Updated dataset in Sentiment Analysis - #11114

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Updated dataset from wikidetox to UCI Sentiment in Sentiment Analysis tutorial to improve model prediction, and updated to version 0.11.0.

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Fixes#7024
Fixes#10080
Fixes#10106
Fixes#11054

@JRAlexanderJRAlexander added doc-update 🚧 Hold for related PR Indicates a PR can only be merged when other related PRs are merged (see comments for links) labels Mar 6, 2019
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@JRAlexanderJRAlexander changed the title Updated dataset in Sentiment Analysis[WIP] Updated dataset in Sentiment AnalysisMar 6, 2019
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Nice work @JRAlexander . Minor changes needed.

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Thanks, @luisquintanilla! I believe I've addressed your concerns.

@JRAlexanderJRAlexander changed the title [WIP] Updated dataset in Sentiment AnalysisUpdated dataset in Sentiment AnalysisMar 7, 2019
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Thanks for making the changes. One last change. Update the name of the data file to match that of the code sample yelp_labelled.txt.

Comment threaddocs/machine-learning/tutorials/sentiment-analysis.md Outdated
1. Download the [Wikipedia detox-250-line-data.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-data.tsv) and the [wikipedia-detox-250-line-test.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-test.tsv) data sets and save them to the *Data* folder previously created. The first dataset trains the machine learning model and the second can be used to evaluate how accurate your model is.
1. Download [The UCI Sentiment Labeled Sentences dataset zip file (see citations in the following note)](https://archive.ics.uci.edu/ml/machine-learning-databases/00331/sentiment%20labelled%20sentences.zip), and unzip.

2. Copy the `yelp_labeled.txt` file into the *Data* directory you created.

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Change file name to match the name in the code sample -> yelp_labelled.txt

@JRAlexanderJRAlexanderMar 8, 2019

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Good catch.

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JRAlexander merged commit d21e422 into dotnet:masterMar 8, 2019
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Updated dataset from wikidetox to UCI Sentiment in Sentiment Analysis tutorial to improve model prediction, and updated to version 0.11.0.
Related Code Sample
Internal Review Link
Fixes#7024
Fixes#10080
Fixes#10106
Fixes#11054
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Nice work @JRAlexander . Minor changes needed.
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Thanks, @luisquintanilla! I believe I've addressed your concerns.
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Thanks for making the changes. One last change. Update the name of the data file to match that of the code sample yelp_labelled.txt.
Comment threaddocs/machine-learning/tutorials/sentiment-analysis.md
Outdated

1. Download the [Wikipedia detox-250-line-data.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-data.tsv) and the [wikipedia-detox-250-line-test.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-test.tsv) data sets and save them to the *Data* folder previously created. The first dataset trains the machine learning model and the second can be used to evaluate how accurate your model is.
1. Download [The UCI Sentiment Labeled Sentences dataset zip file (see citations in the following note)](https://archive.ics.uci.edu/ml/machine-learning-databases/00331/sentiment%20labelled%20sentences.zip), and unzip.

2. Copy the `yelp_labeled.txt` file into the *Data* directory you created.
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Change file name to match the name in the code sample -> yelp_labelled.txt
@JRAlexanderJRAlexanderMar 8, 2019

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Good catch.
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, 'i');
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Updated dataset in Sentiment Analysis - #11114
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Updated dataset from wikidetox to UCI Sentiment in Sentiment Analysis tutorial to improve model prediction, and updated to version 0.11.0.
Related Code Sample
Internal Review Link
Fixes#7024
Fixes#10080
Fixes#10106
Fixes#11054
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Nice work @JRAlexander . Minor changes needed.
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Thanks, @luisquintanilla! I believe I've addressed your concerns.
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Thanks for making the changes. One last change. Update the name of the data file to match that of the code sample yelp_labelled.txt.
Comment threaddocs/machine-learning/tutorials/sentiment-analysis.md
Outdated

1. Download the [Wikipedia detox-250-line-data.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-data.tsv) and the [wikipedia-detox-250-line-test.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-test.tsv) data sets and save them to the *Data* folder previously created. The first dataset trains the machine learning model and the second can be used to evaluate how accurate your model is.
1. Download [The UCI Sentiment Labeled Sentences dataset zip file (see citations in the following note)](https://archive.ics.uci.edu/ml/machine-learning-databases/00331/sentiment%20labelled%20sentences.zip), and unzip.

2. Copy the `yelp_labeled.txt` file into the *Data* directory you created.
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Change file name to match the name in the code sample -> yelp_labelled.txt
@JRAlexanderJRAlexanderMar 8, 2019

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Good catch.
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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");
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Updated dataset from wikidetox to UCI Sentiment in Sentiment Analysis tutorial to improve model prediction, and updated to version 0.11.0.
Related Code Sample
Internal Review Link
Fixes#7024
Fixes#10080
Fixes#10106
Fixes#11054
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Nice work @JRAlexander . Minor changes needed.
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Thanks for making the changes. One last change. Update the name of the data file to match that of the code sample yelp_labelled.txt.
Comment threaddocs/machine-learning/tutorials/sentiment-analysis.md
Outdated

1. Download the [Wikipedia detox-250-line-data.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-data.tsv) and the [wikipedia-detox-250-line-test.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-test.tsv) data sets and save them to the *Data* folder previously created. The first dataset trains the machine learning model and the second can be used to evaluate how accurate your model is.
1. Download [The UCI Sentiment Labeled Sentences dataset zip file (see citations in the following note)](https://archive.ics.uci.edu/ml/machine-learning-databases/00331/sentiment%20labelled%20sentences.zip), and unzip.

2. Copy the `yelp_labeled.txt` file into the *Data* directory you created.
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Change file name to match the name in the code sample -> yelp_labelled.txt
@JRAlexanderJRAlexanderMar 8, 2019

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Good catch.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Updated dataset from wikidetox to UCI Sentiment in Sentiment Analysis tutorial to improve model prediction, and updated to version 0.11.0.
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Nice work @JRAlexander . Minor changes needed.
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Thanks for making the changes. One last change. Update the name of the data file to match that of the code sample yelp_labelled.txt.
Comment threaddocs/machine-learning/tutorials/sentiment-analysis.md
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1. Download the [Wikipedia detox-250-line-data.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-data.tsv) and the [wikipedia-detox-250-line-test.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-test.tsv) data sets and save them to the *Data* folder previously created. The first dataset trains the machine learning model and the second can be used to evaluate how accurate your model is.
1. Download [The UCI Sentiment Labeled Sentences dataset zip file (see citations in the following note)](https://archive.ics.uci.edu/ml/machine-learning-databases/00331/sentiment%20labelled%20sentences.zip), and unzip.

2. Copy the `yelp_labeled.txt` file into the *Data* directory you created.
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Change file name to match the name in the code sample -> yelp_labelled.txt
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Good catch.
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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); }
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Updated dataset from wikidetox to UCI Sentiment in Sentiment Analysis tutorial to improve model prediction, and updated to version 0.11.0.
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Fixes#10080
Fixes#10106
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Nice work @JRAlexander . Minor changes needed.
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Thanks for making the changes. One last change. Update the name of the data file to match that of the code sample yelp_labelled.txt.
Comment threaddocs/machine-learning/tutorials/sentiment-analysis.md
Outdated

1. Download the [Wikipedia detox-250-line-data.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-data.tsv) and the [wikipedia-detox-250-line-test.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-test.tsv) data sets and save them to the *Data* folder previously created. The first dataset trains the machine learning model and the second can be used to evaluate how accurate your model is.
1. Download [The UCI Sentiment Labeled Sentences dataset zip file (see citations in the following note)](https://archive.ics.uci.edu/ml/machine-learning-databases/00331/sentiment%20labelled%20sentences.zip), and unzip.

2. Copy the `yelp_labeled.txt` file into the *Data* directory you created.
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Change file name to match the name in the code sample -> yelp_labelled.txt
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Good catch.
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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");
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Updated dataset from wikidetox to UCI Sentiment in Sentiment Analysis tutorial to improve model prediction, and updated to version 0.11.0.
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Fixes#10080
Fixes#10106
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Nice work @JRAlexander . Minor changes needed.
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Thanks for making the changes. One last change. Update the name of the data file to match that of the code sample yelp_labelled.txt.
Comment threaddocs/machine-learning/tutorials/sentiment-analysis.md
Outdated

1. Download the [Wikipedia detox-250-line-data.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-data.tsv) and the [wikipedia-detox-250-line-test.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-test.tsv) data sets and save them to the *Data* folder previously created. The first dataset trains the machine learning model and the second can be used to evaluate how accurate your model is.
1. Download [The UCI Sentiment Labeled Sentences dataset zip file (see citations in the following note)](https://archive.ics.uci.edu/ml/machine-learning-databases/00331/sentiment%20labelled%20sentences.zip), and unzip.

2. Copy the `yelp_labeled.txt` file into the *Data* directory you created.
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Change file name to match the name in the code sample -> yelp_labelled.txt
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Good catch.
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, 'i');
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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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Updated dataset from wikidetox to UCI Sentiment in Sentiment Analysis tutorial to improve model prediction, and updated to version 0.11.0.
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Fixes#10080
Fixes#10106
Fixes#11054
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Nice work @JRAlexander . Minor changes needed.
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Thanks for making the changes. One last change. Update the name of the data file to match that of the code sample yelp_labelled.txt.
Comment threaddocs/machine-learning/tutorials/sentiment-analysis.md
Outdated

1. Download the [Wikipedia detox-250-line-data.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-data.tsv) and the [wikipedia-detox-250-line-test.tsv](https://github.com/dotnet/machinelearning/blob/master/test/data/wikipedia-detox-250-line-test.tsv) data sets and save them to the *Data* folder previously created. The first dataset trains the machine learning model and the second can be used to evaluate how accurate your model is.
1. Download [The UCI Sentiment Labeled Sentences dataset zip file (see citations in the following note)](https://archive.ics.uci.edu/ml/machine-learning-databases/00331/sentiment%20labelled%20sentences.zip), and unzip.

2. Copy the `yelp_labeled.txt` file into the *Data* directory you created.
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Change file name to match the name in the code sample -> yelp_labelled.txt
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Good catch.
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