弹幕情感分析

barrage/analysisEmotion.py

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+'''
+弹幕情感分析
+利用自然语言处理模型进行弹幕的情感分析，如果没有对应的模型，则会自动下载模型(450MB)
+模型分析速度较慢，文本量大，可以酌情考虑减少一些弹幕数量，当前程序仅选取了1000条弹幕
+如果弹幕情感难以分析或是有敏感词，则会跳过当前弹幕
+
+sep是弹幕文本的分隔标志
+filePath是弹幕文本的路径
+schema是模型数据获取的键值
+model是模型文件
+savePath是柱形图保存路径
+
+ps.柱形图的可能性是指情感识别为真的平均可能性，不是弹幕情感为正向或负向的可能性
+'''
+
+from paddlenlp import Taskflow
+import matplotlib.pyplot as plt
+
+# 加载弹幕字符文本
+def loadText(sep, filePath):
+    with open(filePath, 'r', encoding='utf-8') as file:
+        text = file.read()
+    t_list = text.split(sep)
+    return t_list
+
+# 加载自然语言处理模型
+def loadModel(schema, model):
+    ie = Taskflow('information_extraction', schema=schema, model=model)
+    return ie
+
+# 计算情感方向的数量以及平均的可能性
+def emoChange(emo, pro, count, probability):
+    if emo == '正向':
+        count[0] += 1
+        probability[0] = probability[0] + (pro - probability[0])/count[0]
+    else:
+        count[1] += 1
+        probability[1] = probability[1] + (pro - probability[1])/count[1]
+
+# 绘制柱形图
+def createBar(count, probability, savePath):
+    x_data = [f'正向(可能性：{probability[0]})', f'负向(可能性：{probability[1]})']
+    y_data = count
+    plt.rcParams["font.sans-serif"] = ["SimHei"]
+    plt.rcParams["axes.unicode_minus"] = False
+    plt.figure(figsize=(10, 7))
+    for i in range(len(x_data)):
+        plt.bar(x_data[i], y_data[i], width=0.7)
+    plt.title("弹幕情感方向数量统计")
+    plt.text(x_data[0], y_data[0]+0.01, count[0], ha="center", va="bottom", fontsize=17)
+    plt.text(x_data[1], y_data[1]+0.01, count[1], ha="center", va="bottom", fontsize=17)
+    plt.xlabel("弹幕情感方向")
+    plt.ylabel("数量")
+    plt.savefig(fname=savePath, dpi=500)
+    plt.show()
+
+
+def main():
+    sep = ','
+    filePath = './docs/allBarrage.txt'
+    schema = '情感倾向[正向，负向]'
+    model = 'uie-base'
+    savePath = './docs/emoImg.png'
+
+    t_list = loadText(sep, filePath)
+    ie = loadModel(schema, model)
+
+    count = [0, 0]
+    probability = [0, 0]
+
+    for i in range(1000):
+        if i%100 ==0:
+            print(f'当前正在处理第{i}条弹幕')
+        if schema not in ie(t_list[i])[0]:
+            continue
+        emo = ie(t_list[i])[0][schema][0]['text']
+        pro = ie(t_list[i])[0][schema][0]['probability']
+        emoChange(emo, pro, count, probability)
+
+    createBar(count, probability, savePath)
+
+    print(count)
+    print(probability)
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+jieba==0.42.1
+matplotlib==3.9.2
+numpy==2.1.1
+openpyxl==3.1.5
+paddlepaddle==3.0.0b1
+paddlenlp==2.6.1
+pandas==2.2.2
+Pillow==10.4.0
+Requests==2.32.3
+scikit_learn==1.5.2
+wordcloud==1.9.3