Python 情感分析

语义分析是关于分析观众的总体意见。它可以是对某个新闻、电影或是关于某个讨论话题的推文的反应。通常，这些反应来自社交媒体，并被合并到一个文件中，通过自然语言处理进行分析。我们首先简单地定义积极和消极的词汇。然后采取一种方法来分析那些包含这些词汇的句子。我们使用nltk中的sentiment_analyzer模块。我们首先使用一个词进行分析，然后使用成对的词也称为bigrams进行分析。最后，我们使用 mark_negation 函数将带有消极情绪的词标记出来。

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()

当我们运行上面的程序时，我们会得到以下的输出 –

** 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']