口语文本的情感分析

Python 语音语言处理

Daniel Bourke

Machine Learning Engineer/YouTube Creator

安装情感分析库

$ pip install nltk

# 下载所需的 NLTK 包
import nltk
nltk.download("punkt")
nltk.download("vader_lexicon")

使用 VADER 进行情感分析

# 导入情感分析类
from nltk.sentiment.vader import SentimentIntensityAnalyzer

# 创建情感分析实例
sid = SentimentIntensityAnalyzer()

# 在负面文本上测试情感分析
print(sid.polarity_scores("This customer service is terrible."))

{'neg': 0.437, 'neu': 0.563, 'pos': 0.0, 'compound': -0.4767}

转写文本的情感分析

# 转写 call_3 的客户声道
call_3_channel_2_text = transcribe_audio("call_3_channel_2.wav")
print(call_3_channel_2_text)

"hey Dave is this any better do I order products are currently on July 1st and I haven't 
received the product a three-week step down this parable 6987 5"

# 对 call_3 的客户声道做情感分析
sid.polarity_scores(call_3_channel_2_text)

{'neg': 0.0, 'neu': 0.892, 'pos': 0.108, 'compound': 0.4404}

逐句分析

call_3_paid_api_text = "Okay. Yeah. Hi, Diane. This is paid on this call and obvi..."

# 导入句子分割器
from nltk.tokenize import sent_tokenize

# 对每句计算情感
for sentence in sent_tokenize(call_3_paid_api_text):
  print(sentence)
  print(sid.polarity_scores(sentence))

逐句分析

Okay.
{'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.2263}
Yeah.
{'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.296}
Hi, Diane.
{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}
This is paid on this call and obviously the status of my orders at three weeks ago, 
and that service is terrible.
{'neg': 0.129, 'neu': 0.871, 'pos': 0.0, 'compound': -0.4767}
Is this any better?
{'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404}
Yes...

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Python 语音语言处理