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background-size:cover!important;background-position:center!important; color:#193645; }\/* nd-shared-components:end *\/\n\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\n.nd-native-figure{width:min(760px,100%);margin:24px auto!important}.nd-native-figure figcaption{margin-top:12px;text-align:left}\n\n\/* Saved Calculate outputs examples. *\/\n.nd-output-examples{margin:28px 0}.nd-output-examples .nd-output-example{min-width:0}.nd-output-examples blockquote{margin:16px 0;padding:12px 18px;border-left:3px solid #176b91;background:#f4f7f9;color:#193645;overflow-wrap:anywhere}.nd-output-examples figure{margin-top:16px;margin-bottom:16px}.nd-output-examples figcaption{margin-top:12px;overflow-wrap:anywhere}.nd-output-examples img{height:auto}.nd-output-examples .nd-output-digit{width:112px!important;height:112px!important;image-rendering:pixelated}.nd-output-examples h4{font-size:21px;margin:0 0 16px}.nd-output-examples .nd-output-result{margin:16px 0}\n<\/style>\n<div class=\"ndb\">\n<div class=\"ndb-wrap\">\n<section class=\"ndb-executive\">\n<h2>Turn product reviews into sentiment labels<\/h2>\n<p>Classify short English product reviews as bad or good with an attention-based text model. This example uses 1,000 labelled reviews and reproduces 67.5% accuracy on the 200 documents assigned to testing.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>1,000<\/strong><span>Documents<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>2<\/strong><span>Classes<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>200<\/strong><span>Saved testing-role samples<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>67.50%<\/strong><span>Verified test accuracy<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a class=\"aui aui-button\" href=\"#7-model-deployment\">Explore the model<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/archive.ics.uci.edu\/dataset\/331\/sentiment+labelled+sentences\">View source dataset<\/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>Customer-experience teams need to organize review text before examining recurring product and service issues. A sentiment classifier can prioritize manual review and summarize feedback, provided that uncertain or ambiguous messages remain open to human interpretation.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value\"><h3>Review triage<\/h3><p>Group favourable and unfavourable feedback for a reviewer.<\/p><\/div><div class=\"ndb-value\"><h3>Error inspection<\/h3><p>Inspect false positives and false negatives before defining a workflow.<\/p><\/div><div class=\"ndb-value\"><h3>Reproducible baseline<\/h3><p>Start with the supplied small model and retain the same text preprocessing.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Machine-learning practitioners<\/span><span>Data and analytics teams<\/span><\/div><div class=\"ndb-note\">Sentence-level English sentiment classification. The model does not identify topics, explain dissatisfaction or estimate the effect of a marketing intervention.<\/div><\/section>\n<section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>The local file is the Amazon subset of the UCI Sentiment Labelled Sentences collection, with the binary labels expressed as Bad and Good. These are review sentences, not synthetic examples. The collection is balanced by design; it does not estimate the natural proportion of satisfied customers.<\/p><p><a href=\"https:\/\/archive.ics.uci.edu\/dataset\/331\/sentiment+labelled+sentences\">Dataset source and access conditions<\/a>. Source reviewed on 24 September 2026.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><caption>Target class distribution table<\/caption><thead><tr><th scope=\"col\"><\/th><th scope=\"col\">Documents<\/th><th scope=\"col\">Share (%)<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Bad<\/th><td>500<\/td><td>50.00<\/td><\/tr><tr><th scope=\"row\">Good<\/th><td>500<\/td><td>50.00<\/td><\/tr><\/tbody><\/table><\/div><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-amazon-reviews-t0-s2-20260924\"><div class=\"nd-chart-host\" id=\"nd-amazon-reviews-t0-s2-20260924\" role=\"img\" aria-label=\"Target class distribution pie chart\"><\/div><p class=\"nd-chart-fallback\">Target class distribution pie chart. Enable JavaScript to explore the chart.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"direction\":\"clockwise\",\"hovertemplate\":\"%{label}\\u003cbr\\u003e%{value}\\u003cbr\\u003e%{percent}\\u003cextra\\u003e\\u003c\/extra\\u003e\",\"labels\":[\"Bad\",\"Good\"],\"marker\":{\"colors\":[\"#209fdf\",\"#092d40\"]},\"showlegend\":true,\"sort\":false,\"textinfo\":\"label+percent\",\"type\":\"pie\",\"values\":[50,50]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{},\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Target class distribution pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"}}}<\/script><\/div><figcaption>Class distribution in the complete supplied dataset.<\/figcaption><\/figure><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Subset<\/th><th scope=\"col\">Samples<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Training<\/th><td>600<\/td><\/tr><tr><th scope=\"row\">Validation<\/th><td>200<\/td><\/tr><tr><th scope=\"row\">Testing<\/th><td>200<\/td><\/tr><\/tbody><\/table><\/div><p>Inputs are UTF-8 text, one document per row, followed by its label in the tab-separated source. The saved word-level tokenizer provides padding, unknown, start and end tokens. Its vocabulary and sequence limit must travel with the trained model.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><caption>Vocabulary<\/caption><thead><tr><th scope=\"col\"><\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Vocabulary size<\/th><td>828<\/td><\/tr><tr><th scope=\"row\">Words<\/th><td>824<\/td><\/tr><tr><th scope=\"row\">Reserved tokens<\/th><td>4<\/td><\/tr><tr><th scope=\"row\">Words appearing once<\/th><td>0<\/td><\/tr><tr><th scope=\"row\">Words never used<\/th><td>0<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\" tabindex=\"0\"><table><caption>Sequence length<\/caption><thead><tr><th scope=\"col\"><\/th><th scope=\"col\">Tokens<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Median<\/th><td>13<\/td><\/tr><tr><th scope=\"row\">90th percentile<\/th><td>26<\/td><\/tr><tr><th scope=\"row\">99th percentile<\/th><td>34<\/td><\/tr><tr><th scope=\"row\">Longest<\/th><td>42<\/td><\/tr><tr><th scope=\"row\">Limit<\/th><td>42<\/td><\/tr><tr><th scope=\"row\">Padding (%)<\/th><td>65.73<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\" tabindex=\"0\"><table><caption>Unknown words<\/caption><thead><tr><th scope=\"col\"><\/th><th scope=\"col\">Samples<\/th><th scope=\"col\">Tokens<\/th><th scope=\"col\">Unknown<\/th><th scope=\"col\">Unknown (%)<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Training<\/th><td>600<\/td><td>8533<\/td><td>612<\/td><td>7.17<\/td><\/tr><tr><th scope=\"row\">Validation<\/th><td>200<\/td><td>3040<\/td><td>246<\/td><td>8.09<\/td><\/tr><tr><th scope=\"row\">Testing<\/th><td>200<\/td><td>2820<\/td><td>203<\/td><td>7.2<\/td><\/tr><\/tbody><\/table><\/div><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-amazon-reviews-t1-s5-20260924\"><div class=\"nd-chart-host\" id=\"nd-amazon-reviews-t1-s5-20260924\" role=\"img\" aria-label=\"Vocabulary coverage curve\"><\/div><p class=\"nd-chart-fallback\">Vocabulary coverage curve. Enable JavaScript to explore the chart.<\/p><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\":\"Corpus covered 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sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Vocabulary coverage curve\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[1,824],\"title\":{\"text\":\"Most frequent words\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,100.02],\"title\":{\"text\":\"Corpus covered (%)\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Native vocabulary coverage curve for the saved text dataset.<\/figcaption><\/figure><div class=\"ndb-note\">The project stores a 60\/20\/20 sample partition (rounded for the 1,999-image collection). Results apply only to this example\u2019s split. Group-level independence is not documented.<\/div><\/section>\n<section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The following configuration comes from the saved trained model. The same topology is used before and after training; no separate architecture-selection run is recorded.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Layer<\/th><th scope=\"col\">Input shape<\/th><th scope=\"col\">Output shape<\/th><th scope=\"col\">Configuration<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Embedding<\/th><td>42<\/td><td>42 \u00d7 16<\/td><td>Vocabulary 828; positional encoding<\/td><\/tr><tr><th scope=\"row\">MultiHeadAttention<\/th><td>42 \u00d7 16<\/td><td>42 \u00d7 16<\/td><td>2 attention heads; non-causal<\/td><\/tr><tr><th scope=\"row\">Pooling3d<\/th><td>42 \u00d7 16<\/td><td>16<\/td><td>AveragePooling<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>16<\/td><td>16<\/td><td>ReLU<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>16<\/td><td>1<\/td><td>Sigmoid<\/td><\/tr><\/tbody><\/table><\/div><p>Sigmoid or softmax outputs are uncalibrated model scores. For this binary model, the positive label is good. The 0.5 threshold assigns the positive class.<\/p><figure class=\"ndb-architecture-figure\"><a class=\"ndb-architecture-link\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-amazon-reviews-t2-s1-20260924.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-amazon-reviews-t2-s1-20260924.png\" alt=\"Amazon review sentiment classification \u2014 saved neural network architecture\"><\/a><figcaption>Static architecture diagram rendered by Neural Designer from the saved report. The topology is the same before and after training. Open the image to inspect the layer dimensions.<\/figcaption><\/figure><\/section>\n<section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2><p>Training minimizes cross-entropy using Adam. The saved configuration uses a learning rate of 0.001, mini-batches of 64 and L2 regularization with weight 0.001.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><caption>Adaptive moment estimation results<\/caption><thead><tr><th scope=\"col\"><\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>40<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:02<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Maximum epochs number<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.409<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.462<\/td><\/tr><\/tbody><\/table><\/div><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-amazon-reviews-t3-s2-20260924\"><div class=\"nd-chart-host\" id=\"nd-amazon-reviews-t3-s2-20260924\" role=\"img\" aria-label=\"Adaptive moment estimation error history\"><\/div><p class=\"nd-chart-fallback\">Adaptive moment estimation error history. Enable JavaScript to explore the chart.<\/p><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],\"y\":[0.6930000185966492,0.6930000185966492,0.6919999718666077,0.6909999847412109,0.6899999976158142,0.6869999766349792,0.6819999814033508,0.671999990940094,0.656000018119812,0.6349999904632568,0.6169999837875366,0.6010000109672546,0.5849999785423279,0.5709999799728394,0.5540000200271606,0.546999990940094,0.531000018119812,0.5180000066757202,0.5040000081062317,0.49399998784065247,0.4790000021457672,0.46700000762939453,0.45399999618530273,0.44200000166893005,0.4410000145435333,0.42100000381469727,0.4090000092983246,0.3919999897480011,0.38600000739097595,0.3659999966621399,0.35199999809265137,0.34200000762939453,0.33000001311302185,0.3160000145435333,0.29899999499320984,0.28999999165534973,0.27399998903274536,0.2619999945163727,0.24799999594688416,0.23800000548362732]},{\"connectgaps\":false,\"line\":{\"color\":\"#ed982a\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Validation 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],\"y\":[0.6930000185966492,0.6930000185966492,0.6919999718666077,0.6909999847412109,0.6899999976158142,0.6869999766349792,0.6819999814033508,0.6700000166893005,0.6470000147819519,0.6309999823570251,0.593999981880188,0.5709999799728394,0.5509999990463257,0.5329999923706055,0.5090000033378601,0.5049999952316284,0.4950000047683716,0.48500001430511475,0.4819999933242798,0.47699999809265137,0.4740000069141388,0.47099998593330383,0.4690000116825104,0.4699999988079071,0.4650000035762787,0.4650000035762787,0.4620000123977661,0.4650000035762787,0.46700000762939453,0.4690000116825104,0.4699999988079071,0.4740000069141388,0.4860000014305115,0.4830000102519989,0.48500001430511475,0.49799999594688416,0.5,0.5139999985694885,0.5230000019073486,0.5379999876022339]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.24,\"yanchor\":\"top\"},\"margin\":{\"b\":120,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Adaptive moment estimation error history\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,39],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,0.7001000000000001],\"title\":{\"text\":\"Cross-entropy error\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Native training and validation error history from the supplied report.<\/figcaption><\/figure><p>The summary table and epoch history are retained as separate native report outputs. Epoch-history endpoints need not equal the restored best-validation model; they are not additional test metrics.<\/p><\/section>\n<section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2><p>No architecture selection or feature selection experiment is recorded in this report. The model-selection settings in the project are configuration options, not evidence that a search was run. The validation subset monitors training; the testing subset is intended for subsequent evaluation.<\/p><p>A constant classifier choosing the most frequent training label (bad) achieves 49.00% accuracy on these same testing rows, compared with 67.50% for the supplied model. The baseline label is chosen only from the training rows; this is a simple comparator, not a separately tuned model.<\/p><\/section>\n<section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2><p>The exported model was rerun on the 200 documents whose saved sample role is testing. Predicted labels reproduce the saved confusion counts. Rows are actual classes and columns are predicted classes.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Metric<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Testing documents<\/th><td>200<\/td><\/tr><tr><th scope=\"row\">Accuracy<\/th><td>67.50%<\/td><\/tr><tr><th scope=\"row\">Macro F1<\/th><td>0.675<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Actual \/ predicted<\/th><th scope=\"col\">bad<\/th><th scope=\"col\">good<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">bad<\/th><td>68<\/td><td>30<\/td><\/tr><tr><th scope=\"row\">good<\/th><td>35<\/td><td>67<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Class<\/th><th scope=\"col\">Support<\/th><th scope=\"col\">Precision<\/th><th scope=\"col\">Recall<\/th><th scope=\"col\">F1<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">bad<\/th><td>98<\/td><td>0.660<\/td><td>0.694<\/td><td>0.677<\/td><\/tr><tr><th scope=\"row\">good<\/th><td>102<\/td><td>0.691<\/td><td>0.657<\/td><td>0.673<\/td><\/tr><\/tbody><\/table><\/div><p>At threshold 0.5, good is positive: TP = 67, FN = 35, FP = 30 and TN = 68. Sensitivity is 65.69%, specificity 69.39%, precision 69.07% and NPV 66.02%. Positive-label prevalence is 102\/200 = 51%; predictive values refer to this benchmark composition.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><caption>Area under curve<\/caption><thead><tr><th scope=\"col\"><\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Area under curve<\/th><td>0.785<\/td><\/tr><\/tbody><\/table><\/div><p>The saved ROC AUC is 0.785. Its test-derived threshold of 0.633 is exploratory and is not used for the 0.5-threshold confusion matrix.<\/p><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-amazon-reviews-t4-s1-20260924\"><div class=\"nd-chart-host\" id=\"nd-amazon-reviews-t4-s1-20260924\" role=\"img\" aria-label=\"ROC chart\"><\/div><p class=\"nd-chart-fallback\">ROC chart. 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Positive Rate (sensitivity)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[0,1],\"y\":[0,1]},{\"connectgaps\":false,\"marker\":{\"color\":\"#ed982a\",\"size\":8,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"True Positive Rate (sensitivity)\",\"opacity\":1,\"showlegend\":false,\"type\":\"scatter\",\"x\":[0.19387799501419067],\"y\":[0.6176469922065735]}],\"layout\":{\"annotations\":[{\"font\":{\"color\":\"#404044\"},\"showarrow\":false,\"text\":\"Optimal Threshold\",\"x\":0.3638779950141907,\"y\":0.5276469922065735},{\"font\":{\"color\":\"#404044\"},\"showarrow\":false,\"text\":\"Area under curve: 0.785\",\"x\":0.7,\"y\":0.02}],\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"ROC chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1],\"title\":{\"text\":\"False Positive Rate (1-specificity)\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1],\"title\":{\"text\":\"True Positive Rate (sensitivity)\"},\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>Native ROC curve from the saved report. The highlighted optimal threshold is exploratory, chosen on that evaluation subset.<\/figcaption><\/figure><\/section>\n<section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2><div class=\"nd-output-examples\" id=\"calculate-outputs\"><h3>Understanding customer feedback<\/h3><p>See how the model distinguishes favourable and unfavourable customer reviews. Each example shows the original review and the score assigned to each sentiment.<\/p><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item nd-output-example\" data-output-example=\"1\"><h4>Example 1<\/h4><p><strong>Input text<\/strong><\/p><blockquote>The only thing that I think could improve is the sound leaks out from the headset.<\/blockquote><p class=\"nd-output-result\"><strong>Predicted class: bad<\/strong><br>Model score: 70.61%<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Class<\/th><th scope=\"col\">Output score<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">bad<\/th><td>0.706127<\/td><\/tr><tr><th scope=\"row\">good<\/th><td>0.293873<\/td><\/tr><\/tbody><\/table><\/div><\/div><div class=\"nd-comparison-item nd-output-example\" data-output-example=\"2\"><h4>Example 2<\/h4><p><strong>Input text<\/strong><\/p><blockquote>Excellent sound, battery life and inconspicuous to boot!.<\/blockquote><p class=\"nd-output-result\"><strong>Predicted class: good<\/strong><br>Model score: 62.78%<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Class<\/th><th scope=\"col\">Output score<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">bad<\/th><td>0.372200<\/td><\/tr><tr><th scope=\"row\">good<\/th><td>0.627800<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><p>These are illustrative predictions, separate from the testing metrics above. Output scores have not been calibrated.<\/p><\/div><p>Download the original Neural Designer project and native figures, then relink the source dataset. The ZIP is a project archive; it does not contain the standalone Neural Engine runtime.<\/p><p>For batch inference, export a deployment package from Neural Designer. Keep the generated Python wrapper beside its <code>engine\/<\/code> and <code>model\/<\/code> folders. The supplied export uses Windows x64 and Python 3, with CPU inference by default.<\/p><pre><code>python amazon_reviews.py &quot;A short text to classify&quot;<\/code><\/pre><p>Preserve the trained vocabulary and sequence limit for text, or the saved image dimensions, channel handling and scaling for images. Record the model version and input quality before sending outputs for human review. The wrapper\u2019s score output is not evidence of probability calibration.<\/p><div class=\"ndb-downloads\"><a class=\"aui aui-button\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/amazon-reviews-project-20260924.zip\">Download project and charts (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/archive.ics.uci.edu\/dataset\/331\/sentiment+labelled+sentences\">View source dataset<\/a><\/div><\/section>\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Evidence and limitations<\/h2><p>Short reviews, unfamiliar vocabulary, negation, sarcasm and mixed sentiment can cause errors. The balanced benchmark does not represent live customer sentiment prevalence. Evaluate on a later, product-specific sample before routing reviews automatically.<\/p><p>The supplied artifacts do not establish independence by person, product, author, acquisition session or site. Check duplicates and group-related records before reporting performance on a new population. Calibration and external validation are not established; monitor drift and review errors before operational decisions.<\/p><p>Vocabulary statistics cover the saved dataset. A training-only vocabulary fit is not documented, so these results do not establish a fully isolated preprocessing evaluation. Refit preprocessing inside the training partition when comparing models on a strict benchmark.<\/p><\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/archive.ics.uci.edu\/dataset\/331\/sentiment+labelled+sentences\">Amazon review sentiment classification \u2014 dataset source<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/amazon-reviews-project-20260924.zip\">Supplied Neural Designer project and native figures, 24 September 2026 (ZIP)<\/a><\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/examples\/\">More Neural Designer examples<\/a><\/li><\/ul><\/section>\n<\/div>\n<\/div>\n<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-24117","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>Amazon review sentiment classification - 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\/amazon-review-sentiment-classification\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Amazon review sentiment classification - Neural Designer\" \/>\n<meta property=\"og:description\" content=\"Turn product reviews into sentiment labels Classify short English product reviews as bad or good with an attention-based text model. 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