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'''
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弹幕情感分析
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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)
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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
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如果弹幕情感难以分析或是有敏感词，则会跳过当前弹幕
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sep是弹幕文本的分隔标志
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filePath是弹幕文本的路径
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schema是模型数据获取的键值
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model是模型文件
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savePath是柱形图保存路径
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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
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'''
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from paddlenlp import Taskflow
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import matplotlib.pyplot as plt
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# 加载弹幕字符文本
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def loadText(sep, filePath):
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with open(filePath, 'r', encoding='utf-8') as file:
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text = file.read()
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t_list = text.split(sep)
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return t_list
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# 加载自然语言处理模型
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def loadModel(schema, model):
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ie = Taskflow('information_extraction', schema=schema, model=model)
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return ie
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# 计算情感方向的数量以及平均的可能性
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def emoChange(emo, pro, count, probability):
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if emo == '正向':
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count[0] += 1
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probability[0] = probability[0] + (pro - probability[0])/count[0]
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else:
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count[1] += 1
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probability[1] = probability[1] + (pro - probability[1])/count[1]
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# 绘制柱形图
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def createBar(count, probability, savePath):
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x_data = [f'正向(可能性：{probability[0]})', f'负向(可能性：{probability[1]})']
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y_data = count
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plt.rcParams["font.sans-serif"] = ["SimHei"]
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plt.rcParams["axes.unicode_minus"] = False
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plt.figure(figsize=(10, 7))
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for i in range(len(x_data)):
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plt.bar(x_data[i], y_data[i], width=0.7)
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plt.title("弹幕情感方向数量统计")
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plt.text(x_data[0], y_data[0]+0.01, count[0], ha="center", va="bottom", fontsize=17)
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plt.text(x_data[1], y_data[1]+0.01, count[1], ha="center", va="bottom", fontsize=17)
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plt.xlabel("弹幕情感方向")
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plt.ylabel("数量")
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plt.savefig(fname=savePath, dpi=500)
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plt.show()
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def main():
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sep = ','
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filePath = './docs/allBarrage.txt'
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schema = '情感倾向[正向，负向]'
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model = 'uie-base'
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savePath = './docs/emoImg.png'
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t_list = loadText(sep, filePath)
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ie = loadModel(schema, model)
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count = [0, 0]
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probability = [0, 0]
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for i in range(1000):
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if i%100 ==0:
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print(f'当前正在处理第{i}条弹幕')
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if schema not in ie(t_list[i])[0]:
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continue
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emo = ie(t_list[i])[0][schema][0]['text']
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pro = ie(t_list[i])[0][schema][0]['probability']
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emoChange(emo, pro, count, probability)
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createBar(count, probability, savePath)
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print(count)
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print(probability)
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if __name__ == '__main__':
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main()
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