文本情感分析阿里云天池比赛代码

以下是阿里云天池比赛“零基础入门NLP之新闻文本分类”中，一个样例的情感分析代码：

import pandas as pd
import numpy as np
import jieba
import gensim
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

# 读取数据集
train_data = pd.read_csv('train_set.csv')
test_data = pd.read_csv('test_set.csv')

# 对训练集进行分词
train_data['text'] = train_data['text'].apply(lambda x: " ".join(jieba.cut(x)))
# 对测试集进行分词
test_data['text'] = test_data['text'].apply(lambda x: " ".join(jieba.cut(x)))

# 训练词向量模型
model = gensim.models.Word2Vec(train_data['text'], size=100, window=5, min_count=5, workers=4)

# 将词向量转化为文本向量
def get_text_vector(text):
    words = text.split()
    vector = np.zeros(100)
    for word in words:
        if word in model:
            vector += model[word]
    return vector

train_data['vector'] = train_data['text'].apply(get_text_vector)
test_data['vector'] = test_data['text'].apply(get_text_vector)

# 将文本向量转化为特征矩阵
X = np.stack(train_data['vector'].values)
y = train_data['label'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 训练朴素贝叶斯分类器
classifier = Pipeline([
    ('count_vec', CountVectorizer()),
    ('tfidf_transformer', TfidfTransformer()),
    ('clf', MultinomialNB(alpha=0.01))
])
classifier.fit(X_train, y_train)

# 预测测试集结果
y_pred = classifier.predict(X_test)
print(classification_report(y_test, y_pred))

这段代码使用了jieba分词、gensim训练词向量模型、朴素贝叶斯分类器等技术，对新闻文本进行情感分析。其中，将文本转化为特征矩阵的过程使用了词袋模型和TF-IDF方法。最终，代码输出了测试集的分类报告。