如何将情感分析脚本与聊天机器人集成在一起，在同一控制台屏幕上分析用户的回复？ [英] How to integrate the sentiment analysis script with the chatbot for analysing the user's reply in the same console screen?

问题描述

我想做一个聊天机器人，它使用情绪分析器脚本来了解用户回复的情绪，我已经完成了聊天机器人的制作。

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。
我应该如何将此sentiment_analysis.py脚本与chat bot.py文件集成以分析用户的情绪？

更新： 整体表现如下：
聊天机器人：今天过得怎么样？
用户：这是令人惊叹的一天。今天我感到非常振奋和充满动力。
用户回复：肯定
情绪得分=(某个随机值)
提前感谢您。

推荐答案

将类从情感分析脚本导入到聊天机器人脚本。然后根据你的要求做一些必要的事情。例如。我修改了您的聊天机器人脚本：

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/usernameDesktopchatterbotchatterbot_corpusdata/english/'):
# data = open('C:/Users/usernameDesktopchatterbotchatterbot_corpusdata/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("
")

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("
")

收到错误时通知我。

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