Python - 情感分析

语义分析是为了分析受众的总体意见。它可能是对新闻、电影或关于某个讨论问题的推文的反应。通常，此类反应取自社交媒体，并汇总到一个文件中，以通过 NLP 进行分析。我们将首先定义正面和负面单词的简单案例。然后采用一种方法，将这些单词作为包含这些单词的句子的一部分进行分析。我们使用 nltk 中的 sentiment_analyzer 模块。我们首先对一个单词进行分析，然后对成对的单词（也称为二元词组）进行分析。最后，我们将标记为负面情绪的单词放入 mark_negation 函数中。

import nltk
import nltk.sentiment.sentiment_analyzer 

# Analysing for single words
def OneWord(): 
	positive_words = ['good', 'progress', 'luck']
   	text = 'Hard Work brings progress and good luck.'.split()                 
	analysis = nltk.sentiment.util.extract_unigram_feats(text, positive_words) 
	print(' ** Sentiment with one word **\n')
	print(analysis) 

# Analysing for a pair of words	
def WithBigrams(): 
	word_sets = [('Regular', 'fit'), ('fit', 'fine')] 
	text = 'Regular excercise makes you fit and fine'.split() 
	analysis = nltk.sentiment.util.extract_bigram_feats(text, word_sets) 
	print('\n*** Sentiment with bigrams ***\n') 
	print analysis

# Analysing the negation words. 
def NegativeWord():
	text = 'Lack of good health can not bring success to students'.split() 
	analysis = nltk.sentiment.util.mark_negation(text) 
	print('\n**Sentiment with Negative words**\n')
	print(analysis) 
    
OneWord()
WithBigrams() 
NegativeWord() 

运行上述程序后，将得到以下输出 -

 ** Sentiment with one word **

{'contains(luck)': False, 'contains(good)': True, 'contains(progress)': True}

*** Sentiment with bigrams ***

{'contains(fit - fine)': False, 'contains(Regular - fit)': False}

**Sentiment with Negative words**

['Lack', 'of', 'good', 'health', 'can', 'not', 'bring_NEG', 'success_NEG', 'to_NEG', 'students_NEG']