
About
Text analysis
Resources
Readme
Activity
Stars
0 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Topic Modeling Tool
This tool is designed to perform topic modeling and other various text analysis on textual data using R for core analysis and a Python-based user interface (UI) built with the Tkinter library. It is particularly effective with long textual responses and provides visual aids through word clouds for shorter surveys.
Very quick steps
python setup_env.py
venv\Scripts\activate
python topic_modeling_app.py

Quick steps
  1. First, run the following script: setup_env.py
  2. Second, activate your virtual environment with the following command: venv\Scripts\activate
  3. Third, run the last script: topic_modeling_app.py
Execution Policy Issues?
Are you running to any issues regarding Execution Policy? You can temporarily bypass the restriction for the current PowerShell session by running the following command in your terminal:
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

Afterwards, try activating the virtual environment again:
venv\Scripts\activate

Features
  • Topic Modeling: Ideal for analyzing extensive text data.
  • Word Cloud: Visualizes the most frequent terms in datasets, best suited for shorter surveys.
  • More to follow
Further prerequisites
Before using this tool, please ensure the following steps are completed to set up your environment:
Install and Set Up Required Libraries
  1. R and Python: Ensure both R and Python are installed on your computer. Download them from their official websites if necessary.
  2. Library Installation:
    • R Libraries: Open your R console, navigate to the directory containing requirements.R, and execute source('requirements.R').
    • Python Libraries: Open a command prompt or terminal, navigate to the directory containing requirements.txt, and execute pip install -r requirements.txt.
Update Script Paths
  • Verify that the TreeTagger tool is correctly installed and its path is appropriately set in both R scripts for text processing. Search for the term "teamIR" in the scripts to identify and update these paths.
How to Run the Tool
Starting the Application
  • Open topic_modeling_app.py in your Python IDE (like IDLE or PyCharm) using the file browser.
Using the Application
  • The UI is designed to be user-friendly:

    • Use the Word Cloud option for shorter surveys to visualize key terms.
    • Use the Topic Modeling option for detailed analysis of more complex text data.
  • Once the analysis is complete, the tool automatically saves the results in an Excel file in the same directory as the script.
Getting Help
If you encounter any issues or need further assistance, please feel free to contact amir.khodaie@ru.nl
About
Text analysis
Resources
Readme
Activity
Stars
0 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Topic Modeling Tool
This tool is designed to perform topic modeling and other various text analysis on textual data using R for core analysis and a Python-based user interface (UI) built with the Tkinter library. It is particularly effective with long textual responses and provides visual aids through word clouds for shorter surveys.
Very quick steps
python setup_env.py
venv\Scripts\activate
python topic_modeling_app.py

Quick steps
  1. First, run the following script: setup_env.py
  2. Second, activate your virtual environment with the following command: venv\Scripts\activate
  3. Third, run the last script: topic_modeling_app.py
Execution Policy Issues?
Are you running to any issues regarding Execution Policy? You can temporarily bypass the restriction for the current PowerShell session by running the following command in your terminal:
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

Afterwards, try activating the virtual environment again:
venv\Scripts\activate

Features
  • Topic Modeling: Ideal for analyzing extensive text data.
  • Word Cloud: Visualizes the most frequent terms in datasets, best suited for shorter surveys.
  • More to follow
Further prerequisites
Before using this tool, please ensure the following steps are completed to set up your environment:
Install and Set Up Required Libraries
  1. R and Python: Ensure both R and Python are installed on your computer. Download them from their official websites if necessary.
  2. Library Installation:
    • R Libraries: Open your R console, navigate to the directory containing requirements.R, and execute source('requirements.R').
    • Python Libraries: Open a command prompt or terminal, navigate to the directory containing requirements.txt, and execute pip install -r requirements.txt.
Update Script Paths
  • Verify that the TreeTagger tool is correctly installed and its path is appropriately set in both R scripts for text processing. Search for the term "teamIR" in the scripts to identify and update these paths.
How to Run the Tool
Starting the Application
  • Open topic_modeling_app.py in your Python IDE (like IDLE or PyCharm) using the file browser.
Using the Application
  • The UI is designed to be user-friendly:

    • Use the Word Cloud option for shorter surveys to visualize key terms.
    • Use the Topic Modeling option for detailed analysis of more complex text data.
  • Once the analysis is complete, the tool automatically saves the results in an Excel file in the same directory as the script.
Getting Help
If you encounter any issues or need further assistance, please feel free to contact amir.khodaie@ru.nl
About
Text analysis
Resources
Readme
Activity
Stars
0 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Topic Modeling Tool
This tool is designed to perform topic modeling and other various text analysis on textual data using R for core analysis and a Python-based user interface (UI) built with the Tkinter library. It is particularly effective with long textual responses and provides visual aids through word clouds for shorter surveys.
Very quick steps
python setup_env.py
venv\Scripts\activate
python topic_modeling_app.py

Quick steps
  1. First, run the following script: setup_env.py
  2. Second, activate your virtual environment with the following command: venv\Scripts\activate
  3. Third, run the last script: topic_modeling_app.py
Execution Policy Issues?
Are you running to any issues regarding Execution Policy? You can temporarily bypass the restriction for the current PowerShell session by running the following command in your terminal:
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

Afterwards, try activating the virtual environment again:
venv\Scripts\activate

Features
  • Topic Modeling: Ideal for analyzing extensive text data.
  • Word Cloud: Visualizes the most frequent terms in datasets, best suited for shorter surveys.
  • More to follow
Further prerequisites
Before using this tool, please ensure the following steps are completed to set up your environment:
Install and Set Up Required Libraries
  1. R and Python: Ensure both R and Python are installed on your computer. Download them from their official websites if necessary.
  2. Library Installation:
    • R Libraries: Open your R console, navigate to the directory containing requirements.R, and execute source('requirements.R').
    • Python Libraries: Open a command prompt or terminal, navigate to the directory containing requirements.txt, and execute pip install -r requirements.txt.
Update Script Paths
  • Verify that the TreeTagger tool is correctly installed and its path is appropriately set in both R scripts for text processing. Search for the term "teamIR" in the scripts to identify and update these paths.
How to Run the Tool
Starting the Application
  • Open topic_modeling_app.py in your Python IDE (like IDLE or PyCharm) using the file browser.
Using the Application
  • The UI is designed to be user-friendly:

    • Use the Word Cloud option for shorter surveys to visualize key terms.
    • Use the Topic Modeling option for detailed analysis of more complex text data.
  • Once the analysis is complete, the tool automatically saves the results in an Excel file in the same directory as the script.
Getting Help
If you encounter any issues or need further assistance, please feel free to contact amir.khodaie@ru.nl
About
Text analysis
Resources
Readme
Activity
Stars
0 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Topic Modeling Tool
This tool is designed to perform topic modeling and other various text analysis on textual data using R for core analysis and a Python-based user interface (UI) built with the Tkinter library. It is particularly effective with long textual responses and provides visual aids through word clouds for shorter surveys.
Very quick steps
python setup_env.py
venv\Scripts\activate
python topic_modeling_app.py

Quick steps
  1. First, run the following script: setup_env.py
  2. Second, activate your virtual environment with the following command: venv\Scripts\activate
  3. Third, run the last script: topic_modeling_app.py
Execution Policy Issues?
Are you running to any issues regarding Execution Policy? You can temporarily bypass the restriction for the current PowerShell session by running the following command in your terminal:
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

Afterwards, try activating the virtual environment again:
venv\Scripts\activate

Features
  • Topic Modeling: Ideal for analyzing extensive text data.
  • Word Cloud: Visualizes the most frequent terms in datasets, best suited for shorter surveys.
  • More to follow
Further prerequisites
Before using this tool, please ensure the following steps are completed to set up your environment:
Install and Set Up Required Libraries
  1. R and Python: Ensure both R and Python are installed on your computer. Download them from their official websites if necessary.
  2. Library Installation:
    • R Libraries: Open your R console, navigate to the directory containing requirements.R, and execute source('requirements.R').
    • Python Libraries: Open a command prompt or terminal, navigate to the directory containing requirements.txt, and execute pip install -r requirements.txt.
Update Script Paths
  • Verify that the TreeTagger tool is correctly installed and its path is appropriately set in both R scripts for text processing. Search for the term "teamIR" in the scripts to identify and update these paths.
How to Run the Tool
Starting the Application
  • Open topic_modeling_app.py in your Python IDE (like IDLE or PyCharm) using the file browser.
Using the Application
  • The UI is designed to be user-friendly:

    • Use the Word Cloud option for shorter surveys to visualize key terms.
    • Use the Topic Modeling option for detailed analysis of more complex text data.
  • Once the analysis is complete, the tool automatically saves the results in an Excel file in the same directory as the script.
Getting Help
If you encounter any issues or need further assistance, please feel free to contact amir.khodaie@ru.nl
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