User emotion recognition problem in intelligent assistant system

User emotion recognition issues in intelligent assistant systems require specific code examples

The intelligent assistant system is an application based on artificial intelligence technology, and its purpose is to Provide users with fast and accurate information services and interactive experiences. In recent years, with the rapid development of artificial intelligence technology, the functions of intelligent assistant systems have become more and more abundant. From the initial speech recognition and speech synthesis to the current natural language processing, emotion recognition, etc., the relationship between the user and the system has become more and more complex. Interactions become more intelligent and humane.

However, in practical applications, intelligent assistant systems still face some challenges in user emotion recognition. Users' emotional expressions are diverse and complex, covering a variety of emotions such as anger, happiness, sadness, etc. Therefore, how to accurately identify users' emotions becomes particularly important. Below, we will introduce a user emotion recognition method based on natural language processing and give specific code examples.

Before performing user emotion recognition, you first need to establish an emotion dictionary. The emotion dictionary is a dictionary that contains various emotion words and their corresponding emotion intensity values. It can be built manually or using machine learning methods. Here we take manual construction as an example. Assume that our emotional dictionary contains the following emotional words and their emotional intensity values:

emotion_dict = {
    'happy': 1.0,
    'sad': -1.0,
    'angry': -1.5,
    'excited': 1.5,
    'calm': 0.0
}

Next, we need to perform emotion recognition on the text input by the user. A commonly used method is the sentiment weighted summation method based on sentiment words. The specific steps are as follows:

1. First, perform word segmentation processing on the text input by the user. Word segmentation is the process of splitting text into small words or phrases. You can use existing word segmentation tools or implement a simple word segmentation function yourself.

import jieba

def word_segmentation(text):
    words = jieba.cut(text) # 使用jieba进行中文分词
    return list(words)

2. Then, traverse the word segmentation results and calculate the sentiment score of each word. If the word is in the sentiment dictionary, its sentiment intensity value is added to the total score; otherwise, the word is ignored.

def sentiment_analysis(words):
    score = 0.0
    for word in words:
        if word in emotion_dict:
            score += emotion_dict[word]
    return score

3. Finally, the user’s emotional category is determined based on the score. If the score is greater than or equal to 0, it is judged to be a positive emotion; if the score is less than 0, it is judged to be a negative emotion; otherwise, it is judged to be a neutral emotion.

def emotion_recognition(score):
    if score > 0:
        return 'Positive'
    elif score < 0:
        return 'Negative'
    else:
        return 'Neutral'

The above is a user emotion recognition method based on emotion dictionary. The following is a complete sample code:

import jieba

emotion_dict = {
    'happy': 1.0,
    'sad': -1.0,
    'angry': -1.5,
    'excited': 1.5,
    'calm': 0.0
}

def word_segmentation(text):
    words = jieba.cut(text)
    return list(words)

def sentiment_analysis(words):
    score = 0.0
    for word in words:
        if word in emotion_dict:
            score += emotion_dict[word]
    return score

def emotion_recognition(score):
    if score > 0:
        return 'Positive'
    elif score < 0:
        return 'Negative'
    else:
        return 'Neutral'

text = '今天天气真好，心情很愉快！'
words = word_segmentation(text)
score = sentiment_analysis(words)
emotion = emotion_recognition(score)
print(f'Text: {text}')
print(f'Words: {words}')
print(f'Sentiment Score: {score}')
print(f'Emotion: {emotion}')

The above code example demonstrates how to perform a given text Emotion recognition, and outputs emotion categories and emotion scores. Through this method, we can use the user's emotion as an important factor to optimize the interaction and services of the intelligent assistant system, thereby improving the user experience.

Of course, the above code example is just a simple emotion recognition method, and more complex models and technologies may be needed in actual applications to improve accuracy. However, the sentiment dictionary-based approach is still a simple and effective starting point that can help us understand and apply users’ emotional needs.

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