Update dataset usage and to version 0.11 for Sentiment Analysis - #680

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Update dataset usage and to version 0.11 for Sentiment Analysis #680
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Revising with UCI Sentiment Dataset for greater model accuracy, and updating to 0.11.0.

@JRAlexanderJRAlexander added the 🚧 Hold for related PR Indicates a PR can only be merged when other related PRs are merged (see comments for links) label Feb 28, 2019
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@JRAlexanderJRAlexander changed the title Update dataset usage for Sentiment AnalysisUpdate dataset usage and to version 0.11 for Sentiment Analysis Mar 7, 2019
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@JRAlexander I tested the PR.working fine. But I have questions on the output.
1.The accuracy metrics is 79%. - I believe accuracy should be more than 85%.Correct me if I am wrong.
2. See the probabilities in below image. Howcome a text with probability <50% is showing prediction correctly?
image

I believe that probability also should be > 50% to get prediction correctly. Correct me if I am wrong.

@CESARDELATORRE Adding Cesar for reference.

@JRAlexander

JRAlexander commented Mar 8, 2019

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It leaves the door open for improving the model. This is the dataset we are using. It has better accuracy than using wikidetox.

@CESARDELATORRE

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Well, a rule of thumb is to have an accuracy higher than 80%. However, depending on the business/domain it might need to be a lot higher or even be acceptable to be lower.

When using small datasets it is normal in most cases that the accuracy won't be very super high, neither the probabilities.

Since we're using small datasets for short trainings, I'd suggest not to show the probability in the code or output..

@JRAlexander

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I'm going to leave it in there with an explanation about the small data

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Looks good @JRAlexander. Added a suggestion. I'll leave the decision to implement or not up to you.

Comment threadmachine-learning/tutorials/SentimentAnalysis/Program.cs
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Added your suggestion as issue here dotnet/docs#11221

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JRAlexander merged commit 545ca31 into dotnet:masterMar 8, 2019
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Thanks, @luisquintanilla!

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Update dataset usage and to version 0.11 for Sentiment Analysis  - #680
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JRAlexander merged 5 commits into
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JRAlexander:jralexander-022819-01
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Merged
Update dataset usage and to version 0.11 for Sentiment Analysis #680
JRAlexander merged 5 commits into
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Revising with UCI Sentiment Dataset for greater model accuracy, and updating to 0.11.0.
@JRAlexanderJRAlexander
added
the 
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Indicates a PR can only be merged when other related PRs are merged (see comments for links)
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Update dataset usage for Sentiment AnalysisUpdate dataset usage and to version 0.11 for Sentiment Analysis Mar 7, 2019
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@JRAlexander I tested the PR.working fine. But I have questions on the output.

1.The accuracy metrics is 79%. - I believe accuracy should be more than 85%.Correct me if I am wrong.

2. See the probabilities in below image. Howcome a text with probability <50% is showing prediction correctly?
image
I believe that probability also should be > 50% to get prediction correctly. Correct me if I am wrong.
@CESARDELATORRE Adding Cesar for reference.
@JRAlexander
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It leaves the door open for improving the model. This is the dataset we are using. It has better accuracy than using wikidetox.
@CESARDELATORRE
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Well, a rule of thumb is to have an accuracy higher than 80%. However, depending on the business/domain it might need to be a lot higher or even be acceptable to be lower.
When using small datasets it is normal in most cases that the accuracy won't be very super high, neither the probabilities.
Since we're using small datasets for short trainings, I'd suggest not to show the probability in the code or output..
@JRAlexander
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I'm going to leave it in there with an explanation about the small data
luisquintanilla
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Looks good @JRAlexander. Added a suggestion. I'll leave the decision to implement or not up to you.
Comment threadmachine-learning/tutorials/SentimentAnalysis/Program.cs
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Added your suggestion as issue here dotnet/docs#11221
@JRAlexander
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Thanks, @luisquintanilla!
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var __m = "*";
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Update dataset usage and to version 0.11 for Sentiment Analysis  - #680
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JRAlexander merged 5 commits into
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JRAlexander:jralexander-022819-01
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Merged
Update dataset usage and to version 0.11 for Sentiment Analysis #680
JRAlexander merged 5 commits into
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Revising with UCI Sentiment Dataset for greater model accuracy, and updating to 0.11.0.
@JRAlexanderJRAlexander
added
the 
🚧 Hold for related PR
Indicates a PR can only be merged when other related PRs are merged (see comments for links)
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Feb 28, 2019
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Update dataset usage for Sentiment AnalysisUpdate dataset usage and to version 0.11 for Sentiment Analysis Mar 7, 2019
@prathyusha12345
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@JRAlexander I tested the PR.working fine. But I have questions on the output.

1.The accuracy metrics is 79%. - I believe accuracy should be more than 85%.Correct me if I am wrong.

2. See the probabilities in below image. Howcome a text with probability <50% is showing prediction correctly?
image
I believe that probability also should be > 50% to get prediction correctly. Correct me if I am wrong.
@CESARDELATORRE Adding Cesar for reference.
@JRAlexander
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It leaves the door open for improving the model. This is the dataset we are using. It has better accuracy than using wikidetox.
@CESARDELATORRE
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Mar 8, 2019

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Well, a rule of thumb is to have an accuracy higher than 80%. However, depending on the business/domain it might need to be a lot higher or even be acceptable to be lower.
When using small datasets it is normal in most cases that the accuracy won't be very super high, neither the probabilities.
Since we're using small datasets for short trainings, I'd suggest not to show the probability in the code or output..
@JRAlexander
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I'm going to leave it in there with an explanation about the small data
luisquintanilla
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Looks good @JRAlexander. Added a suggestion. I'll leave the decision to implement or not up to you.
Comment threadmachine-learning/tutorials/SentimentAnalysis/Program.cs
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Added your suggestion as issue here dotnet/docs#11221
@JRAlexander
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merged commit 545ca31
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dotnet:masterMar 8, 2019
@JRAlexander
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Thanks, @luisquintanilla!
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, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Update dataset usage and to version 0.11 for Sentiment Analysis  - #680
Merged
JRAlexander merged 5 commits into
dotnet:masterfrom 
JRAlexander:jralexander-022819-01
Mar 8, 2019
Merged
Update dataset usage and to version 0.11 for Sentiment Analysis #680
JRAlexander merged 5 commits into
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Revising with UCI Sentiment Dataset for greater model accuracy, and updating to 0.11.0.
@JRAlexanderJRAlexander
added
the 
🚧 Hold for related PR
Indicates a PR can only be merged when other related PRs are merged (see comments for links)
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Feb 28, 2019
@JRAlexanderJRAlexander
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Update dataset usage for Sentiment AnalysisUpdate dataset usage and to version 0.11 for Sentiment Analysis Mar 7, 2019
@prathyusha12345
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@JRAlexander I tested the PR.working fine. But I have questions on the output.

1.The accuracy metrics is 79%. - I believe accuracy should be more than 85%.Correct me if I am wrong.

2. See the probabilities in below image. Howcome a text with probability <50% is showing prediction correctly?
image
I believe that probability also should be > 50% to get prediction correctly. Correct me if I am wrong.
@CESARDELATORRE Adding Cesar for reference.
@JRAlexander
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It leaves the door open for improving the model. This is the dataset we are using. It has better accuracy than using wikidetox.
@CESARDELATORRE
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Well, a rule of thumb is to have an accuracy higher than 80%. However, depending on the business/domain it might need to be a lot higher or even be acceptable to be lower.
When using small datasets it is normal in most cases that the accuracy won't be very super high, neither the probabilities.
Since we're using small datasets for short trainings, I'd suggest not to show the probability in the code or output..
@JRAlexander
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I'm going to leave it in there with an explanation about the small data
luisquintanilla
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Mar 8, 2019
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Looks good @JRAlexander. Added a suggestion. I'll leave the decision to implement or not up to you.
Comment threadmachine-learning/tutorials/SentimentAnalysis/Program.cs
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Added your suggestion as issue here dotnet/docs#11221
@JRAlexander
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merged commit 545ca31
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dotnet:masterMar 8, 2019
@JRAlexander
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Thanks, @luisquintanilla!
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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" + '

Skip to content
Update dataset usage and to version 0.11 for Sentiment Analysis  - #680
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JRAlexander merged 5 commits into
dotnet:masterfrom 
JRAlexander:jralexander-022819-01
Mar 8, 2019
Merged
Update dataset usage and to version 0.11 for Sentiment Analysis #680
JRAlexander merged 5 commits into
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Revising with UCI Sentiment Dataset for greater model accuracy, and updating to 0.11.0.
@JRAlexanderJRAlexander
added
the 
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Indicates a PR can only be merged when other related PRs are merged (see comments for links)
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Update dataset usage for Sentiment AnalysisUpdate dataset usage and to version 0.11 for Sentiment Analysis Mar 7, 2019
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@JRAlexander I tested the PR.working fine. But I have questions on the output.

1.The accuracy metrics is 79%. - I believe accuracy should be more than 85%.Correct me if I am wrong.

2. See the probabilities in below image. Howcome a text with probability <50% is showing prediction correctly?
image
I believe that probability also should be > 50% to get prediction correctly. Correct me if I am wrong.
@CESARDELATORRE Adding Cesar for reference.
@JRAlexander
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commented
Mar 8, 2019

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It leaves the door open for improving the model. This is the dataset we are using. It has better accuracy than using wikidetox.
@CESARDELATORRE
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Mar 8, 2019

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Well, a rule of thumb is to have an accuracy higher than 80%. However, depending on the business/domain it might need to be a lot higher or even be acceptable to be lower.
When using small datasets it is normal in most cases that the accuracy won't be very super high, neither the probabilities.
Since we're using small datasets for short trainings, I'd suggest not to show the probability in the code or output..
@JRAlexander
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Mar 8, 2019

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I'm going to leave it in there with an explanation about the small data
luisquintanilla
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Mar 8, 2019
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Looks good @JRAlexander. Added a suggestion. I'll leave the decision to implement or not up to you.
Comment threadmachine-learning/tutorials/SentimentAnalysis/Program.cs
@JRAlexander
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Mar 8, 2019

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Added your suggestion as issue here dotnet/docs#11221
@JRAlexander
JRAlexander
merged commit 545ca31
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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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Update dataset usage and to version 0.11 for Sentiment Analysis  - #680
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JRAlexander:jralexander-022819-01
Mar 8, 2019
Merged
Update dataset usage and to version 0.11 for Sentiment Analysis #680
JRAlexander merged 5 commits into
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Revising with UCI Sentiment Dataset for greater model accuracy, and updating to 0.11.0.
@JRAlexanderJRAlexander
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Update dataset usage for Sentiment AnalysisUpdate dataset usage and to version 0.11 for Sentiment Analysis Mar 7, 2019
@prathyusha12345
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@JRAlexander I tested the PR.working fine. But I have questions on the output.

1.The accuracy metrics is 79%. - I believe accuracy should be more than 85%.Correct me if I am wrong.

2. See the probabilities in below image. Howcome a text with probability <50% is showing prediction correctly?
image
I believe that probability also should be > 50% to get prediction correctly. Correct me if I am wrong.
@CESARDELATORRE Adding Cesar for reference.
@JRAlexander
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It leaves the door open for improving the model. This is the dataset we are using. It has better accuracy than using wikidetox.
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Well, a rule of thumb is to have an accuracy higher than 80%. However, depending on the business/domain it might need to be a lot higher or even be acceptable to be lower.
When using small datasets it is normal in most cases that the accuracy won't be very super high, neither the probabilities.
Since we're using small datasets for short trainings, I'd suggest not to show the probability in the code or output..
@JRAlexander
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I'm going to leave it in there with an explanation about the small data
luisquintanilla
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Looks good @JRAlexander. Added a suggestion. I'll leave the decision to implement or not up to you.
Comment threadmachine-learning/tutorials/SentimentAnalysis/Program.cs
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Added your suggestion as issue here dotnet/docs#11221
@JRAlexander
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merged commit 545ca31
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Thanks, @luisquintanilla!
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@luisquintanillaluisquintanillaluisquintanilla approved these changes
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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('^' + ".*" + '

Skip to content
Update dataset usage and to version 0.11 for Sentiment Analysis  - #680
Merged
JRAlexander merged 5 commits into
dotnet:masterfrom 
JRAlexander:jralexander-022819-01
Mar 8, 2019
Merged
Update dataset usage and to version 0.11 for Sentiment Analysis #680
JRAlexander merged 5 commits into
dotnet:masterfrom 
JRAlexander:jralexander-022819-01
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@JRAlexander
@JRAlexanderJRAlexander
commented
Feb 28, 2019•

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Revising with UCI Sentiment Dataset for greater model accuracy, and updating to 0.11.0.
@JRAlexanderJRAlexander
added
the 
🚧 Hold for related PR
Indicates a PR can only be merged when other related PRs are merged (see comments for links)
label
Feb 28, 2019
@JRAlexanderJRAlexander
self-assigned this
Feb 28, 2019
@JRAlexanderJRAlexander
mentioned this pull request
Mar 6, 2019
 Merged

@JRAlexanderJRAlexander
changed the title
Update dataset usage for Sentiment AnalysisUpdate dataset usage and to version 0.11 for Sentiment Analysis Mar 7, 2019
@prathyusha12345
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Mar 8, 2019•

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@JRAlexander I tested the PR.working fine. But I have questions on the output.

1.The accuracy metrics is 79%. - I believe accuracy should be more than 85%.Correct me if I am wrong.

2. See the probabilities in below image. Howcome a text with probability <50% is showing prediction correctly?
image
I believe that probability also should be > 50% to get prediction correctly. Correct me if I am wrong.
@CESARDELATORRE Adding Cesar for reference.
@JRAlexander
JRAlexander
commented
Mar 8, 2019

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It leaves the door open for improving the model. This is the dataset we are using. It has better accuracy than using wikidetox.
@CESARDELATORRE
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Mar 8, 2019

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Well, a rule of thumb is to have an accuracy higher than 80%. However, depending on the business/domain it might need to be a lot higher or even be acceptable to be lower.
When using small datasets it is normal in most cases that the accuracy won't be very super high, neither the probabilities.
Since we're using small datasets for short trainings, I'd suggest not to show the probability in the code or output..
@JRAlexander
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Mar 8, 2019

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I'm going to leave it in there with an explanation about the small data
luisquintanilla
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Mar 8, 2019
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Looks good @JRAlexander. Added a suggestion. I'll leave the decision to implement or not up to you.
Comment threadmachine-learning/tutorials/SentimentAnalysis/Program.cs
@JRAlexander
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Mar 8, 2019

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Added your suggestion as issue here dotnet/docs#11221
@JRAlexander
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merged commit 545ca31
into
dotnet:masterMar 8, 2019
@JRAlexander
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Thanks, @luisquintanilla!
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@luisquintanillaluisquintanillaluisquintanilla approved these changes
@rpetrusharpetrushaAwaiting requested review from rpetrusha
@mairawmairawAwaiting requested review from mairaw
@CESARDELATORRECESARDELATORREAwaiting requested review from CESARDELATORRE
@prathyusha12345prathyusha12345Awaiting requested review from prathyusha12345

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4 participants

@JRAlexander@prathyusha12345@CESARDELATORRE@luisquintanilla

, '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); }
})();
})();

Skip to content
Update dataset usage and to version 0.11 for Sentiment Analysis  - #680
Merged
JRAlexander merged 5 commits into
dotnet:masterfrom 
JRAlexander:jralexander-022819-01
Mar 8, 2019
Merged
Update dataset usage and to version 0.11 for Sentiment Analysis #680
JRAlexander merged 5 commits into
dotnet:masterfrom 
JRAlexander:jralexander-022819-01
Conversation
@JRAlexander
@JRAlexanderJRAlexander
commented
Feb 28, 2019•

edited

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Revising with UCI Sentiment Dataset for greater model accuracy, and updating to 0.11.0.
@JRAlexanderJRAlexander
added
the 
🚧 Hold for related PR
Indicates a PR can only be merged when other related PRs are merged (see comments for links)
label
Feb 28, 2019
@JRAlexanderJRAlexander
self-assigned this
Feb 28, 2019
@JRAlexanderJRAlexander
mentioned this pull request
Mar 6, 2019
 Merged

@JRAlexanderJRAlexander
changed the title
Update dataset usage for Sentiment AnalysisUpdate dataset usage and to version 0.11 for Sentiment Analysis Mar 7, 2019
@prathyusha12345
prathyusha12345
commented
Mar 8, 2019•

edited

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@JRAlexander I tested the PR.working fine. But I have questions on the output.

1.The accuracy metrics is 79%. - I believe accuracy should be more than 85%.Correct me if I am wrong.

2. See the probabilities in below image. Howcome a text with probability <50% is showing prediction correctly?
image
I believe that probability also should be > 50% to get prediction correctly. Correct me if I am wrong.
@CESARDELATORRE Adding Cesar for reference.
@JRAlexander
JRAlexander
commented
Mar 8, 2019

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ContributorAuthor
It leaves the door open for improving the model. This is the dataset we are using. It has better accuracy than using wikidetox.
@CESARDELATORRE
CESARDELATORRE
commented
Mar 8, 2019

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Well, a rule of thumb is to have an accuracy higher than 80%. However, depending on the business/domain it might need to be a lot higher or even be acceptable to be lower.
When using small datasets it is normal in most cases that the accuracy won't be very super high, neither the probabilities.
Since we're using small datasets for short trainings, I'd suggest not to show the probability in the code or output..
@JRAlexander
JRAlexander
commented
Mar 8, 2019

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ContributorAuthor
I'm going to leave it in there with an explanation about the small data
luisquintanilla
luisquintanilla
requested changes
Mar 8, 2019
View reviewed changes
@luisquintanillaluisquintanilla
left a comment


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Looks good @JRAlexander. Added a suggestion. I'll leave the decision to implement or not up to you.
Comment threadmachine-learning/tutorials/SentimentAnalysis/Program.cs
@JRAlexander
JRAlexander
commented
Mar 8, 2019

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ContributorAuthor
Added your suggestion as issue here dotnet/docs#11221
@JRAlexander
JRAlexander
merged commit 545ca31
into
dotnet:masterMar 8, 2019
@JRAlexander
JRAlexander
commented
Mar 8, 2019

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Thanks, @luisquintanilla!
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@luisquintanillaluisquintanillaluisquintanilla approved these changes
@rpetrusharpetrushaAwaiting requested review from rpetrusha
@mairawmairawAwaiting requested review from mairaw
@CESARDELATORRECESARDELATORREAwaiting requested review from CESARDELATORRE
@prathyusha12345prathyusha12345Awaiting requested review from prathyusha12345

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@JRAlexander@prathyusha12345@CESARDELATORRE@luisquintanilla