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实战项目中文影评情感分析系统

news 来源：原创 2025/6/7 7:34:10

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<div class="page-wrap"><header><h1>中文影评情感分析系统</h1><div class="subtitle">基于LSTM与预训练词向量的高精度影评情感判别系统</div></header><div class="container-main"><div class="left-panel"><div><div class="input-area"><h2>输入影评</h2><form id="analysis-form" autocomplete="off"><div class="input-row"><label for="segment-method">分词方式：</label><select id="segment-method"><option value="jieba">jieba中文分词</option><option value="custom">自定义分词（空格分隔）</option></select></div><textarea id="review-text" placeholder="请输入分词后的电影评论内容，例如：剧情 精彩 演员 演技 出色 值得 一看"></textarea><button type="submit" id="analyze-btn">分析情感倾向</button></form></div><div class="examples-block"><h3>示例影评</h3><div class="example-list" id="example-list"></div></div><div class="history-block" id="history-block" style="display:none;"><div class="history-title">分析历史</div><div class="history-list" id="history-list"></div></div></div><div style="margin-top:auto;padding-top:28px;"><button class="help-btn" id="help-btn">帮助中心</button></div></div><div class="right-panel"><div><div class="result-title">分析结果</div><div class="loading" id="loading"><div class="spinner"></div><p>正在分析情感倾向，请稍候…</p></div><div class="result-box" id="result-box"><div class="result-placeholder" id="result-placeholder"><p>输入电影评论并点击「分析情感倾向」，系统会显示情感分析结果</p><p>结果包括：情感（正面/负面/中性）、置信度、分词结果</p></div><div class="result-content" id="result-content" style="display:none;"><div class="sentiment-tag" id="sentiment-tag">情感倾向</div><p><strong>评论内容：</strong><span id="result-text"></span></p><p><strong>置信度：</strong><span id="result-confidence"></span></p><p><strong>预测概率：</strong><span id="result-probability"></span></p><div class="tokens" id="tokens-container"></div></div></div></div><div><div class="model-info-block"><b>模型信息：</b><br><ul><li>模型类型：<code>双向LSTM</code>, 层数：<code>2</code></li><li>嵌入层：<code>预训练Word2Vec</code> (50维)</li><li>优化器：<code>Adam</code>，损失函数：<code>binary_crossentropy</code></li><li>训练集：IMDB/自建中文数据集</li><li>分词方式：<code>jieba/自定义分词</code></li></ul></div><div class="system-info"><h2 style="color:var(--secondary);font-size:1.05rem;">系统技术说明</h2><p>本系统采用：</p><ul><li>深度学习：双向LSTM神经网络</li><li>词向量：预训练中文Word2Vec (50维)</li><li>中文分词：jieba分词</li><li>后端：Python Flask</li><li>前端：HTML5+CSS3+JavaScript</li></ul><p style="font-size:.98rem;color:#789;margin-top:7px;">支持正面/负面/中性情感判别，体验高精度中文情感分析。</p></div></div></div></div><footer style="text-align:center;padding:20px 0 17px 0;background: #23408e;color: #fff;font-size:1.01rem;letter-spacing:1px;"><div>中文影评情感分析系统 &copy; 2025 | 期末项目出品 | <a href="https://github.com/yourrepo" style="color:#bce;">Github</a></div><div style="font-size:.97rem;margin-top:7px;opacity:.85;">开发团队：AI 研究实验室 | 设计指导：张三老师</div></footer>
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<div class="modal-mask" id="modal-mask"><div class="modal-dialog"><button class="modal-close" id="modal-close">&times;</button><h4>帮助中心</h4><p><b>【系统简介】</b><br>本系统可自动分析中文电影评论的情感倾向（支持正面/负面/中性等），并提供分词结果和置信概率。<br><b>【使用说明】</b><br>1. 选择分词方式，输入或粘贴分词后的影评文本。<br>2. 点击“分析情感倾向”按钮，结果将在右侧显示。<br>3. 可点击左侧示例快速体验；分析历史可随时回看。<br><b>【模型说明】</b><br>LSTM深度学习网络，词向量采用预训练的Word2Vec。<br><b>【异常说明】</b><br>若遇接口异常请检查后端服务或联系开发团队。</p></div>
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"正面" : ex.label === "negative" ? "负面" : "中性"}</div>`;list.appendChild(div);});}renderExamples();// ======= 历史记录 =======function saveHistory(item){let history=JSON.parse(localStorage.getItem('sentiment_history')||"[]");history.unshift(item);if(history.length>20)history=history.slice(0,20);localStorage.setItem('sentiment_history',JSON.stringify(history));renderHistory();}function renderHistory(){const historyBlock=document.getElementById('history-block');const list=document.getElementById('history-list');let history=JSON.parse(localStorage.getItem('sentiment_history')||"[]");if(!history||history.length===0){historyBlock.style.display="none";return;}historyBlock.style.display="block";list.innerHTML="";history.forEach((h,i)=>{const div=document.createElement('div');div.className='history-item';div.title="点击查看分析结果";div.innerHTML=`<span class="history-tag ${h.sentiment}">${h.sentiment==='正面'?'正面':h.sentiment==='负面'?'负面':'中性'}</span><span>${h.text.length>22?h.text.slice(0,22)+'...':h.text}</span>`;div.οnclick=()=>showHistoryResult(h);list.appendChild(div);});}function showHistoryResult(h){document.getElementById('result-text').textContent = h.text;document.getElementById('result-confidence').textContent = (h.confidence|| (h.probability !== undefined ? (h.probability > 0.5 ? h.probability : 1-h.probability) : '--')).toFixed(4);document.getElementById('result-probability').textContent = h.probability !== undefined ? h.probability.toFixed(4) : '--';document.getElementById('sentiment-tag').textContent = h.sentiment;document.getElementById('result-box').className = 'result-box ' + (h.sentiment === '正面' ? 'positive' : h.sentiment === '负面' ? 'negative':'neutral');// 分词const tokensContainer = document.getElementById('tokens-container');tokensContainer.innerHTML = '';if (h.tokens && h.tokens.length > 0) {const title = document.createElement('strong');title.textContent = '分词结果：';tokensContainer.appendChild(title);h.tokens.forEach(token => {const tokenEl = document.createElement('div');tokenEl.className = 'token';tokenEl.textContent = token;tokensContainer.appendChild(tokenEl);});}document.getElementById('result-content').style.display = 'block';document.getElementById('result-placeholder').style.display = 'none';}renderHistory();// ======= 分析流程 =======const form = document.getElementById('analysis-form');const textarea = document.getElementById('review-text');form.addEventListener('submit', e => {e.preventDefault();analyzeSentiment();});function loadExample(text) {textarea.value = text;analyzeSentiment();textarea.focus();}function analyzeSentiment() {const text = textarea.value.trim();const segMethod = document.getElementById('segment-method').value;if (!text) {textarea.focus();alert('请输入要分析的影评内容');return;}document.getElementById('loading').style.display = 'block';document.getElementById('result-placeholder').style.display = 'none';document.getElementById('result-content').style.display = 'none';fetch('/predict', {method: 'POST',headers: { 'Content-Type': 'application/x-www-form-urlencoded' },body: `text=${encodeURIComponent(text)}&segment=${encodeURIComponent(segMethod)}`}).then(resp => resp.json()).then(data => {document.getElementById('loading').style.display = 'none';if (data.error) {alert('分析出错: ' + data.error);return;}document.getElementById('result-text').textContent = data.text;document.getElementById('result-confidence').textContent = (data.confidence || (data.probability !== undefined ? (data.probability > 0.5 ? data.probability : 1-data.probability) : '--')).toFixed(4);document.getElementById('result-probability').textContent = data.probability !== undefined ? data.probability.toFixed(4) : '--';const sentimentText = data.sentiment === '正面' ? '正面' : data.sentiment === '负面' ? '负面' : '中性';document.getElementById('sentiment-tag').textContent = sentimentText;const resultBox = document.getElementById('result-box');resultBox.className = 'result-box ' + (data.sentiment === '正面' ? 'positive' : data.sentiment === '负面' ? 'negative' : 'neutral');// 分词const tokensContainer = document.getElementById('tokens-container');tokensContainer.innerHTML = '';if (data.tokens && data.tokens.length > 0) {const title = document.createElement('strong');title.textContent = '分词结果：';tokensContainer.appendChild(title);data.tokens.forEach(token => {const tokenEl = document.createElement('div');tokenEl.className = 'token';tokenEl.textContent = token;tokensContainer.appendChild(tokenEl);});}document.getElementById('result-content').style.display = 'block';saveHistory({text:data.text, sentiment:data.sentiment, confidence:data.confidence, probability:data.probability, tokens:data.tokens});}).catch(error => {document.getElementById('loading').style.display = 'none';alert('分析过程中发生错误，请稍后再试');console.error(error);});}// Ctrl+Enter 快捷分析textarea.addEventListener('keydown', e => {if ((e.ctrlKey || e.metaKey) && e.key === 'Enter') {analyzeSentiment();}});
</script>
</body>
</html>需要改一些配置：

import os
import re
from collections import defaultdict
import numpy as np
from flask import Flask, request, jsonify, render_template
from flask_cors import CORS
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.sequence import pad_sequences
import jiebaapp = Flask(__name__)
CORS(app)  # 支持跨域# =========================== 全局变量 ===========================
word2idx = {}
max_len = 100
model = None# =========================== 词表加载函数 ===========================
def load_vocab():"""加载词表到word2idx，全局可用"""global word2idxword2idx = defaultdict(lambda: 0)  # OOV返回0vocab_path = os.path.join('vocab', 'word.txt')if not os.path.isfile(vocab_path):print(f"警告：缺少词表文件 {vocab_path}")returnwith open(vocab_path, 'r', encoding='utf-8') as f:for idx, line in enumerate(f):word = line.strip()word2idx[word] = idxprint(f"词表加载完成，共 {len(word2idx)} 个词。")# =========================== 模型加载函数 ===========================
def load_models():"""加载LSTM情感分析模型到全局变量model"""global modelmodel_path = os.path.join('models', 'best_model.h5')if not os.path.isfile(model_path):print(f"警告：缺少模型文件 {model_path}")returnmodel = load_model(model_path)print("模型加载完成。")# =========================== 文本预处理 ===========================
def preprocess_text(text, segment_method='jieba'):"""按指定分词方式处理文本，返回分词列表:param text: str:param segment_method: 'jieba' or 'custom':return: list"""text = re.sub(r'[^\u4e00-\u9fa5a-zA-Z0-9\s]', '', text)  # 移除特殊字符if segment_method == 'custom':# 假设用户输入使用空格分词tokens = [token for token in text.split() if token.strip()]else:tokens = [token for token in jieba.cut(text) if token.strip()]return tokens# =========================== 文本编码 ===========================
def encode_text(tokens):"""词索引映射 + padding"""seq = [word2idx.get(token, 0) for token in tokens]padded = pad_sequences([seq], maxlen=max_len, padding='post', truncating='post')return padded# =========================== 情感预测主函数 ===========================
def predict_sentiment(text, segment_method='jieba'):tokens = preprocess_text(text, segment_method)encoded_text = encode_text(tokens)prediction = model.predict(encoded_text)pred_prob = prediction[0][0]# 阈值可调，如果接近0.5可判为"中性"if pred_prob >= 0.6:sentiment = "正面"elif pred_prob <= 0.4:sentiment = "负面"else:sentiment = "中性"# 置信度定义confidence = abs(pred_prob - 0.5) * 2return {'text': text,'tokens': tokens,'sentiment': sentiment,'probability': float(pred_prob),'confidence': confidence}# =========================== 健康检查接口 ===========================
@app.route('/health')
def health():return jsonify({'status': 'ok', 'model_loaded': model is not None, 'vocab_loaded': len(word2idx) > 0})# =========================== 首页 ===========================
@app.route('/')
def index():return render_template('index.html')# =========================== 预测API ===========================
@app.route('/predict', methods=['POST'])
def predict():try:# 支持x-www-form-urlencoded和application/json两种格式if request.content_type and 'json' in request.content_type:data = request.get_json()text = data.get('text', '')segment_method = data.get('segment', 'jieba')else:text = request.form.get('text', '')segment_method = request.form.get('segment', 'jieba')if not text:return jsonify({'error': '请输入影评内容'}), 400result = predict_sentiment(text, segment_method)return jsonify(result), 200except Exception as e:import tracebackprint(traceback.format_exc())return jsonify({'error': '后端推理异常: ' + str(e)}), 500# =========================== 示例数据（供模板渲染） ===========================
@app.context_processor
def inject_examples():examples = [{"text": "这部电影太精彩了，演员表演出色，剧情紧凑，强烈推荐！", "label": "正面"},{"text": "非常失望，剧情拖沓，特效也很假，完全不值票价。", "label": "负面"},{"text": "中规中矩的电影，没有特别出彩的地方，但也不差。", "label": "中性"},{"text": "导演的叙事手法很新颖，画面精美，但结尾有些仓促。", "label": "中性"},{"text": "演员阵容强大，但剧本太弱，浪费了这么好的演员。", "label": "负面"},{"text": "从头到尾都吸引人，今年看过最好的电影之一！", "label": "正面"}]return dict(examples=examples)# =========================== 启动时加载资源 ===========================
if __name__ == '__main__':os.makedirs('vocab', exist_ok=True)os.makedirs('models', exist_ok=True)# 加载资源load_vocab()load_models()if model is None or len(word2idx) == 0:print("模型或词表加载失败，请检查！")else:print("后端服务准备就绪，可以开始接收情感分析请求。")# 运行应用app.run(host='0.0.0.0', port=5000, debug=True)