Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Algoritme klasifikasi teks adalah inti dari berbagai sistem
software yang memproses data teks dalam skala besar. Software email menggunakan klasifikasi teks untuk menentukan apakah email masuk dikirim ke kotak masuk atau difilter ke folder spam. Forum diskusi menggunakan klasifikasi teks untuk menentukan apakah komentar harus ditandai sebagai tidak pantas.
Ini adalah dua contoh klasifikasi topik, mengategorikan dokumen teks ke dalam salah satu rangkaian topik yang telah ditentukan. Dalam banyak masalah klasifikasi topik, kategorisasi ini terutama didasarkan pada kata kunci dalam teks.
Gambar 1: Klasifikasi topik digunakan untuk menandai email spam masuk yang difilter ke folder spam.
Jenis klasifikasi teks yang umum lainnya adalah analisis minat, yang
tujuannya untuk mengidentifikasi polaritas konten teks: jenis opini yang
dinyatakan. Ini dapat berupa biner seperti rating suka/tidak suka, atau kumpulan opsi yang lebih terperinci, seperti rating bintang dari 1 sampai 5. Contoh analisis analisis meliputi analisis postingan Twitter untuk menentukan apakah orang-orang menyukai film Black Panther atau tidak, atau dengan mengekstrapolasi opini publik tentang merek sepatu Nike baru dari ulasan Walmart.
Panduan ini akan mengajari Anda beberapa praktik terbaik machine learning utama untuk menyelesaikan
masalah klasifikasi teks. Berikut hal yang akan Anda pelajari:
Alur kerja tingkat tinggi dan menyeluruh untuk menyelesaikan masalah klasifikasi teks menggunakan machine learning
Cara memilih model yang tepat untuk masalah klasifikasi teks Anda
Cara menerapkan model pilihan Anda menggunakan TensorFlow
Alur Kerja Klasifikasi Teks
Berikut adalah ringkasan tingkat tinggi tentang alur kerja yang digunakan untuk menyelesaikan masalah machine learning:
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Informasi yang saya butuhkan tidak ada","missingTheInformationINeed","thumb-down"],["Terlalu rumit/langkahnya terlalu banyak","tooComplicatedTooManySteps","thumb-down"],["Sudah usang","outOfDate","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Masalah kode / contoh","samplesCodeIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2022-09-27 UTC."],[[["\u003cp\u003eText classification algorithms are widely used to categorize text data, with applications like spam filtering and content moderation.\u003c/p\u003e\n"],["\u003cp\u003eTopic classification and sentiment analysis are two common types of text classification, focusing on categorizing text into predefined topics and identifying the sentiment expressed, respectively.\u003c/p\u003e\n"],["\u003cp\u003eThis guide provides a comprehensive workflow for solving text classification problems using machine learning, including data gathering, exploration, preparation, model building, training, evaluation, hyperparameter tuning, and deployment.\u003c/p\u003e\n"],["\u003cp\u003eChoosing the right machine learning model is crucial for effective text classification and is discussed in detail within the guide.\u003c/p\u003e\n"],["\u003cp\u003eTensorFlow is used to implement the chosen model for practical application in text classification tasks.\u003c/p\u003e\n"]]],[],null,["# Introduction\n\nText classification algorithms are at the heart of a variety of software\nsystems that process text data at scale. Email software uses text classification\nto determine whether incoming mail is sent to the inbox or filtered into the\nspam folder. Discussion forums use text classification to determine whether\ncomments should be flagged as inappropriate.\n\nThese are two examples of topic classification, categorizing a text document\ninto one of a predefined set of topics. In many topic classification problems,\nthis categorization is based primarily on keywords in the text.\n\n**Figure 1: Topic classification is used to flag incoming spam emails, which\nare filtered into a spam folder.**\n\nAnother common type of text classification is ***sentiment analysis***, whose\ngoal is to identify the polarity of text content: the type of opinion it\nexpresses. This can take the form of a binary like/dislike rating, or a more\ngranular set of options, such as a star rating from 1 to 5. Examples of\nsentiment analysis include analyzing Twitter posts to determine if people\nliked the Black Panther movie, or extrapolating the general public's opinion\nof a new brand of Nike shoes from Walmart reviews.\n\nThis guide will teach you some key machine learning best practices for solving\ntext classification problems. Here's what you'll learn:\n\n- The high-level, end-to-end workflow for solving text classification problems using machine learning\n- How to choose the right model for your text classification problem\n- How to implement your model of choice using TensorFlow\n\nText Classification Workflow\n----------------------------\n\nHere's a high-level overview of the workflow used to solve machine learning problems:\n\n- [Step 1: Gather Data](/machine-learning/guides/text-classification/step-1)\n- [Step 2: Explore Your Data](/machine-learning/guides/text-classification/step-2)\n- *[Step 2.5: Choose a Model\\*](/machine-learning/guides/text-classification/step-2-5)*\n- [Step 3: Prepare Your Data](/machine-learning/guides/text-classification/step-3)\n- [Step 4: Build, Train, and Evaluate Your Model](/machine-learning/guides/text-classification/step-4)\n- [Step 5: Tune Hyperparameters](/machine-learning/guides/text-classification/step-5)\n- [Step 6: Deploy Your Model](/machine-learning/guides/text-classification/step-6)\n\n**Figure 2: Workflow for solving machine learning problems**\n| \"Choose a model\" is not a formal step of the traditional machine learning workflow; however, selecting an appropriate model for your problem is a critical task that clarifies and simplifies the work in the steps that follow.\n\nThe following sections explain each step in detail, and how to implement them for text data."]]
