How to do sentiment analysis and text classification in PHP?

With the development of artificial intelligence and natural language processing, sentiment analysis and text classification have become increasingly important application scenarios. In PHP, we can use different tools and algorithms to achieve these functions. This article will explain how to perform sentiment analysis and text classification in PHP, and which tools and algorithms should be used.

1. Sentiment Analysis

Sentiment analysis refers to analyzing text to determine whether the emotion expressed is positive or negative. In PHP, we can perform sentiment analysis in the following two ways.

1. Sentiment analysis based on sentiment dictionary

Sentiment analysis based on sentiment dictionary is a simple and effective sentiment analysis method. Its basic idea is to match each word in the text with the emotional words in the emotional dictionary, count the number of positive words and negative words, and thereby determine the emotion of the text. In PHP, we can use ready-made emotional lexicon libraries, such as Chinese emotional vocabulary ontology library or CNKI emotional vocabulary library, or we can manually build our own emotional dictionary.

The following is a simple sentiment analysis sample code based on sentiment dictionary:

<?php

// 加载情感词典
$positive_words = file('positive.txt', FILE_IGNORE_NEW_LINES);
$negative_words = file('negative.txt', FILE_IGNORE_NEW_LINES);

// 定义情感值
$positive_score = 0;
$negative_score = 0;

// 分词
$words = mb_str_split($text);

// 统计情感值
foreach($words as $word){
    if(in_array($word, $positive_words)){
        $positive_score++;
    } elseif(in_array($word, $negative_words)){
        $negative_score++;
    }
}

// 输出结果
if($positive_score > $negative_score){
    echo '积极';
} elseif($positive_score < $negative_score){
    echo '消极';
} else {
    echo '中性';
}

?>

2. Sentiment analysis based on machine learning

Sentiment based on machine learning Analysis is a more precise method of sentiment analysis. Its basic idea is to build an emotion classification model by training existing annotated data, and then use the model to make emotional judgments on unknown texts. In PHP, we can use ready-made machine learning frameworks such as Scikit-Learn or TensorFlow, or we can write our own classification algorithms. The following is a simple sentiment analysis example code based on the Naive Bayes algorithm:

<?php

// 加载训练集和测试集
$train = file('train.txt', FILE_IGNORE_NEW_LINES);
$test  = file('test.txt',  FILE_IGNORE_NEW_LINES);

// 构建特征向量
foreach($train as $item){
    $words = mb_str_split($item);
    foreach($words as $word){
        $features[$item][$word] = 1;
    }
}

// 训练模型
$classifier = new NaiveBayes();
foreach($features as $item => $vector){
    $label = ($item[0] == '+') ? 'positive' : 'negative';
    $classifier->train($vector, $label);
}

// 预测测试集
foreach($test as $item){
    $words    = mb_str_split($item);
    $vector   = array_fill_keys($words, 1);
    $predicts = $classifier->predict($vector);
    $score    = $predicts['positive'] - $predicts['negative'];
    if($score > 0){
        echo '积极';
    } elseif($score < 0){
        echo '消极';
    } else {
        echo '中性';
    }
}

?>

2. Text Classification

Text classification refers to dividing text into different categories based on similarity or specified criteria. category. In PHP, text classification is widely used in spam filtering, news classification, product evaluation and other fields. Two commonly used text classification methods are introduced below.

1. Text classification based on vector space model

Text classification based on vector space model is a commonly used text classification method. Its basic idea is to represent the text as a vector and calculate the distance or angle between the vectors to determine the category to which the text belongs by comparing it with a predefined classification vector. In PHP, we can use the TF-IDF algorithm to perform feature extraction on text and use cosine similarity to calculate the similarity between vectors. The following is a simple example code for text classification based on vector space model:

<?php

// 加载训练集和测试集
$train = file('train.txt', FILE_IGNORE_NEW_LINES);
$test  = file('test.txt',  FILE_IGNORE_NEW_LINES);

// 提取特征
$vectorizer = new TfIdfVectorizer();
$X_train    = $vectorizer->fit_transform($train);
$y_train    = array_map(function($item){
    return ($item[0] == '+') ? 1 : 0;
}, $train);

// 训练模型
$classifier = new LogisticRegression();
$classifier->fit($X_train, $y_train);

// 测试模型
$X_test      = $vectorizer->transform($test);
$predictions = $classifier->predict($X_test);
foreach($predictions as $predict){
    if($predict){
        echo '积极';
    } else {
        echo '消极';
    }
}

?>

2. Text classification based on hidden Markov model

Based on hidden Markov model Text classification is a more complex method of classifying text. Its basic idea is to represent the text as an implicit state sequence, and by learning the transition probability between states and the emission probability between the state and the observation, to infer the category to which the text belongs. In PHP, we can use the HMM algorithm to model text and the Viterbi algorithm to infer state sequences. The following is a simple example code for text classification based on hidden Markov models:

<?php

// 加载训练集和测试集
$train = file('train.txt', FILE_IGNORE_NEW_LINES);
$test  = file('test.txt',  FILE_IGNORE_NEW_LINES);

// 提取特征
$vectorizer = new WordVectorizer();
$X_train    = $vectorizer->fit_transform($train);
$y_train    = array_map(function($item){
    return ($item[0] == '+') ? 'positive' : 'negative';
}, $train);

// 训练模型
$model = new HMM();
$model->fit($X_train, $y_train);

// 测试模型
$X_test      = $vectorizer->transform($test);
$predictions = $model->predict($X_test);
foreach($predictions as $predict){
    echo $predict;
}

?>

Summary

This article introduces two basic methods for sentiment analysis and text classification in PHP. Sentiment analysis based on sentiment dictionaries and text classification based on vector space models are suitable for simple sentiment judgment and text classification scenarios; while sentiment analysis based on machine learning and text classification based on hidden Markov models are suitable for more complex sentiment judgments. and text classification scenarios. When choosing a method, you need to make a choice based on specific needs and data characteristics.

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