基于词典的社交媒体内容的情感分析（Python实现）

import time import jieba emotion_dic = {} filename = 'BosonNLP_sentiment_score.txt' # txt文件和当前脚本在同一目录下，所以不用写具体路径 with open(filename, 'rb') as file: while True: try: senList = file.readline().decode('utf-8') # print(senList) senList = senList[:-1] senList = senList.split(' ') emotion_dic[senList[0]] = senList[1] except IndexError: break def get_emotion(score): emotion_archive = ['绝望，十分愤怒，对生活不在抱有希望', '难过，失望，抑郁', '有点小难过或者小愤怒', '轻微的难受或者不屑，想得太多啦，洗洗睡觉吧', '生活也就这样吧', '有点小开心或者小激动', '蛮开心的，生活多美好', '喜笑颜开，每天的太阳都是新的，生活充满了希望'] if score <= -3.9: return emotion_archive[0] elif -3.9 < score <= -2.5: return emotion_archive[1] elif -2.5 < score <= -1: return emotion_archive[2] elif -1 < score <= 0: return emotion_archive[3] elif 0 <score <= 1: return emotion_archive[4] elif 1 < score <= 2.5: return emotion_archive[5] elif 2.5 < score < 3.9: return emotion_archive[6] else: return emotion_archive[7] test = "才拒绝做爱情代罪的羔羊" seg_list = jieba.cut(test, cut_all=True) string = "/ ".join(seg_list) string_list = string.split('/') emotion_index = 0 time.sleep(1) print("-5分为极端消极，5分为非常高兴") for _ in range(len(string_list)): if string_list[_] in emotion_dic: emotion_index += float(emotion_dic[string_list[_]]) print(emotion_index) print(get_emotion(emotion_index))