python - 使用 python 量化情感分析

Question

我一直在Python中使用NLTK进行情感分析，它只有积极、中立和消极类别，如果我们想做情感分析并用一个数字来显示一个句子可以有多少消极或积极的程度，该怎么办？有点将其视为回归问题。有没有经过预先培训的库可以这样做？

Answer 1

我知道有几种方法可以做到这一点:

• Vader 以等级形式返回分数(0 到 1 之间)
• Stanford NLP 返回分类(即 0、1、2、3)。

NLTK 方式:

from nltk.sentiment.vader import SentimentIntensityAnalyzer as sia
sentences = ['This is the worst lunch I ever had!',
             'This is the best lunch I have ever had!!',
             'I don\'t like this lunch.',
             'I eat food for lunch.',
             'Red is a color.',
             'A really bad, horrible book, the plot was .']

hal = sia()
for sentence in sentences:
    print(sentence)
    ps = hal.polarity_scores(sentence)
    for k in sorted(ps):
        print('\t{}: {:>1.4}'.format(k, ps[k]), end='  ')
    print()

示例输出:

This is the worst lunch I ever had!
    compound: -0.6588   neg: 0.423      neu: 0.577      pos: 0.0  

斯坦福自然语言处理，Python 方式:

(请注意，这种方式需要您启动 CoreNLP 服务器的实例来运行，例如:java -mx1g -cp "*"edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 15000)

from pycorenlp import StanfordCoreNLP
stanford = StanfordCoreNLP('http://localhost:9000')

for sentence in sentences:
    print(sentence)
    result = stanford.annotate(sentence,
                               properties={
                                'annotators': 'sentiment',
                                'outputFormat': 'json',
                                'timeout': '5000'
                               })
    for s in result['sentences']:
        score = (s['sentimentValue'], s['sentiment'])
    print(f'\tScore: {score[0]}, Value: {score[1]}')

示例输出:

This is the worst lunch I ever had!
    Score: 0, Value: Verynegative