Python - 情感分析

语义分析是关于分析观众的一般意见。这可能是对一则新闻，电影或任何有关正在讨论的事项的推文的反应。通常，此类反应来自社交媒体，并通过NLP分组到文件中进行分析。我们将首先简单地定义正面和负面的单词。然后采用一种方法来分析这些单词作为句子的一部分使用这些单词。我们使用nltk中的sentiment_analyzer模块。我们首先用一个单词进行分析，然后用成对单词进行分析，也称为双字组。最后，我们用 mark_negation 函数中定义的负面情绪标记单词。

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']