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sentimenttree.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "sentimenttree.ipynb", | |
| "provenance": [], | |
| "authorship_tag": "ABX9TyP1XZ9ggJKBG4IyuD7yAu5h", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/maxkleiner/04be36466144a9ff32bbeb36dbbbf1b7/sentimenttree.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "pggupr0A9oOx" | |
| }, | |
| "source": [ | |
| "# Sentiment Tree Classifier" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "04EukN1I80cD", | |
| "outputId": "a0ff7b2b-bffe-488e-dfb6-744a279c2fc8", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "source": [ | |
| "# use a preclassifier dtree with high recall and then a second \n", | |
| "# classifier SVC to sort false positives out!\n", | |
| "# use SVC as classifier and dtree to plot decision\n", | |
| "\n", | |
| "from sklearn import tree\n", | |
| "from sklearn.feature_extraction.text import CountVectorizer\n", | |
| "from sklearn import svm\n", | |
| "from sklearn.metrics import confusion_matrix\n", | |
| "from sklearn.metrics import classification_report\n", | |
| "\n", | |
| "#https://ritza.co/showcase/repl.it/introduction-to-machine-learning-with-python-and-repl-it.html\n", | |
| "\n", | |
| "positive_texts = [\n", | |
| " \"we love you\",\n", | |
| " \"they love us\",\n", | |
| " \"you are good\",\n", | |
| " \"he is good\",\n", | |
| " \"they love max\"\n", | |
| "]\n", | |
| "\n", | |
| "negative_texts = [\n", | |
| " \"we hate you\",\n", | |
| " \"they hate us\",\n", | |
| " \"you are bad\",\n", | |
| " \"he is bad\",\n", | |
| " \"we hate max\"\n", | |
| "]\n", | |
| "\n", | |
| "test_texts = [\n", | |
| " \"they love bad max\", # this is labeled as positive !\n", | |
| " \"they are good\",\n", | |
| " \"why do you hate mary\",\n", | |
| " \"they are almost always good\",\n", | |
| " \"we are very bad\"\n", | |
| "]\n", | |
| "\n", | |
| "print('testset: ',test_texts)\n", | |
| "\n", | |
| "training_texts = negative_texts + positive_texts\n", | |
| "training_labels = [\"neg\"] * len(negative_texts) + [\"pos\"] * len(positive_texts)\n", | |
| "#print(training_labels)\n", | |
| "\n", | |
| "#mapping the words to numbers (bag of words)\n", | |
| "vectorizer = CountVectorizer()\n", | |
| "vectorizer.fit(training_texts)\n", | |
| "print('vocabulary: ',vectorizer.vocabulary_)\n", | |
| "\n", | |
| "#vectorizer.fit(test_texts)\n", | |
| "#print(vectorizer.vocabulary_)\n", | |
| "\n", | |
| "#You can join all lines and then use split: \n", | |
| "print('unique words:',set(\" \".join(test_texts).split()))\n", | |
| "#print(set([wo for line in test_texts for wo in line.split()]))\n", | |
| "\n", | |
| "training_vectors = vectorizer.transform(training_texts)\n", | |
| "testing_vectors = vectorizer.transform(test_texts)\n", | |
| "\n", | |
| "classifier = tree.DecisionTreeClassifier()\n", | |
| "classifier.fit(training_vectors, training_labels)\n", | |
| "predictions = classifier.predict(testing_vectors)\n", | |
| "print('predict: ',predictions)\n", | |
| "#print('testset: ',test_texts)\n", | |
| "\n", | |
| "#http://www.webgraphviz.com/\n", | |
| "tree.export_graphviz(\n", | |
| " classifier,\n", | |
| " out_file='maxtree.dot',\n", | |
| " feature_names=vectorizer.get_feature_names(),\n", | |
| ")\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "# Create a linear SVM classifier\n", | |
| "clf = svm.SVC(kernel='linear')\n", | |
| "# Train & learn classifier\n", | |
| "clf.fit(training_vectors, training_labels)\n", | |
| "\n", | |
| "# Make predictions on unseen test data\n", | |
| "clf_predictions = clf.predict(testing_vectors)\n", | |
| "print('predict SVC: ',clf_predictions)\n", | |
| "test_labels=['pos','pos','neg','pos','neg']\n", | |
| "print(\"Accuracy: {}%\".format(clf.score(testing_vectors,test_labels)*100))\n", | |
| "\n", | |
| "y_test = test_labels\n", | |
| "y_pred = clf_predictions\n", | |
| "\n", | |
| "print('actual:',y_test)\n", | |
| "print('predic:',y_pred)\n", | |
| "\n", | |
| "print('confusion matrix - classification report \\n')\n", | |
| "print(confusion_matrix(y_test, y_pred))\n", | |
| "print(classification_report(y_test, y_pred))" | |
| ], | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "testset: ['they love bad max', 'they are good', 'why do you hate mary', 'they are almost always good', 'we are very bad']\n", | |
| "vocabulary: {'we': 10, 'hate': 3, 'you': 11, 'they': 8, 'us': 9, 'are': 0, 'bad': 1, 'he': 4, 'is': 5, 'max': 7, 'love': 6, 'good': 2}\n", | |
| "unique words: {'love', 'good', 'max', 'are', 'mary', 'almost', 'very', 'you', 'bad', 'hate', 'we', 'always', 'they', 'why', 'do'}\n", | |
| "predict: ['pos' 'pos' 'neg' 'pos' 'neg']\n", | |
| "predict SVC: ['neg' 'pos' 'neg' 'pos' 'neg']\n", | |
| "Accuracy: 80.0%\n", | |
| "actual: ['pos', 'pos', 'neg', 'pos', 'neg']\n", | |
| "predic: ['neg' 'pos' 'neg' 'pos' 'neg']\n", | |
| "confusion matrix - classification report \n", | |
| "\n", | |
| "[[2 0]\n", | |
| " [1 2]]\n", | |
| " precision recall f1-score support\n", | |
| "\n", | |
| " neg 0.67 1.00 0.80 2\n", | |
| " pos 1.00 0.67 0.80 3\n", | |
| "\n", | |
| " accuracy 0.80 5\n", | |
| " macro avg 0.83 0.83 0.80 5\n", | |
| "weighted avg 0.87 0.80 0.80 5\n", | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "fRVScZB4-uDt" | |
| }, | |
| "source": [ | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ZWnuFOm--XX5" | |
| }, | |
| "source": [ | |
| "Because love or hate has 3 counts as discriminator in training set its easier to decide than good and bad with 2 ocurrences.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "LG5br5Vu95Ac" | |
| }, | |
| "source": [ | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "zJcRJ6-_9Q_r" | |
| }, | |
| "source": [ | |
| "# Sentiment Tree Classifier with Support Vector Machine and Decision Tree Graph" | |
| ] | |
| } | |
| ] | |
| } |
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