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HashTagSplitter

A recursive python function to break down hashtags or compound words created by putting together multiple words

My implementation of the maximum matching algorithm used in Natural Language Processing (NLP) to split compound words or hashtags to multiple words.

Example Usage:

>>> split_hashtag_to_words_all_possibilities("edgeofentertainment")
[['edge', 'of', 'entertainment']]

>>> split_hashtag_to_words_all_possibilities("playtowin")
[['play', 'tow', 'in'], ['play', 'to', 'win']]

>>> split_hashtag_to_words_all_possibilities("datascience")
[['data', 'science'], ['da', 'ta', 'science']]

>>> split_hashtag_to_words_all_possibilities("superbowl")
[['superb', 'owl'], ['super', 'bowl'], ['sup', 'er', 'bowl']]

As can be seen from the examples, the output is totally based on the quality/vocabulary of the dictionary that is used.

TODO:

Build an n-gram model based on a corpus from nltk to order the possibilities by probability of occurence/usage and display only the top 3/5 most probable possibilities

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A Python function to break down hashtags or compound words created by putting together multiple words

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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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HashTagSplitter
A recursive python function to break down hashtags or compound words created by putting together multiple words
My implementation of the maximum matching algorithm used in Natural Language Processing (NLP) to split compound words or hashtags to multiple words.
Example Usage:
>>> split_hashtag_to_words_all_possibilities("edgeofentertainment")
[['edge', 'of', 'entertainment']]
>>> split_hashtag_to_words_all_possibilities("playtowin")
[['play', 'tow', 'in'], ['play', 'to', 'win']]
>>> split_hashtag_to_words_all_possibilities("datascience")
[['data', 'science'], ['da', 'ta', 'science']]
>>> split_hashtag_to_words_all_possibilities("superbowl")
[['superb', 'owl'], ['super', 'bowl'], ['sup', 'er', 'bowl']]
As can be seen from the examples, the output is totally based on the quality/vocabulary of the dictionary that is used.
TODO:
Build an n-gram model based on a corpus from nltk to order the possibilities by probability of occurence/usage and display only the top 3/5 most probable possibilities
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A Python function to break down hashtags or compound words created by putting together multiple words
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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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HashTagSplitter
A recursive python function to break down hashtags or compound words created by putting together multiple words
My implementation of the maximum matching algorithm used in Natural Language Processing (NLP) to split compound words or hashtags to multiple words.
Example Usage:
>>> split_hashtag_to_words_all_possibilities("edgeofentertainment")
[['edge', 'of', 'entertainment']]
>>> split_hashtag_to_words_all_possibilities("playtowin")
[['play', 'tow', 'in'], ['play', 'to', 'win']]
>>> split_hashtag_to_words_all_possibilities("datascience")
[['data', 'science'], ['da', 'ta', 'science']]
>>> split_hashtag_to_words_all_possibilities("superbowl")
[['superb', 'owl'], ['super', 'bowl'], ['sup', 'er', 'bowl']]
As can be seen from the examples, the output is totally based on the quality/vocabulary of the dictionary that is used.
TODO:
Build an n-gram model based on a corpus from nltk to order the possibilities by probability of occurence/usage and display only the top 3/5 most probable possibilities
About
A Python function to break down hashtags or compound words created by putting together multiple words
Resources
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1 watching
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0 forks
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, '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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HashTagSplitter
A recursive python function to break down hashtags or compound words created by putting together multiple words
My implementation of the maximum matching algorithm used in Natural Language Processing (NLP) to split compound words or hashtags to multiple words.
Example Usage:
>>> split_hashtag_to_words_all_possibilities("edgeofentertainment")
[['edge', 'of', 'entertainment']]
>>> split_hashtag_to_words_all_possibilities("playtowin")
[['play', 'tow', 'in'], ['play', 'to', 'win']]
>>> split_hashtag_to_words_all_possibilities("datascience")
[['data', 'science'], ['da', 'ta', 'science']]
>>> split_hashtag_to_words_all_possibilities("superbowl")
[['superb', 'owl'], ['super', 'bowl'], ['sup', 'er', 'bowl']]
As can be seen from the examples, the output is totally based on the quality/vocabulary of the dictionary that is used.
TODO:
Build an n-gram model based on a corpus from nltk to order the possibilities by probability of occurence/usage and display only the top 3/5 most probable possibilities
About
A Python function to break down hashtags or compound words created by putting together multiple words
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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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Repository files navigation
HashTagSplitter
A recursive python function to break down hashtags or compound words created by putting together multiple words
My implementation of the maximum matching algorithm used in Natural Language Processing (NLP) to split compound words or hashtags to multiple words.
Example Usage:
>>> split_hashtag_to_words_all_possibilities("edgeofentertainment")
[['edge', 'of', 'entertainment']]
>>> split_hashtag_to_words_all_possibilities("playtowin")
[['play', 'tow', 'in'], ['play', 'to', 'win']]
>>> split_hashtag_to_words_all_possibilities("datascience")
[['data', 'science'], ['da', 'ta', 'science']]
>>> split_hashtag_to_words_all_possibilities("superbowl")
[['superb', 'owl'], ['super', 'bowl'], ['sup', 'er', 'bowl']]
As can be seen from the examples, the output is totally based on the quality/vocabulary of the dictionary that is used.
TODO:
Build an n-gram model based on a corpus from nltk to order the possibilities by probability of occurence/usage and display only the top 3/5 most probable possibilities
About
A Python function to break down hashtags or compound words created by putting together multiple words
Resources
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1 watching
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, '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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HashTagSplitter
A recursive python function to break down hashtags or compound words created by putting together multiple words
My implementation of the maximum matching algorithm used in Natural Language Processing (NLP) to split compound words or hashtags to multiple words.
Example Usage:
>>> split_hashtag_to_words_all_possibilities("edgeofentertainment")
[['edge', 'of', 'entertainment']]
>>> split_hashtag_to_words_all_possibilities("playtowin")
[['play', 'tow', 'in'], ['play', 'to', 'win']]
>>> split_hashtag_to_words_all_possibilities("datascience")
[['data', 'science'], ['da', 'ta', 'science']]
>>> split_hashtag_to_words_all_possibilities("superbowl")
[['superb', 'owl'], ['super', 'bowl'], ['sup', 'er', 'bowl']]
As can be seen from the examples, the output is totally based on the quality/vocabulary of the dictionary that is used.
TODO:
Build an n-gram model based on a corpus from nltk to order the possibilities by probability of occurence/usage and display only the top 3/5 most probable possibilities
About
A Python function to break down hashtags or compound words created by putting together multiple words
Resources
Readme
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1 watching
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, '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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HashTagSplitter
A recursive python function to break down hashtags or compound words created by putting together multiple words
My implementation of the maximum matching algorithm used in Natural Language Processing (NLP) to split compound words or hashtags to multiple words.
Example Usage:
>>> split_hashtag_to_words_all_possibilities("edgeofentertainment")
[['edge', 'of', 'entertainment']]
>>> split_hashtag_to_words_all_possibilities("playtowin")
[['play', 'tow', 'in'], ['play', 'to', 'win']]
>>> split_hashtag_to_words_all_possibilities("datascience")
[['data', 'science'], ['da', 'ta', 'science']]
>>> split_hashtag_to_words_all_possibilities("superbowl")
[['superb', 'owl'], ['super', 'bowl'], ['sup', 'er', 'bowl']]
As can be seen from the examples, the output is totally based on the quality/vocabulary of the dictionary that is used.
TODO:
Build an n-gram model based on a corpus from nltk to order the possibilities by probability of occurence/usage and display only the top 3/5 most probable possibilities
About
A Python function to break down hashtags or compound words created by putting together multiple words
Resources
Readme
Activity
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1 watching
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, '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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HashTagSplitter
A recursive python function to break down hashtags or compound words created by putting together multiple words
My implementation of the maximum matching algorithm used in Natural Language Processing (NLP) to split compound words or hashtags to multiple words.
Example Usage:
>>> split_hashtag_to_words_all_possibilities("edgeofentertainment")
[['edge', 'of', 'entertainment']]
>>> split_hashtag_to_words_all_possibilities("playtowin")
[['play', 'tow', 'in'], ['play', 'to', 'win']]
>>> split_hashtag_to_words_all_possibilities("datascience")
[['data', 'science'], ['da', 'ta', 'science']]
>>> split_hashtag_to_words_all_possibilities("superbowl")
[['superb', 'owl'], ['super', 'bowl'], ['sup', 'er', 'bowl']]
As can be seen from the examples, the output is totally based on the quality/vocabulary of the dictionary that is used.
TODO:
Build an n-gram model based on a corpus from nltk to order the possibilities by probability of occurence/usage and display only the top 3/5 most probable possibilities
About
A Python function to break down hashtags or compound words created by putting together multiple words
Resources
Readme
Activity
Stars
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Watchers
1 watching
Forks
0 forks
Report repository
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