python对csv文件中某列数据进行文本分词、去停用词、高频词提取、语义网络分析、文本情感分析详细代码编写及代码详解

由于任务较为复杂，需要使用多个第三方库，以下是详细代码及代码解释：

1. 导入所需库

import csv
import jieba
import jieba.analyse
import networkx as nx
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
from snownlp import SnowNLP

2. 读取csv文件中需要处理的列数据

data = []
with open('data.csv', 'r', encoding='utf-8') as f:
    reader = csv.reader(f)
    for row in reader:
        data.append(row[1])  # 假设需要处理的列为第二列

3. 对每个文本进行分词和去停用词处理

stopwords = [line.strip() for line in open('stopwords.txt', 'r', encoding='utf-8').readlines()]  # 读取停用词表
corpus = []
for text in data:
    words = [word for word in jieba.cut(text) if word not in stopwords]  # 分词并去停用词
    corpus.append(' '.join(words))  # 将分词后的词语用空格连接成字符串

4. 对整个语料库进行高频词提取

keywords = jieba.analyse.extract_tags(' '.join(corpus), topK=10, withWeight=True, allowPOS=('n', 'ns', 'vn', 'v'))  # 提取名词、地名、动名词、动词
for keyword, weight in keywords:
    print(keyword, weight)

5. 构建语义网络

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)
terms = vectorizer.get_feature_names()  # 获取所有单词
model = LatentDirichletAllocation(n_components=5, max_iter=50, learning_method='online', learning_offset=50., random_state=0).fit(X)  # 使用LDA模型进行主题建模
topic_words = []
for topic_idx, topic in enumerate(model.components_):
    word_idx = topic.argsort()[::-1][:10]  # 获取每个主题中权重最高的10个单词索引
    topic_words.append([terms[i] for i in word_idx])  # 将每个主题中的单词转换为实际单词
G = nx.Graph()
for topic in topic_words:
    G.add_nodes_from(topic)  # 将每个主题中的单词添加到语义网络中
for i in range(len(topic_words)):
    for j in range(i+1, len(topic_words)):
        for word1 in topic_words[i]:
            for word2 in topic_words[j]:
                if word1 != word2:
                    G.add_edge(word1, word2)  # 将两个主题中的单词之间存在共现关系的单词连接起来
nx.draw(G, with_labels=True)
plt.show()

6. 对每个文本进行情感分析

for text in corpus:
    s = SnowNLP(text)
    print('Text:', text)
    print('Sentiment:', s.sentiments)

以上就是对csv文件中某列数据进行文本分词、去停用词、高频词提取、语义网络分析、文本情感分析的详细代码及代码解释。