小说文本分析工具：基于streamlit实现的文本分析
主要在于使用python对小说文本中章节之间的识别与分割，通过分词以及停用词库，抽取关键词章节的词云展示，以及关键词在整个文本当中的权重网络。
import re
import streamlit as st
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import jieba
from collections import Counter
import chardet
import numpy as np
import networkx as nx
import os
from sklearn.feature_extraction.text import TfidfVectorizer
# =====================
# 全局配置
# =====================
DEFAULT_STOPWORDS_PATH = r"D:\daku\小说图谱\stopwords.txt"
MAX_FILE_SIZE = 200 # MB
BACKGROUND_COLOR = "#0E1117"
TEXT_COLOR = "#FFFFFF"
# =====================
# 初始化设置
# =====================
st.set_page_config(
page_title="小说文本分析工具",
page_icon="📚",
layout="wide",
initial_sidebar_state="expanded"
)
jieba.initialize()
# =====================
# 核心功能模块
# =====================
@st.cache_data
def split_chapters(content):
"""增强型章节分割"""
patterns = [
r'(第[零一二三四五六七八九十百千万\d]+章\s*[^\n]*)',
r'(【.*?】)\s*',
r'(<h1>.*?</h1>)\s*'
]
# 动态生成复合正则表达式
matches = []
for pattern in patterns:
for match in re.finditer(pattern, content, flags=re.MULTILINE):
start_pos = match.start()
full_title = match.group(0).strip()
matches.append((start_pos, full_title))
# 按位置排序并去重
matches = sorted(list({x[0]: x for x in matches}.values()), key=lambda x: x[0])
chapters = []
prev_end = 0
# 处理前言部分
if matches and matches[0][0] > 0:
chapters.append(("前言", content[0:matches[0][0]].strip()))
# 分割章节内容
for i in range(len(matches)):
start_pos, title = matches[i]
end_pos = matches[i + 1][0] if i < len(matches) - 1 else len(content)
chapter_content = content[start_pos:end_pos].strip()
# 过滤空内容章节
if len(chapter_content) > 10: # 至少包含10个字符
chapters.append((title, chapter_content))
# 处理无章节情况
if not chapters:
chapters = [("全文", content.strip())]
return chapters
@st.cache_data
def calculate_jaccard_similarity(keyword_data, top_n=10):
"""基于前N关键词的Jaccard相似度计算"""
all_keywords = set()
keyword_sets = []
for _, keywords in keyword_data:
chapter_keywords = set([k for k, _ in keywords[:top_n]])
keyword_sets.append(chapter_keywords)
all_keywords.update(chapter_keywords)
similarity_matrix = np.zeros((len(keyword_sets), len(keyword_sets)))
for i in range(len(keyword_sets)):
for j in range(i + 1, len(keyword_sets)):
intersection = len(keyword_sets[i] & keyword_sets[j])
union = len(keyword_sets[i] | keyword_sets[j])
similarity_matrix[i][j] = similarity_matrix[j][i] = intersection / union if union != 0 else 0
return similarity_matrix
# =====================
# 可视化模块
# =====================
def generate_dark_wordcloud(counter):
"""深色背景词云生成"""
wc = WordCloud(
font_path="simhei.ttf",
width=800,
height=400,
background_color=BACKGROUND_COLOR,
colormap='viridis',
max_words=50,
contour_color=TEXT_COLOR
).generate_from_frequencies(counter)
fig, ax = plt.subplots(figsize=(10, 6))
ax.imshow(wc, interpolation='bilinear')
ax.axis("off")
return fig
def draw_network_graph(similarity_matrix, labels, threshold=0.3):
"""符合设计图的网络关系图"""
G = nx.Graph()
# 添加节点和边
for i, label in enumerate(labels):
G.add_node(label[:12], size=800)
for j in range(i + 1, len(labels)):
if similarity_matrix[i][j] > threshold:
G.add_edge(label[:12], labels[j][:12], weight=similarity_matrix[i][j])
# 可视化参数
plt.figure(figsize=(12, 8))
pos = nx.spring_layout(G, k=0.8)
# 绘制节点
nx.draw_networkx_nodes(
G, pos,
node_size=1200,
node_color="#4B8BBE",
alpha=0.9
)
# 绘制边
edges = G.edges(data=True)
nx.draw_networkx_edges(
G, pos,
edgelist=edges,
width=[d['weight'] * 3 for _, _, d in edges],
edge_color="#7F7F7F",
alpha=0.6
)
# 节点标签
nx.draw_networkx_labels(
G, pos,
font_size=10,
font_family='SimHei',
font_color=TEXT_COLOR
)
# 边权重标签
edge_labels = {(u, v): f"{d['weight']:.2f}" for u, v, d in edges if d['weight'] > 0.3}
nx.draw_networkx_edge_labels(
G, pos,
edge_labels=edge_labels,
font_color="#FF4B4B"
)
plt.axis('off')
return plt
# =====================
# 主界面实现
# =====================
def main():
# 页面样式
st.markdown(f"""
<style>
.reportview-container {{
background: {BACKGROUND_COLOR};
color: {TEXT_COLOR};
}}
.sidebar .sidebar-content {{
background: {BACKGROUND_COLOR};
border-right: 1px solid #2e2e2e;
}}
.st-bq {{
color: {TEXT_COLOR} !important;
}}
</style>
""", unsafe_allow_html=True)
# 侧边栏设置
with st.sidebar:
st.header("⚙️ 设置")
uploaded_file = st.file_uploader(
"上传小说文件",
type=['txt'],
help="最大文件尺寸：200MB"
)
threshold = st.slider("关系阈值", 0.0, 1.0, 0.75, 0.05)
num_keywords = st.slider("关键词数量", 10, 50, 10)
# 主内容区
st.title("小说文本分析工具")
if uploaded_file:
try:
# 文件大小验证
if uploaded_file.size > MAX_FILE_SIZE * 1024 * 1024:
st.error(f"文件大小超过{MAX_FILE_SIZE}MB限制")
return
# 文件编码检测
raw_data = uploaded_file.getvalue()
encoding = chardet.detect(raw_data)['encoding']
content = raw_data.decode(encoding or 'utf-8', errors='replace')
st.write(f"Detected encoding: {encoding}")
st.write(f"File content preview: {content[:500]}...")
# 章节分割
chapters = split_chapters(content)
if not chapters:
st.error("未能识别到任何章节内容")
return
st.write(f"Chapters detected: {[title for title, _ in chapters]}")
# 加载停用词
stopwords = set()
if os.path.exists(DEFAULT_STOPWORDS_PATH):
with open(DEFAULT_STOPWORDS_PATH, 'r', encoding='utf-8') as f:
stopwords = set(line.strip() for line in f if line.strip())
else:
st.warning(f"未找到停用词文件：{DEFAULT_STOPWORDS_PATH}")
# 关键词分析
keyword_data = []
with st.spinner('分析中...'):
for title, text in chapters:
# 清理文本中的换行符和其他特殊字符
cleaned_text = re.sub(r'\s+', ' ', text)
# 分词处理
words = [word for word in jieba.lcut(cleaned_text)
if len(word) > 1
and word not in stopwords
and not re.match(r'^\d+$', word)]
counter = Counter(words)
keyword_data.append((title, counter.most_common(num_keywords)))
# 相似度矩阵计算
if len(chapters) > 1:
similarity_matrix = calculate_jaccard_similarity(keyword_data, top_n=10)
else:
similarity_matrix = np.zeros((1, 1))
# 布局管理
col1, col2 = st.columns([1, 1])
with col1:
st.subheader("章节关键词")
selected_chapter = st.selectbox(
"选择章节",
options=[title for title, _ in chapters],
index=0
)
idx = [title for title, _ in chapters].index(selected_chapter)
try:
st.pyplot(generate_dark_wordcloud(dict(keyword_data[idx][1])))
except Exception as e:
st.error(f"生成词云失败: {str(e)}")
with col2:
st.subheader("章节关系网络")
if len(chapters) > 1:
plt = draw_network_graph(similarity_matrix, [title for title, _ in chapters], threshold)
st.pyplot(plt)
else:
st.info("需要至少两个章节生成关系网络")
# 分析报告
with st.expander("📊 分析详情"):
report_data = {
"文件名": uploaded_file.name,
"文件大小": f"{uploaded_file.size / 1024:.1f} KB",
"识别章节数": len(chapters),
"总字数": sum(len(text) for _, text in chapters),
"平均章节长度": f"{sum(len(text) for _, text in chapters) / len(chapters):.0f} 字",
"高频关键词": "、".join([k for k, _ in keyword_data[0][1][:5]])
}
st.table(report_data)
except Exception as e:
st.error(f"处理失败: {str(e)}")
st.error("请检查文件格式是否符合要求（UTF-8/GBK编码的文本文件）")
else:
st.info("👈 请上传小说文件开始分析")
if __name__ == "__main__":
main()