NRC analysis

NRC analysis

NRC = pd.read_csv("NRC.csv",  names=["word", "sentiment", "classifaction"])
NRC_sentiments = ['anger', 'anticipation', 'disgust', 'fear', 'joy', 'negative',
                    'positive', 'sadness', 'suprise', 'trust']

def nrc_classify(word):
    return NRC[NRC['word'] == word].loc[:, 'classifaction'].tolist()

def nrc_clean(book_nrc):
    book_nrc['classifications'] = book_nrc['word2'].apply(nrc_classify)
    book_nrc = book_nrc[book_nrc['classifications'].str.len() > 0]
    
    classification_df = pd.DataFrame.from_dict(dict(book_nrc['classifications'])).transpose()
    classification_df.columns = NRC_sentiments
    
    book_nrc = book_nrc.join(classification_df)
    book_nrc = book_nrc.drop(['classifications'], axis = 1)
    return book_nrc

def nrc_context(book_bigrams):
    for e in NRC_sentiments:
        book_bigrams[e] = book_bigrams.apply(
                                        lambda x: x[e] * -1 if x['word1'] in negations else x[e], 
                                        axis = 1)