如果用jieba，做情感分析，用python示例，3.6版本

可以参考以下代码示例：

import jieba
import jieba.analyse
import jieba.posseg as pseg

# 设置停用词
stop_words = set(['的', '了', '在', '是', '我', '你', '他', '她'])

# 分词和提取关键词
def preprocess(text):
    # 去除标点符号和空格
    text = text.replace('\n', '').replace('\r', '').replace('\t', '').replace(' ', '')
    # 分词
    words = pseg.cut(text)
    # 提取名词和动词作为关键词
    keywords = []
    for word, flag in words:
        if flag.startswith('n') or flag.startswith('v'):
            if word not in stop_words:
                keywords.append(word)
    return keywords

# 加载情感词典
def load_dict(filename):
    sents = {}
    with open(filename, 'r', encoding='utf-8') as f:
        for line in f.readlines():
            s = line.strip().split('\t')
            if len(s) == 1:
                continue
            word = s[0]
            score = s[1]
            if '-' in score:
                score = score.split('-')[0]
            else:
                score = score.split('+')[0]
            sents[word] = float(score)
    return sents

# 计算情感得分
def sentiment_score(text, pos_dict, neg_dict):
    words = preprocess(text)
    pos_score, neg_score = 0, 0
    for word in words:
        if word in pos_dict:
            pos_score += pos_dict[word]
        if word in neg_dict:
            neg_score += neg_dict[word]
    return pos_score - neg_score

# 主函数
if __name__ == '__main__':
    # 加载情感词典
    pos_dict = load_dict('pos.txt')
    neg_dict = load_dict('neg.txt')
    
    # 示例
    text = '这个电影真的很好看，演员演技都很棒，剧情也很精彩！'
    score = sentiment_score(text, pos_dict, neg_dict)
    print('情感得分：', score)    

其中，pos.txt和neg.txt是情感词典文件，里面存储了正向和负向情感的词语及其得分。在本例中，我们使用jiba进行中文分词和提取关键词，然后根据情感词典计算情感得分。