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Sentimental

Python port of github.com/Wobot/Sentimental with some improvements

A simple dictionary-based sentiment analysis system with Russian language support.

Installation

pip install -U git+https://github.com/text-machine-lab/sentimental.git

or

pip install -U git+ssh://git@github.com/text-machine-lab/sentimental.git

Usage

fromsentimentalimportSentimentalsent=Sentimental()
sentence='Today is a good day!'result=sent.analyze(sentence)

The result is a dictionary with four fields:

{'negative': 0.0, 'positive': 3.0, 'score': 3.0, 'comparative': 0.6}

The filed score reflects the overall sentiment of the input data, and the comparative field is normalized by the length of the input, so it can be used to compare the sentiment of different texts.

Citation

If you've found this project useful, please cite the following paper:

@inproceedings{rumshisky2017combining,
title={Combining network and language indicators for tracking conflict intensity},
author={Rumshisky, Anna and Gronas, Mikhail and Potash, Peter and Dubov, Mikhail and Romanov, Alexey and Kulshreshtha, Saurabh and Gribov, Alex},
booktitle={International Conference on Social Informatics},
pages={391--404},
year={2017},
organization={Springer}
}

About

A simple dictionary-based sentiment analysis system with Russian language support

Resources

Readme
Activity
Custom properties

Stars

28 stars

Watchers

16 watching

Forks

14 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" + '

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Sentimental
Python port of github.com/Wobot/Sentimental
with some improvements
A simple dictionary-based sentiment analysis system with Russian language support.
Installation
pip install -U git+https://github.com/text-machine-lab/sentimental.git

or
pip install -U git+ssh://git@github.com/text-machine-lab/sentimental.git

Usage
fromsentimentalimportSentimentalsent=Sentimental()
sentence='Today is a good day!'result=sent.analyze(sentence)
The result is a dictionary with four fields:
{'negative': 0.0, 'positive': 3.0, 'score': 3.0, 'comparative': 0.6}
The filed score reflects the overall sentiment of the input data,
and the comparative field is normalized by the length of the input,
so it can be used to compare the sentiment of different texts.
Citation
If you've found this project useful, please cite the following paper:
@inproceedings{rumshisky2017combining,
title={Combining network and language indicators for tracking conflict intensity},
author={Rumshisky, Anna and Gronas, Mikhail and Potash, Peter and Dubov, Mikhail and Romanov, Alexey and Kulshreshtha, Saurabh and Gribov, Alex},
booktitle={International Conference on Social Informatics},
pages={391--404},
year={2017},
organization={Springer}
}

About
A simple dictionary-based sentiment analysis system with Russian language support
Resources
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Activity
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Stars
28 stars
Watchers
16 watching
Forks
14 forks
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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Repository files navigation
Sentimental
Python port of github.com/Wobot/Sentimental
with some improvements
A simple dictionary-based sentiment analysis system with Russian language support.
Installation
pip install -U git+https://github.com/text-machine-lab/sentimental.git

or
pip install -U git+ssh://git@github.com/text-machine-lab/sentimental.git

Usage
fromsentimentalimportSentimentalsent=Sentimental()
sentence='Today is a good day!'result=sent.analyze(sentence)
The result is a dictionary with four fields:
{'negative': 0.0, 'positive': 3.0, 'score': 3.0, 'comparative': 0.6}
The filed score reflects the overall sentiment of the input data,
and the comparative field is normalized by the length of the input,
so it can be used to compare the sentiment of different texts.
Citation
If you've found this project useful, please cite the following paper:
@inproceedings{rumshisky2017combining,
title={Combining network and language indicators for tracking conflict intensity},
author={Rumshisky, Anna and Gronas, Mikhail and Potash, Peter and Dubov, Mikhail and Romanov, Alexey and Kulshreshtha, Saurabh and Gribov, Alex},
booktitle={International Conference on Social Informatics},
pages={391--404},
year={2017},
organization={Springer}
}

About
A simple dictionary-based sentiment analysis system with Russian language support
Resources
Readme
Activity
Custom properties
Stars
28 stars
Watchers
16 watching
Forks
14 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('^' + ".*" + '

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Repository files navigation
Sentimental
Python port of github.com/Wobot/Sentimental
with some improvements
A simple dictionary-based sentiment analysis system with Russian language support.
Installation
pip install -U git+https://github.com/text-machine-lab/sentimental.git

or
pip install -U git+ssh://git@github.com/text-machine-lab/sentimental.git

Usage
fromsentimentalimportSentimentalsent=Sentimental()
sentence='Today is a good day!'result=sent.analyze(sentence)
The result is a dictionary with four fields:
{'negative': 0.0, 'positive': 3.0, 'score': 3.0, 'comparative': 0.6}
The filed score reflects the overall sentiment of the input data,
and the comparative field is normalized by the length of the input,
so it can be used to compare the sentiment of different texts.
Citation
If you've found this project useful, please cite the following paper:
@inproceedings{rumshisky2017combining,
title={Combining network and language indicators for tracking conflict intensity},
author={Rumshisky, Anna and Gronas, Mikhail and Potash, Peter and Dubov, Mikhail and Romanov, Alexey and Kulshreshtha, Saurabh and Gribov, Alex},
booktitle={International Conference on Social Informatics},
pages={391--404},
year={2017},
organization={Springer}
}

About
A simple dictionary-based sentiment analysis system with Russian language support
Resources
Readme
Activity
Custom properties
Stars
28 stars
Watchers
16 watching
Forks
14 forks
Report repository
Releases
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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" + '

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Repository files navigation
Sentimental
Python port of github.com/Wobot/Sentimental
with some improvements
A simple dictionary-based sentiment analysis system with Russian language support.
Installation
pip install -U git+https://github.com/text-machine-lab/sentimental.git

or
pip install -U git+ssh://git@github.com/text-machine-lab/sentimental.git

Usage
fromsentimentalimportSentimentalsent=Sentimental()
sentence='Today is a good day!'result=sent.analyze(sentence)
The result is a dictionary with four fields:
{'negative': 0.0, 'positive': 3.0, 'score': 3.0, 'comparative': 0.6}
The filed score reflects the overall sentiment of the input data,
and the comparative field is normalized by the length of the input,
so it can be used to compare the sentiment of different texts.
Citation
If you've found this project useful, please cite the following paper:
@inproceedings{rumshisky2017combining,
title={Combining network and language indicators for tracking conflict intensity},
author={Rumshisky, Anna and Gronas, Mikhail and Potash, Peter and Dubov, Mikhail and Romanov, Alexey and Kulshreshtha, Saurabh and Gribov, Alex},
booktitle={International Conference on Social Informatics},
pages={391--404},
year={2017},
organization={Springer}
}

About
A simple dictionary-based sentiment analysis system with Russian language support
Resources
Readme
Activity
Custom properties
Stars
28 stars
Watchers
16 watching
Forks
14 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('^' + ".*" + '

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README.md
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Repository files navigation
Sentimental
Python port of github.com/Wobot/Sentimental
with some improvements
A simple dictionary-based sentiment analysis system with Russian language support.
Installation
pip install -U git+https://github.com/text-machine-lab/sentimental.git

or
pip install -U git+ssh://git@github.com/text-machine-lab/sentimental.git

Usage
fromsentimentalimportSentimentalsent=Sentimental()
sentence='Today is a good day!'result=sent.analyze(sentence)
The result is a dictionary with four fields:
{'negative': 0.0, 'positive': 3.0, 'score': 3.0, 'comparative': 0.6}
The filed score reflects the overall sentiment of the input data,
and the comparative field is normalized by the length of the input,
so it can be used to compare the sentiment of different texts.
Citation
If you've found this project useful, please cite the following paper:
@inproceedings{rumshisky2017combining,
title={Combining network and language indicators for tracking conflict intensity},
author={Rumshisky, Anna and Gronas, Mikhail and Potash, Peter and Dubov, Mikhail and Romanov, Alexey and Kulshreshtha, Saurabh and Gribov, Alex},
booktitle={International Conference on Social Informatics},
pages={391--404},
year={2017},
organization={Springer}
}

About
A simple dictionary-based sentiment analysis system with Russian language support
Resources
Readme
Activity
Custom properties
Stars
28 stars
Watchers
16 watching
Forks
14 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('^' + ".*" + '

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Repository files navigation
Sentimental
Python port of github.com/Wobot/Sentimental
with some improvements
A simple dictionary-based sentiment analysis system with Russian language support.
Installation
pip install -U git+https://github.com/text-machine-lab/sentimental.git

or
pip install -U git+ssh://git@github.com/text-machine-lab/sentimental.git

Usage
fromsentimentalimportSentimentalsent=Sentimental()
sentence='Today is a good day!'result=sent.analyze(sentence)
The result is a dictionary with four fields:
{'negative': 0.0, 'positive': 3.0, 'score': 3.0, 'comparative': 0.6}
The filed score reflects the overall sentiment of the input data,
and the comparative field is normalized by the length of the input,
so it can be used to compare the sentiment of different texts.
Citation
If you've found this project useful, please cite the following paper:
@inproceedings{rumshisky2017combining,
title={Combining network and language indicators for tracking conflict intensity},
author={Rumshisky, Anna and Gronas, Mikhail and Potash, Peter and Dubov, Mikhail and Romanov, Alexey and Kulshreshtha, Saurabh and Gribov, Alex},
booktitle={International Conference on Social Informatics},
pages={391--404},
year={2017},
organization={Springer}
}

About
A simple dictionary-based sentiment analysis system with Russian language support
Resources
Readme
Activity
Custom properties
Stars
28 stars
Watchers
16 watching
Forks
14 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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Repository files navigation
Sentimental
Python port of github.com/Wobot/Sentimental
with some improvements
A simple dictionary-based sentiment analysis system with Russian language support.
Installation
pip install -U git+https://github.com/text-machine-lab/sentimental.git

or
pip install -U git+ssh://git@github.com/text-machine-lab/sentimental.git

Usage
fromsentimentalimportSentimentalsent=Sentimental()
sentence='Today is a good day!'result=sent.analyze(sentence)
The result is a dictionary with four fields:
{'negative': 0.0, 'positive': 3.0, 'score': 3.0, 'comparative': 0.6}
The filed score reflects the overall sentiment of the input data,
and the comparative field is normalized by the length of the input,
so it can be used to compare the sentiment of different texts.
Citation
If you've found this project useful, please cite the following paper:
@inproceedings{rumshisky2017combining,
title={Combining network and language indicators for tracking conflict intensity},
author={Rumshisky, Anna and Gronas, Mikhail and Potash, Peter and Dubov, Mikhail and Romanov, Alexey and Kulshreshtha, Saurabh and Gribov, Alex},
booktitle={International Conference on Social Informatics},
pages={391--404},
year={2017},
organization={Springer}
}

About
A simple dictionary-based sentiment analysis system with Russian language support
Resources
Readme
Activity
Custom properties
Stars
28 stars
Watchers
16 watching
Forks
14 forks
Report repository
Releases
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Used by
Contributors
Languages