To categorize text and assign labels to different skills, you can use machine learning classifiers. The solution to this problem will be divided into two steps:
Data preprocessing: Converting raw text into a digital format that machine learning models can understand and process. This includes steps such as text cleaning, word segmentation, vectorization, etc.
Training classifier: Using preprocessed data to train machine learning models to recognize skills and assign them to exclusive labels. Common classifier algorithms include naive Bayes, logistic regression, decision tree, etc.
Here is a simple example that uses a naive Bayes classifier to classify text:
import pandas as pd from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB # 读入数据 df = pd.read_csv('job_descriptions.csv') # 预处理数据 vectorizer = CountVectorizer(stop_words='english') X = vectorizer.fit_transform(df['job_description']) y = df['skill'] # 训练分类器 clf = MultinomialNB() clf.fit(X, y) # 对新数据进行预测 new_job_description = "We are looking for a software engineer with experience in Python and machine learning." X_new = vectorizer.transform([new_job_description]) y_pred = clf.predict(X_new) print(y_pred) # 输出预测的技能标签
In this example, assume that you already have a file called job_descriptions.csv that contains the job description and the appropriate skill tags. First, use the library to read in the data, and use the CountVectorizer class to convert the text into a word-frequency vector. Then, using MultinomialNB class training a naive bayesian classifier. Finally, a trained classifier is used to classify the new job descriptions and output the predicted skill tags.
Please note that this is a simple example, and actual text sorting problems may require more complex data preprocessing and machine learning algorithms to get better results.