sentiment.js

/**
 * https://github.com/tensorflow/tfjs-examples/tree/master/sentiment
 *
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
 
var model;
var metadataJson;
var indexFrom;
var maxLen;
var wordIndex;
var vocabularySize;

async function loadSentimentAnalysis(){
	if (sentimentAnalysisLoaded!==false){return;}
	sentimentAnalysisLoaded = null;
	

	model = await tf.loadLayersModel('./thirdparty/model.json');
	metadataJson = await fetch('./thirdparty/metadata.json');
	var sentimentMetadata = await metadataJson.json();
	indexFrom = sentimentMetadata['index_from'];
	maxLen = sentimentMetadata['max_len'];
	wordIndex = sentimentMetadata['word_index'];
	vocabularySize = sentimentMetadata['vocabulary_size'];
	console.log("model loaded");
	sentimentAnalysisLoaded = true;
}

function padSequences(
	sequences, padding = 'pre', truncating = 'pre', value = 0) {
		return sequences.map(seq => {
			if (seq.length > maxLen) {
				if (truncating === 'pre') {
				  seq.splice(0, seq.length - maxLen);
				} else {
				  seq.splice(maxLen, seq.length - maxLen);
				}
			}
			if (seq.length < maxLen) {
				const pad = [];
				for (let i = 0; i < maxLen - seq.length; ++i) {
				  pad.push(value);
				}
				if (padding === 'pre') {
				  seq = pad.concat(seq);
				} else {
				  seq = seq.concat(pad);
				}
			}
		return seq;
		}
	);
}
	
function inferSentiment(input_text) {
	if (!sentimentAnalysisLoaded){return;}
	const inputText = input_text.trim().toLowerCase().replace(/(\.|\,|\!)/g, '').split(' ');
	const sequence = inputText.map(word => {
		var wdIndex = wordIndex[word] + indexFrom;
		if (wdIndex > vocabularySize) {
			wdIndex = 2;
		}
		return wdIndex;
	});
	const paddedSequence = padSequences([sequence]);
	const input = tf.tensor2d(paddedSequence, [1, maxLen]);
	const predictOut = model.predict(input);
	const score = predictOut.dataSync()[0];
	predictOut.dispose();
	return score;
}