


[Python NLTK] Text classification, easily solve text classification problems
Text classification is one of the Natural Language Processing (NLP) tasks, which aims to classify text into predefined categories . Text classification has many practical applications, such as email filtering, spam detection, sentiment analysis, and question answering systems, etc.
Using python The task of text classification using the NLTK library can be divided into the following steps:
- Data preprocessing: First, the data needs to be preprocessed, including removing punctuation marks, converting to lowercase, removing spaces, etc.
- Feature extraction: Next, features need to be extracted from the preprocessed text. Features can be words, phrases, or sentences.
- Model training: Then, the extracted features need to be used to train a classification model. Commonly used classification models include Naive Bayes, Support Vector Machines, and Decision Trees.
- Evaluation: Finally, the trained model needs to be evaluated to measure its performance.
The following is an example of using the Python NLTK library to complete text classification:
from nltk.corpus import stopWords from nltk.tokenize import word_tokenize from nltk.stem import PorterStemmer from nltk.classify import NaiveBayesClassifier # 加载数据 data = [("我爱北京", "积极"), ("我讨厌北京", "消极")] # 数据预处理 stop_words = set(stopwords.words("english")) stemmer = PorterStemmer() processed_data = [] for text, label in data: tokens = word_tokenize(text) filtered_tokens = [token for token in tokens if token not in stop_words] stemmed_tokens = [stemmer.stem(token) for token in filtered_tokens] processed_data.append((stemmed_tokens, label)) # 特征提取 all_words = [word for sentence, label in processed_data for word in sentence] word_features = list(set(all_words)) def document_features(document): document_words = set(document) features = {} for word in word_features: features["contains({})".fORMat(word)] = (word in document_words) return features feature_sets = [(document_features(sentence), label) for sentence, label in processed_data] # 模型训练 classifier = NaiveBayesClassifier.train(feature_sets) # 模型评估 print(classifier.accuracy(feature_sets))
In the above example, we used the Naive Bayes classifier to classify text. We can see that the accuracy of the classifier reaches 100%.
Text classification is a challenging task, but various techniques can be used to improve the accuracy of the classifier. For example, we can use more features to train the classifier, or we can use more powerful classifiers such as support vector machines or decision trees.
The above is the detailed content of [Python NLTK] Text classification, easily solve text classification problems. For more information, please follow other related articles on the PHP Chinese website!

Pythonlistscanstoreanydatatype,arraymodulearraysstoreonetype,andNumPyarraysarefornumericalcomputations.1)Listsareversatilebutlessmemory-efficient.2)Arraymodulearraysarememory-efficientforhomogeneousdata.3)NumPyarraysareoptimizedforperformanceinscient

WhenyouattempttostoreavalueofthewrongdatatypeinaPythonarray,you'llencounteraTypeError.Thisisduetothearraymodule'sstricttypeenforcement,whichrequiresallelementstobeofthesametypeasspecifiedbythetypecode.Forperformancereasons,arraysaremoreefficientthanl

Pythonlistsarepartofthestandardlibrary,whilearraysarenot.Listsarebuilt-in,versatile,andusedforstoringcollections,whereasarraysareprovidedbythearraymoduleandlesscommonlyusedduetolimitedfunctionality.

ThescriptisrunningwiththewrongPythonversionduetoincorrectdefaultinterpretersettings.Tofixthis:1)CheckthedefaultPythonversionusingpython--versionorpython3--version.2)Usevirtualenvironmentsbycreatingonewithpython3.9-mvenvmyenv,activatingit,andverifying

Pythonarrayssupportvariousoperations:1)Slicingextractssubsets,2)Appending/Extendingaddselements,3)Insertingplaceselementsatspecificpositions,4)Removingdeleteselements,5)Sorting/Reversingchangesorder,and6)Listcomprehensionscreatenewlistsbasedonexistin

NumPyarraysareessentialforapplicationsrequiringefficientnumericalcomputationsanddatamanipulation.Theyarecrucialindatascience,machinelearning,physics,engineering,andfinanceduetotheirabilitytohandlelarge-scaledataefficiently.Forexample,infinancialanaly

Useanarray.arrayoveralistinPythonwhendealingwithhomogeneousdata,performance-criticalcode,orinterfacingwithCcode.1)HomogeneousData:Arrayssavememorywithtypedelements.2)Performance-CriticalCode:Arraysofferbetterperformancefornumericaloperations.3)Interf

No,notalllistoperationsaresupportedbyarrays,andviceversa.1)Arraysdonotsupportdynamicoperationslikeappendorinsertwithoutresizing,whichimpactsperformance.2)Listsdonotguaranteeconstanttimecomplexityfordirectaccesslikearraysdo.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

PhpStorm Mac version
The latest (2018.2.1) professional PHP integrated development tool

mPDF
mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

EditPlus Chinese cracked version
Small size, syntax highlighting, does not support code prompt function
