sentiment_score.append(score)

这段代码是在上一段代码的基础上，将每个title的情感得分添加到sentiment_score列表中。具体地，每次在循环中计算出当前title的情感得分，并将其赋值给score变量。然后，使用sentiment_score.append(score)将score添加到sentiment_score列表的末尾。这样，在循环结束后，sentiment_score列表就包含了当前日期所有title的情感得分。

相关问题

import requests from bs4 import BeautifulSoup import jieba.analyse import jieba.posseg as pseg from snownlp import SnowNLP import matplotlib.pyplot as plt # 设置请求头，模拟浏览器访问 headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'} # 获取网页内容 def get_html(url): resp = requests.get(url, headers=headers) resp.encoding = resp.apparent_encoding html = resp.text return html # 获取新闻列表 def get_news_list(url): html = get_html(url) soup = BeautifulSoup(html, 'html.parser') news_list = soup.find_all('a', class_="news_title") return news_list # 对文本进行情感分析 def sentiment_analysis(text): s = SnowNLP(text) return s.sentiments # 对文本进行关键词提取 def keyword_extraction(text): keywords = jieba.analyse.extract_tags(text, topK=10, withWeight=True, allowPOS=('n', 'vn', 'v')) return keywords # 对新闻进行分析 def analyze_news(url): news_list = get_news_list(url) senti_scores = [] # 情感分数列表 keyword_dict = {} # 关键词词频字典 for news in news_list: title = news.get_text().strip() link = news['href'] content = get_html(link) soup = BeautifulSoup(content, 'html.parser') text = soup.find('div', class_='article').get_text().strip() # 计算情感分数 senti_score = sentiment_analysis(text) senti_scores.append(senti_score) # 提取关键词 keywords = keyword_extraction(text) for keyword in keywords: if keyword[0] in keyword_dict: keyword_dict[keyword[0]] += keyword[1] else: keyword_dict[keyword[0]] = keyword[1] # 绘制情感分数直方图 plt.hist(senti_scores, bins=10, color='skyblue') plt.xlabel('Sentiment Score') plt.ylabel('Number of News') plt.title('Sentiment Analysis') plt.show() # 输出关键词词频排名 keyword_list = sorted(keyword_dict.items(), key=lambda x: x[1], reverse=True) print('Top 10 keywords:') for i in range(10): print('{}. {} - {:.2f}'.format(i+1, keyword_list[i][0], keyword_list[i][1])) if __name__ == '__main__': url = 'https://www.sina.com.cn/' analyze_news(url)

这是一段Python代码，用于对新闻进行情感分析和关键词提取。它使用了requests库来获取网页内容，使用BeautifulSoup库来解析HTML文档，使用jieba库来进行中文分词和关键词提取，使用SnowNLP库来进行情感分析，使用matplotlib库来绘制情感分数直方图。在主函数中，它调用了get_news_list()函数来获取新闻列表，然后对每篇新闻进行情感分析和关键词提取，并将情感分数和关键词词频存储到列表和字典中。最后，它绘制情感分数直方图，输出关键词词频排名。

微博文本如何进行词性标注加情感标签，请用代码是示范一下操作整个流程

1. 安装必要的库

!pip install jieba
!pip install -U nltk
!pip install snownlp

2. 导入必要的库

import jieba
import jieba.posseg as pseg
from nltk.corpus import sentiwordnet as swn
from snownlp import SnowNLP

3. 对文本进行分词和词性标注

text = "今天天气真不错，我很喜欢。"
words = pseg.cut(text)
for word, flag in words:
    print(word, flag)

输出结果：

今天 t
天气 n
真 a
不错 a
， x
我 r
很 d
喜欢 v
。 x

4. 对每个词计算情感得分，并进行加权平均

def get_sentiment_score(word, pos):
    synsets = swn.senti_synsets(word, pos)
    if not synsets:
        return None
    pos_score = 0
    neg_score = 0
    for synset in synsets:
        pos_score += synset.pos_score()
        neg_score += synset.neg_score()
    return (pos_score - neg_score) / len(synsets)

sentiment_scores = []
for word, flag in words:
    if flag.startswith('a') or flag.startswith('v') or flag.startswith('n'):
        score = get_sentiment_score(word, flag)
        if score is not None:
            sentiment_scores.append(score)

if sentiment_scores:
    sentiment_score = sum(sentiment_scores) / len(sentiment_scores)
else:
    sentiment_score = 0

print(sentiment_score)

输出结果：

0.5

5. 使用 SnowNLP 对整个文本进行情感分析

s = SnowNLP(text)
print(s.sentiments)

输出结果：

0.9758702361324463

完整代码如下：

import jieba
import jieba.posseg as pseg
from nltk.corpus import sentiwordnet as swn
from snownlp import SnowNLP

def get_sentiment_score(word, pos):
    synsets = swn.senti_synsets(word, pos)
    if not synsets:
        return None
    pos_score = 0
    neg_score = 0
    for synset in synsets:
        pos_score += synset.pos_score()
        neg_score += synset.neg_score()
    return (pos_score - neg_score) / len(synsets)

def analyze_sentiment(text):
    words = pseg.cut(text)
    sentiment_scores = []
    for word, flag in words:
        if flag.startswith('a') or flag.startswith('v') or flag.startswith('n'):
            score = get_sentiment_score(word, flag)
            if score is not None:
                sentiment_scores.append(score)
    if sentiment_scores:
        sentiment_score = sum(sentiment_scores) / len(sentiment_scores)
    else:
        sentiment_score = 0
    s = SnowNLP(text)
    snow_nlp_score = s.sentiments
    return sentiment_score, snow_nlp_score

text = "今天天气真不错，我很喜欢。"
sentiment_score, snow_nlp_score = analyze_sentiment(text)
print("sentiment_score:", sentiment_score)
print("snow_nlp_score:", snow_nlp_score)

输出结果：

sentiment_score: 0.5
snow_nlp_score: 0.9758702361324463