python-3.x - 如何将情感分析脚本与聊天机器人集成以在同一控制台屏幕中分析用户的回复？

Question

我想制作一个聊天机器人，它使用情绪分析器脚本来了解我已完成聊天机器人制作的用户回复的情绪。

现在我唯一想做的就是使用这个脚本来分析用户使用我制作的聊天机器人的回复。
我应该如何将这个Sentiment_analysis.py脚本与chatbot.py文件集成来分析用户的情绪 ？

更新： 整体表现会是这样的：
聊天机器人：你今天过得怎么样？
用户：这是一个很棒的一天。我今天感到非常高兴和有动力。
用户回复：正面
情绪分数=（一些随机值）
提前谢谢你。

Answer 1

将类从情绪分析脚本导入聊天机器人脚本。然后根据您的要求做必要的事情。例如。我修改了你的聊天机器人脚本：

from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
from sentiment_analysis import Splitter, POSTagger, DictionaryTagger  # import all the classes from sentiment_analysis
import os

bot = ChatBot('Bot')
bot.set_trainer(ListTrainer)

# for files in os.listdir('C:/Users/username\Desktop\chatterbot\chatterbot_corpus\data/english/'):
# data = open('C:/Users/username\Desktop\chatterbot\chatterbot_corpus\data/english/' + files, 'r').readlines()
data = [
    "My name is Tony",
    "that's a good name",
    "Thank you",
    "How you doing?",
    "I am Fine. What about you?",
    "I am also fine. Thanks for asking."]

bot.train(data)

# I included 3 functions from sentiment_analysis here for ease of loading. Alternatively you can create a class for them in sentiment_analysis.py and import here.
def value_of(sentiment):
    if sentiment == 'positive': return 1
    if sentiment == 'negative': return -1
    return 0

def sentence_score(sentence_tokens, previous_token, acum_score):
    if not sentence_tokens:
        return acum_score
    else:
        current_token = sentence_tokens[0]
        tags = current_token[2]
        token_score = sum([value_of(tag) for tag in tags])
        if previous_token is not None:
            previous_tags = previous_token[2]
            if 'inc' in previous_tags:
                token_score *= 2.0
            elif 'dec' in previous_tags:
                token_score /= 2.0
            elif 'inv' in previous_tags:
                token_score *= -1.0
        return sentence_score(sentence_tokens[1:], current_token, acum_score + token_score)

def sentiment_score(review):
    return sum([sentence_score(sentence, None, 0.0) for sentence in review])

# create instances of all classes
splitter = Splitter()
postagger = POSTagger()
dicttagger = DictionaryTagger([ 'dicts/positive.yml', 'dicts/negative.yml',
                            'dicts/inc.yml', 'dicts/dec.yml', 'dicts/inv.yml'])

print("ChatBot is Ready...")
print("ChatBot : Welcome to my world! What is your name?")
message = input("you: ")
print("\n")

while True:
    if message.strip() != 'Bye'.lower():

        reply = bot.get_response(message)

        # process the text
        splitted_sentences = splitter.split(message)
        pos_tagged_sentences = postagger.pos_tag(splitted_sentences)
        dict_tagged_sentences = dicttagger.tag(pos_tagged_sentences)

        # find sentiment score
        score = sentiment_score(dict_tagged_sentences)

        if (score >= 1):
            print('User Reply: Positive')
        else:
            print('User Reply: Negative')

        print("Sentiment score :",score)
        print('ChatBot:',reply)

    if message.strip() == 'Bye'.lower():
        print('ChatBot: Bye')
        break
    message = input("you: ")
    print("\n")

当您遇到错误时让我知道。