Emotional tendency issues in text sentiment classification

The emotional tendency problem in text sentiment classification requires specific code examples

[Introduction]
With the popularity of social media and online comments, people are more concerned about There is growing interest in text sentiment analysis. Sentiment classification is a method of studying the sentiment of text that can help us understand people's emotional tendencies toward specific topics. In text emotion classification, the issue of emotional tendency is an important research direction. This article explores the issue of sentimentality and provides some concrete code examples.

[Emotional tendency problem]
The emotional tendency problem means that we need to judge the emotional tendency in the text, that is, whether the text is positive, neutral or negative. Through emotional tendency questions, we can understand users' attitudes towards a certain product, event or opinion, and then provide decision-making and reference basis for enterprises, governments, etc.

[Code Example]
The following is a Python code example that uses machine learning methods to perform emotional tendency problems in text sentiment classification.

import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # 读取数据集 data = pd.read_csv('dataset.csv') # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data['text'], data['label'], test_size=0.2, random_state=42) # 特征提取 vectorizer = TfidfVectorizer(max_features=5000) X_train_vec = vectorizer.fit_transform(X_train) X_test_vec = vectorizer.transform(X_test) # 训练模型 model = LogisticRegression() model.fit(X_train_vec, y_train) # 预测 y_pred = model.predict(X_test_vec) # 计算准确率 accuracy = accuracy_score(y_test, y_pred) print("准确率：", accuracy)

[Code Description]
The code uses TfidfVectorizer in the sklearn library to extract text features and convert the text into a sparse matrix. At the same time, LogisticRegression is used as a classifier for emotion classification training. Finally, the accuracy is used to evaluate the performance of the model.

[Summary]
In text emotion classification, the issue of emotional tendency is an important research direction. Through specific code examples, we can understand how to use machine learning methods to classify text sentiment and determine the emotional tendency of the text. For enterprises, governments, etc., understanding users' emotional tendencies can better understand the market and user needs, and provide better decision-making basis. I hope this article can give readers a certain understanding of the emotional tendency issue in emotion classification.

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