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Report.pdf
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Emotion Recognition in Arabic Tweets

Team 8:

Breif overview

this project compares the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets. Our approach compares 7 BERT models:

  1. AraBERT-base
  2. AraBERT-Twitter-base
  3. MARBERT
  4. CAMELBERT-DA
  5. CAMELBERT-MSA
  6. CAMELBERT-MSA-16th
  7. mBERT

All the experiments are avaialble in Experiments Directory. The model that achieved the highest accuracy is MARBERT. Its notebook is available in MARBERT.ipynb with some added inference examples in the end.

If you wish to experiment with other models or redo an experiment, you can simply open the Jupyter notebook in Google Colab or Kaggle, connect to a GPU, and paste the desired model path from Hugging Face into the model_path parameter. Run all the cells if you don't wish to adjust any more parameters and dataset will be automatically imported, processed, and the model will be fine-tuned on the dataset and evaluated. Check the Report for more details :)

About

We compare the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.

Topics

arabic-nlpbertemotion-classificationmarbertnlptweetstweets-classification

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Readme
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1 watching

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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" + '

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Repository files navigation
Emotion Recognition in Arabic Tweets
Team 8:
Breif overview
this project compares the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Our approach compares 7 BERT models:
  1. AraBERT-base
  2. AraBERT-Twitter-base
  3. MARBERT
  4. CAMELBERT-DA
  5. CAMELBERT-MSA
  6. CAMELBERT-MSA-16th
  7. mBERT
All the experiments are avaialble in Experiments Directory. The model that achieved the highest accuracy is MARBERT. Its notebook is available in MARBERT.ipynb with some added inference examples in the end.
If you wish to experiment with other models or redo an experiment, you can simply open the Jupyter notebook in Google Colab or Kaggle, connect to a GPU, and paste the desired model path from Hugging Face into the model_path parameter. Run all the cells if you don't wish to adjust any more parameters and dataset will be automatically imported, processed, and the model will be fine-tuned on the dataset and evaluated. Check the Report for more details :)
About
We compare the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Topics
arabic-nlpbertemotion-classificationmarbertnlptweetstweets-classification
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
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, '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('^' + ".*" + '

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NLP_M2_Slides.pdf
NLP_M2_Slides.pdf
README.md
README.md
Report.pdf
Report.pdf
Repository files navigation
Emotion Recognition in Arabic Tweets
Team 8:
Breif overview
this project compares the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Our approach compares 7 BERT models:
  1. AraBERT-base
  2. AraBERT-Twitter-base
  3. MARBERT
  4. CAMELBERT-DA
  5. CAMELBERT-MSA
  6. CAMELBERT-MSA-16th
  7. mBERT
All the experiments are avaialble in Experiments Directory. The model that achieved the highest accuracy is MARBERT. Its notebook is available in MARBERT.ipynb with some added inference examples in the end.
If you wish to experiment with other models or redo an experiment, you can simply open the Jupyter notebook in Google Colab or Kaggle, connect to a GPU, and paste the desired model path from Hugging Face into the model_path parameter. Run all the cells if you don't wish to adjust any more parameters and dataset will be automatically imported, processed, and the model will be fine-tuned on the dataset and evaluated. Check the Report for more details :)
About
We compare the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Topics
arabic-nlpbertemotion-classificationmarbertnlptweetstweets-classification
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
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('^' + ".*" + '

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NLP_M2_Slides.pdf
README.md
README.md
Report.pdf
Report.pdf
Repository files navigation
Emotion Recognition in Arabic Tweets
Team 8:
Breif overview
this project compares the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Our approach compares 7 BERT models:
  1. AraBERT-base
  2. AraBERT-Twitter-base
  3. MARBERT
  4. CAMELBERT-DA
  5. CAMELBERT-MSA
  6. CAMELBERT-MSA-16th
  7. mBERT
All the experiments are avaialble in Experiments Directory. The model that achieved the highest accuracy is MARBERT. Its notebook is available in MARBERT.ipynb with some added inference examples in the end.
If you wish to experiment with other models or redo an experiment, you can simply open the Jupyter notebook in Google Colab or Kaggle, connect to a GPU, and paste the desired model path from Hugging Face into the model_path parameter. Run all the cells if you don't wish to adjust any more parameters and dataset will be automatically imported, processed, and the model will be fine-tuned on the dataset and evaluated. Check the Report for more details :)
About
We compare the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Topics
arabic-nlpbertemotion-classificationmarbertnlptweetstweets-classification
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
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, '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" + '

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NameName
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MARBERT.ipynb
MARBERT.ipynb
NLP_M2_Slides.pdf
NLP_M2_Slides.pdf
README.md
README.md
Report.pdf
Report.pdf
Repository files navigation
Emotion Recognition in Arabic Tweets
Team 8:
Breif overview
this project compares the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Our approach compares 7 BERT models:
  1. AraBERT-base
  2. AraBERT-Twitter-base
  3. MARBERT
  4. CAMELBERT-DA
  5. CAMELBERT-MSA
  6. CAMELBERT-MSA-16th
  7. mBERT
All the experiments are avaialble in Experiments Directory. The model that achieved the highest accuracy is MARBERT. Its notebook is available in MARBERT.ipynb with some added inference examples in the end.
If you wish to experiment with other models or redo an experiment, you can simply open the Jupyter notebook in Google Colab or Kaggle, connect to a GPU, and paste the desired model path from Hugging Face into the model_path parameter. Run all the cells if you don't wish to adjust any more parameters and dataset will be automatically imported, processed, and the model will be fine-tuned on the dataset and evaluated. Check the Report for more details :)
About
We compare the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Topics
arabic-nlpbertemotion-classificationmarbertnlptweetstweets-classification
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
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('^' + ".*" + '

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NLP_M2_Slides.pdf
README.md
README.md
Report.pdf
Report.pdf
Repository files navigation
Emotion Recognition in Arabic Tweets
Team 8:
Breif overview
this project compares the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Our approach compares 7 BERT models:
  1. AraBERT-base
  2. AraBERT-Twitter-base
  3. MARBERT
  4. CAMELBERT-DA
  5. CAMELBERT-MSA
  6. CAMELBERT-MSA-16th
  7. mBERT
All the experiments are avaialble in Experiments Directory. The model that achieved the highest accuracy is MARBERT. Its notebook is available in MARBERT.ipynb with some added inference examples in the end.
If you wish to experiment with other models or redo an experiment, you can simply open the Jupyter notebook in Google Colab or Kaggle, connect to a GPU, and paste the desired model path from Hugging Face into the model_path parameter. Run all the cells if you don't wish to adjust any more parameters and dataset will be automatically imported, processed, and the model will be fine-tuned on the dataset and evaluated. Check the Report for more details :)
About
We compare the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Topics
arabic-nlpbertemotion-classificationmarbertnlptweetstweets-classification
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
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('^' + ".*" + '

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NLP_M2_Slides.pdf
README.md
README.md
Report.pdf
Report.pdf
Repository files navigation
Emotion Recognition in Arabic Tweets
Team 8:
Breif overview
this project compares the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Our approach compares 7 BERT models:
  1. AraBERT-base
  2. AraBERT-Twitter-base
  3. MARBERT
  4. CAMELBERT-DA
  5. CAMELBERT-MSA
  6. CAMELBERT-MSA-16th
  7. mBERT
All the experiments are avaialble in Experiments Directory. The model that achieved the highest accuracy is MARBERT. Its notebook is available in MARBERT.ipynb with some added inference examples in the end.
If you wish to experiment with other models or redo an experiment, you can simply open the Jupyter notebook in Google Colab or Kaggle, connect to a GPU, and paste the desired model path from Hugging Face into the model_path parameter. Run all the cells if you don't wish to adjust any more parameters and dataset will be automatically imported, processed, and the model will be fine-tuned on the dataset and evaluated. Check the Report for more details :)
About
We compare the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Topics
arabic-nlpbertemotion-classificationmarbertnlptweetstweets-classification
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
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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NLP_M2_Slides.pdf
README.md
README.md
Report.pdf
Report.pdf
Repository files navigation
Emotion Recognition in Arabic Tweets
Team 8:
Breif overview
this project compares the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Our approach compares 7 BERT models:
  1. AraBERT-base
  2. AraBERT-Twitter-base
  3. MARBERT
  4. CAMELBERT-DA
  5. CAMELBERT-MSA
  6. CAMELBERT-MSA-16th
  7. mBERT
All the experiments are avaialble in Experiments Directory. The model that achieved the highest accuracy is MARBERT. Its notebook is available in MARBERT.ipynb with some added inference examples in the end.
If you wish to experiment with other models or redo an experiment, you can simply open the Jupyter notebook in Google Colab or Kaggle, connect to a GPU, and paste the desired model path from Hugging Face into the model_path parameter. Run all the cells if you don't wish to adjust any more parameters and dataset will be automatically imported, processed, and the model will be fine-tuned on the dataset and evaluated. Check the Report for more details :)
About
We compare the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Topics
arabic-nlpbertemotion-classificationmarbertnlptweetstweets-classification
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
Contributors
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