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background-size:cover!important;background-position:center!important; color:#193645; }\/* nd-shared-components:end *\/\n<\/style>\n<div class=\"ndb\">\n<div class=\"ndb-wrap\">\n<section class=\"ndb-executive\">\n<h2>Classify six GoEmotions annotation categories<\/h2>\n<p>The updated text example uses 5,272 selected GoEmotions records and six labels: love, anger, joy, sadness, surprise and fear. Its saved report evaluates 1,054 held-out texts.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>5,272<\/strong><span>Source records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>45<\/strong><span>Encoded input values<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>1,054<\/strong><span>Testing-role records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>6<\/strong><span>Model outputs<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a class=\"aui aui-button\" href=\"#7-model-deployment\">Review the current model<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-emotion_analysis-licensed-20261006.zip\">Download model and data (ZIP)<\/a><\/div>\n<\/section>\n<ul class=\"ndb-toc\">\n<li><a href=\"#1-industrial-challenge\">Business decision<\/a><\/li><li><a href=\"#2-data-set\">Data set<\/a><\/li><li><a href=\"#3-model\">Model<\/a><\/li><li><a href=\"#4-training\">Training<\/a><\/li><li><a href=\"#5-selection\">Model selection<\/a><\/li><li><a href=\"#6-testing\">Testing<\/a><\/li><li><a href=\"#7-model-deployment\">Deployment<\/a><\/li><li><a href=\"#8-limitations\">Limitations<\/a><\/li>\n<\/ul>\n<section id=\"1-industrial-challenge\" class=\"ndb-card\"><h2>1. Business decision<\/h2><p>Text analysts can organize annotated language and inspect class-specific errors before using model scores for qualitative review. An emotion label describes the dataset annotation, not a person\u2019s mental state or intent.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value-card\"><h3>Updated annotation source<\/h3><p>Use the packaged GoEmotions selection.<\/p><\/div><div class=\"ndb-value-card\"><h3>Class-specific evidence<\/h3><p>Inspect the complete held-out confusion table.<\/p><\/div><div class=\"ndb-value-card\"><h3>Interpretation boundary<\/h3><p>Treat labels as annotations, not psychological assessments.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Text analytics<\/span><span>Language research<\/span><span>Model evaluation<\/span><\/div><div class=\"ndb-note\">Single-label English text classification for research and text organization.<\/div><\/section>\n<section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>The package contains an adapted GoEmotions subset from Google Research, with its selection and attribution documented in LICENSES\/OPENNN-SOURCE.md. The current label order is love, anger, joy, sadness, surprise, fear; it differs from the former DAIR.AI example.<\/p><p>Source: <a href=\"https:\/\/github.com\/google-research\/google-research\/tree\/d36068b845da4c2b24927fee2cea1e6ef98dadda\/goemotions\">GoEmotions<\/a>. Dataset license: CC-BY-4.0. The downloadable ZIP includes the adapted data and attribution notices.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Dataset measure<\/th><th scope=\"col\">Saved value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Analysis unit<\/th><td>labelled text<\/td><\/tr><tr><th scope=\"row\">Records<\/th><td>5,272<\/td><\/tr><tr><th scope=\"row\">Raw variables<\/th><td>2<\/td><\/tr><tr><th scope=\"row\">Encoded model inputs<\/th><td>45<\/td><\/tr><tr><th scope=\"row\">Model outputs<\/th><td>6<\/td><\/tr><tr><th scope=\"row\">Training roles<\/th><td>3164<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection roles<\/th><td>1054<\/td><\/tr><tr><th scope=\"row\">Testing roles<\/th><td>1054<\/td><\/tr><tr><th scope=\"row\">Unused roles<\/th><td>0<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Field<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Type<\/th><th scope=\"col\">Categories<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">input_sequence<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">target_sequence<\/th><td>Target<\/td><td>Categorical<\/td><td>love, anger, joy, sadness, surprise, fear<\/td><\/tr><\/tbody><\/table><\/div><div class=\"ndb-note\">The saved project assigns rows to training, validation and testing as shown above. This is internal record-level evaluation; it does not demonstrate separation by subject, device, site or acquisition batch.<\/div><\/section>\n<section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The model has 45 encoded inputs and 6 outputs. No architecture-selection experiment is recorded in this project. The diagram shows the topology used by the saved model.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Layer<\/th><th scope=\"col\">Input shape<\/th><th scope=\"col\">Output shape<\/th><th scope=\"col\">Activation<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Embedding<\/th><td>45<\/td><td>45 \u00d7 64<\/td><td><\/td><\/tr><tr><th scope=\"row\">MultiHeadAttention<\/th><td>45 \u00d7 64<\/td><td>45 \u00d7 64<\/td><td><\/td><\/tr><tr><th scope=\"row\">Pooling3d<\/th><td>45 \u00d7 64<\/td><td>64<\/td><td><\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>64<\/td><td>64<\/td><td>ReLU<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>64<\/td><td>6<\/td><td>Softmax<\/td><\/tr><\/tbody><\/table><\/div><p>Output values are uncalibrated model scores. Use the output encodings and decision rule documented with this project; do not assume independent sigmoid scores sum to one.<\/p><figure class=\"ndb-architecture-figure\"><a class=\"ndb-architecture-link\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-emotion_analysis-architecture-20261006.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-emotion_analysis-architecture-20261006.png\" alt=\"Emotion classification from text \u2014 initial network architecture\"><\/a><figcaption>Topology of the saved current model; no architecture selection is recorded.<\/figcaption><\/figure><\/section>\n<section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2><p>The saved training configuration uses Adam with CrossEntropy.<\/p><h3>Adaptive moment estimation results<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>75<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:08<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Maximum epochs number<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.632<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>1.03<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-emotion_analysis-t0-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-emotion_analysis-t0-s2-20261006\" role=\"img\" aria-label=\"Adaptive moment estimation error history\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-emotion_analysis-t0-s2-20261006.png\" alt=\"Adaptive moment estimation error history\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Training error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74],\"y\":[1.5700000524520874,1.2699999809265137,1.0499999523162842,0.8830000162124634,0.7459999918937683,0.6320000290870667,0.5270000100135803,0.42399999499320984,0.3540000021457672,0.29499998688697815,0.24400000274181366,0.19499999284744263,0.1469999998807907,0.11599999666213989,0.09529999643564224,0.07880000025033951,0.07320000231266022,0.0731000006198883,0.08129999786615372,0.11800000071525574,0.10100000351667404,0.09139999747276306,0.0617000013589859,0.0714000016450882,0.039000000804662704,0.0210999995470047,0.01510000042617321,0.014600000344216824,0.011900000274181366,0.015799999237060547,0.013000000268220901,0.012600000016391277,0.013299999758601189,0.01769999973475933,0.125,0.28200000524520874,0.13899999856948853,0.049400001764297485,0.022099999710917473,0.01209999993443489,0.009150000289082527,0.008410000242292881,0.010099999606609344,0.010300000198185444,0.010599999688565731,0.013500000350177288,0.012000000104308128,0.01140000019222498,0.01209999993443489,0.01119999960064888,0.010999999940395355,0.011099999770522118,0.010499999858438969,0.013000000268220901,0.01209999993443489,0.01679999940097332,0.11699999868869781,0.24400000274181366,0.11400000005960464,0.03869999945163727,0.012900000438094139,0.006060000043362379,0.005750000011175871,0.006219999864697456,0.006339999847114086,0.007009999826550484,0.007269999943673611,0.00675999978557229,0.007739999797195196,0.007079999893903732,0.007199999876320362,0.0074800001457333565,0.0077599999494850636,0.008430000394582748,0.006709999870508909]},{\"connectgaps\":false,\"line\":{\"color\":\"#ed982a\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Validation 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sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.24,\"yanchor\":\"top\"},\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Training history\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,74],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,2.0005],\"title\":{\"text\":\"Cross-entropy error\"},\"zerolinecolor\":\"#aeb6c2\"},\"height\":500}}<\/script><\/div><figcaption>Adaptive moment estimation error history. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><\/section>\n<section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2><p>No model selection experiment is recorded for this version. The validation subset guides fitting where a training report is present; it is distinct from the held-out test rows.<\/p><\/section>\n<section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2><p>The figures and tables below refer to the current project\u2019s saved testing analysis. The subset uses testing role 2; it contains 1054 source records.<\/p><p>The multiclass decision uses the largest output score. The confusion matrix gives class prevalence and per-class errors; binary sensitivity, specificity, precision and a single ROC AUC are not interchangeable with this multiclass accuracy.<\/p><h3>Confusion table<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Predicted love<\/th><th scope=\"col\">Predicted anger<\/th><th scope=\"col\">Predicted joy<\/th><th scope=\"col\">Predicted sadness<\/th><th scope=\"col\">Predicted surprise<\/th><th scope=\"col\">Predicted fear<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Actual love<\/th><td>258 (24.5%)<\/td><td>11 (1.0%)<\/td><td>14 (1.3%)<\/td><td>9 (0.9%)<\/td><td>3 (0.3%)<\/td><td>2 (0.2%)<\/td><td>297 (28.2%)<\/td><\/tr><tr><th scope=\"row\">Actual anger<\/th><td>12 (1.1%)<\/td><td>131 (12.4%)<\/td><td>12 (1.1%)<\/td><td>21 (2.0%)<\/td><td>16 (1.5%)<\/td><td>10 (0.9%)<\/td><td>202 (19.2%)<\/td><\/tr><tr><th scope=\"row\">Actual joy<\/th><td>15 (1.4%)<\/td><td>19 (1.8%)<\/td><td>106 (10.1%)<\/td><td>15 (1.4%)<\/td><td>12 (1.1%)<\/td><td>6 (0.6%)<\/td><td>173 (16.4%)<\/td><\/tr><tr><th scope=\"row\">Actual sadness<\/th><td>12 (1.1%)<\/td><td>23 (2.2%)<\/td><td>15 (1.4%)<\/td><td>94 (8.9%)<\/td><td>11 (1.0%)<\/td><td>14 (1.3%)<\/td><td>169 (16.0%)<\/td><\/tr><tr><th scope=\"row\">Actual surprise<\/th><td>7 (0.7%)<\/td><td>10 (0.9%)<\/td><td>9 (0.9%)<\/td><td>6 (0.6%)<\/td><td>95 (9.0%)<\/td><td>6 (0.6%)<\/td><td>133 (12.6%)<\/td><\/tr><tr><th scope=\"row\">Actual fear<\/th><td>6 (0.6%)<\/td><td>15 (1.4%)<\/td><td>6 (0.6%)<\/td><td>14 (1.3%)<\/td><td>9 (0.9%)<\/td><td>30 (2.8%)<\/td><td>80 (7.6%)<\/td><\/tr><tr><th scope=\"row\">Total<\/th><td>310 (29.4%)<\/td><td>209 (19.8%)<\/td><td>162 (15.4%)<\/td><td>159 (15.1%)<\/td><td>146 (13.9%)<\/td><td>68 (6.5%)<\/td><td>1054 (100.0%)<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Test measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Testing records<\/th><td>1054<\/td><\/tr><tr><th scope=\"row\">Accuracy<\/th><td>67.74%<\/td><\/tr><tr><th scope=\"row\">Largest-class baseline<\/th><td>28.18%<\/td><\/tr><\/tbody><\/table><\/div><\/section>\n<section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2><p>Open the downloaded project in Neural Designer, inspect the dataset roles and preprocessing, then review the saved task report. Use the same input schema and category order when calculating outputs. The ZIP contains the exact current .nd, its source data and the applicable dataset notices.<\/p><p>Workflow: source measurements \u2192 schema and availability checks \u2192 model output \u2192 domain review. Keep model versions, validation evidence and incoming-data monitoring together.<\/p><div class=\"ndb-downloads\"><a class=\"aui aui-button\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-emotion_analysis-licensed-20261006.zip\">Download model, data and licenses (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Download Neural Designer<\/a><\/div><\/section>\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Evidence and limitations<\/h2><p>Context, irony and mixed emotions can cause errors. The selected subset does not represent the prevalence of emotion in another text collection. Evaluate the same tokenizer, class order and text preprocessing, and review rare-class errors before applying the model to another domain.<\/p><\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/github.com\/google-research\/google-research\/tree\/d36068b845da4c2b24927fee2cea1e6ef98dadda\/goemotions\">GoEmotions<\/a>. Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko, Alan Cowen,. 10.18653\/v1\/2020.acl-main.372<\/li><li>Dataset terms: Creative Commons Attribution 4.0 International. Full attribution and transformations are included in <code>LICENSES\/DATASET-LICENSE.txt<\/code>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-emotion_analysis-licensed-20261006.zip\">Current Neural Designer project and saved task report<\/a>, snapshot 6 October 2026.<\/li><\/ul><\/section>\n<\/div>\n<\/div>\n<style>\n.nd-chart-bundle{width:100%;max-width:760px;min-width:0;margin:24px auto;box-sizing:border-box;position:relative;overflow-x:auto;text-align:center;background:#fff;color:#30343b}\n.nd-chart-bundle .nd-chart-host{width:100%;height:440px;min-width:0;box-sizing:border-box}\n.nd-chart-bundle:not(.is-ready) .nd-chart-host{position:absolute;visibility:hidden}\n.nd-chart-bundle.is-ready .nd-chart-fallback{display:none!important}\n.nd-chart-bundle .nd-chart-fallback{display:block;margin:0 auto!important;width:100%;height:auto;max-width:100%}\n.nd-chart-bundle>script,.nd-chart-bundle>br,.nd-chart-bundle>p:empty{display:none!important}\n@media(max-width:600px){.nd-chart-bundle{margin:20px auto}.nd-chart-bundle .nd-chart-host{height:440px}}\n.nd-chart-bundle .nd-chart-host{min-width:0!important}.nd-chart-bundle{max-width:100%}html body .nd-comparison-grid>.nd-comparison-item:only-child{flex-basis:min(760px,100%)!important;width:min(760px,100%)!important}.ndb-card details,.nds-card details{margin:20px 0}.ndb-card summary,.nds-card summary{cursor:pointer;font-weight:600}<\/style><script data-noptimize=\"1\" data-cfasync=\"false\" src=\"https:\/\/cdn.plot.ly\/plotly-basic-4.0.0.min.js\"><\/script><script data-noptimize=\"1\">(()=>{const start=()=>{if(!window.Plotly)return;document.querySelectorAll('.nd-chart-bundle').forEach(bundle=>{if(bundle.dataset.started)return;bundle.dataset.started='1';const host=bundle.querySelector('.nd-chart-host'),source=bundle.querySelector('script[type=\"application\/json\"]');if(!host||!source)return;const figure=JSON.parse(source.textContent);window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).then(async()=>{const title=host.querySelector('.gtitle');if(title)host.style.minWidth=Math.ceil(title.getComputedTextLength()+48)+'px';await window.Plotly.Plots.resize(host);bundle.classList.add('is-ready');if(window.ResizeObserver){let timer;new ResizeObserver(()=>{clearTimeout(timer);timer=setTimeout(()=>window.Plotly.Plots.resize(host),80);}).observe(bundle);}}).catch(error=>{bundle.dataset.error=String(error);console.error(error);});});};const ready=()=>{if(window.Plotly){start();return;}let count=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);start();}else if(++count>200)clearInterval(timer);},50);};if(document.readyState==='loading')document.addEventListener('DOMContentLoaded',ready,{once:true});else ready();})();<\/script>","protected":false},"author":152,"featured_media":0,"template":"","categories":[29],"tags":[],"class_list":["post-24118","learning","type-learning","status-publish","hentry","category-examples"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Emotion classification from text - Neural Designer<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/emotion-classification-from-text\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Emotion classification from text - Neural Designer\" \/>\n<meta property=\"og:description\" content=\"The updated text example uses 5,272 selected GoEmotions records and six labels: love, anger, joy, sadness, surprise and fear. 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