8-5. Sentiment Analysis || SentiWordNet

비지도학습 기반 감성 분석

SentiWordNet을 이용한 Sentiment Analysis

• WordNet Synset과 SentiWordNet SentiSynset 클래스의 이해

import nltk
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True

 

from nltk.corpus import wordnet as wn

term = 'present'

# 'present'라는 단어로 wordnet의 synsets 생성. 
synsets = wn.synsets(term)
print('synsets() 반환 type :', type(synsets))
print('synsets() 반환 값 갯수:', len(synsets))
print('synsets() 반환 값 :', synsets)


synsets() 반환 type : <class 'list'>
synsets() 반환 값 갯수: 18
synsets() 반환 값 : [Synset('present.n.01'), Synset('present.n.02'), Synset('present.n.03'), Synset('show.v.01'), Synset('present.v.02'), Synset('stage.v.01'), Synset('present.v.04'), Synset('present.v.05'), Synset('award.v.01'), Synset('give.v.08'), Synset('deliver.v.01'), Synset('introduce.v.01'), Synset('portray.v.04'), Synset('confront.v.03'), Synset('present.v.12'), Synset('salute.v.06'), Synset('present.a.01'), Synset('present.a.02')]

 

for synset in synsets :
    print('##### Synset name : ', synset.name(),'#####')
    print('POS :',synset.lexname())
    print('Definition:',synset.definition())
    print('Lemmas:',synset.lemma_names())
    
    
##### Synset name :  present.n.01 #####
POS : noun.time
Definition: the period of time that is happening now; any continuous stretch of time including the moment of speech
Lemmas: ['present', 'nowadays']
##### Synset name :  present.n.02 #####
POS : noun.possession
Definition: something presented as a gift
Lemmas: ['present']
##### Synset name :  present.n.03 #####
POS : noun.communication
Definition: a verb tense that expresses actions or states at the time of speaking
Lemmas: ['present', 'present_tense']
##### Synset name :  show.v.01 #####
POS : verb.perception
Definition: give an exhibition of to an interested audience
Lemmas: ['show', 'demo', 'exhibit', 'present', 'demonstrate']
##### Synset name :  present.v.02 #####
POS : verb.communication
Definition: bring forward and present to the mind
Lemmas: ['present', 'represent', 'lay_out']
##### Synset name :  stage.v.01 #####
POS : verb.creation
Definition: perform (a play), especially on a stage
Lemmas: ['stage', 'present', 'represent']
##### Synset name :  present.v.04 #####
POS : verb.possession
Definition: hand over formally
Lemmas: ['present', 'submit']
##### Synset name :  present.v.05 #####
POS : verb.stative
Definition: introduce
Lemmas: ['present', 'pose']
##### Synset name :  award.v.01 #####
POS : verb.possession
Definition: give, especially as an honor or reward
Lemmas: ['award', 'present']
##### Synset name :  give.v.08 #####
POS : verb.possession
Definition: give as a present; make a gift of
Lemmas: ['give', 'gift', 'present']
##### Synset name :  deliver.v.01 #####
POS : verb.communication
Definition: deliver (a speech, oration, or idea)
Lemmas: ['deliver', 'present']
##### Synset name :  introduce.v.01 #####
POS : verb.communication
Definition: cause to come to know personally
Lemmas: ['introduce', 'present', 'acquaint']
##### Synset name :  portray.v.04 #####
POS : verb.creation
Definition: represent abstractly, for example in a painting, drawing, or sculpture
Lemmas: ['portray', 'present']
##### Synset name :  confront.v.03 #####
POS : verb.communication
Definition: present somebody with something, usually to accuse or criticize
Lemmas: ['confront', 'face', 'present']
##### Synset name :  present.v.12 #####
POS : verb.communication
Definition: formally present a debutante, a representative of a country, etc.
Lemmas: ['present']
##### Synset name :  salute.v.06 #####
POS : verb.communication
Definition: recognize with a gesture prescribed by a military regulation; assume a prescribed position
Lemmas: ['salute', 'present']
##### Synset name :  present.a.01 #####
POS : adj.all
Definition: temporal sense; intermediate between past and future; now existing or happening or in consideration
Lemmas: ['present']
##### Synset name :  present.a.02 #####
POS : adj.all
Definition: being or existing in a specified place
Lemmas: ['present']

 

# synset 객체를 단어별로 생성합니다. 
tree = wn.synset('tree.n.01')
lion = wn.synset('lion.n.01')
tiger = wn.synset('tiger.n.02')
cat = wn.synset('cat.n.01')
dog = wn.synset('dog.n.01')

entities = [tree , lion , tiger , cat , dog]
similarities = []
entity_names = [ entity.name().split('.')[0] for entity in entities]

# 단어별 synset 들을 iteration 하면서 다른 단어들의 synset과 유사도를 측정합니다. 
for entity in entities:
    similarity = [ round(entity.path_similarity(compared_entity), 2)  for compared_entity in entities ]
    similarities.append(similarity)
    
# 개별 단어별 synset과 다른 단어의 synset과의 유사도를 DataFrame형태로 저장합니다.  
similarity_df = pd.DataFrame(similarities , columns=entity_names,index=entity_names)
similarity_df


	tree	lion	tiger	cat	dog
tree	1.00	0.07	0.07	0.08	0.12
lion	0.07	1.00	0.33	0.25	0.17
tiger	0.07	0.33	1.00	0.25	0.17
cat	0.08	0.25	0.25	1.00	0.20
dog	0.12	0.17	0.17	0.20	1.00

 

import nltk
from nltk.corpus import sentiwordnet as swn

senti_synsets = list(swn.senti_synsets('slow'))
print('senti_synsets() 반환 type :', type(senti_synsets))
print('senti_synsets() 반환 값 갯수:', len(senti_synsets))
print('senti_synsets() 반환 값 :', senti_synsets)



senti_synsets() 반환 type : <class 'list'>
senti_synsets() 반환 값 갯수: 11
senti_synsets() 반환 값 : [SentiSynset('decelerate.v.01'), SentiSynset('slow.v.02'), SentiSynset('slow.v.03'), SentiSynset('slow.a.01'), SentiSynset('slow.a.02'), SentiSynset('dense.s.04'), SentiSynset('slow.a.04'), SentiSynset('boring.s.01'), SentiSynset('dull.s.08'), SentiSynset('slowly.r.01'), SentiSynset('behind.r.03')]

 

import nltk
from nltk.corpus import sentiwordnet as swn

father = swn.senti_synset('father.n.01')
print('father 긍정감성 지수: ', father.pos_score())
print('father 부정감성 지수: ', father.neg_score())
print('father 객관성 지수: ', father.obj_score())
print('\n')
fabulous = swn.senti_synset('fabulous.a.01')
print('fabulous 긍정감성 지수: ',fabulous .pos_score())
print('fabulous 부정감성 지수: ',fabulous .neg_score())


father 긍정감성 지수:  0.0
father 부정감성 지수:  0.0
father 객관성 지수:  1.0


fabulous 긍정감성 지수:  0.875
fabulous 부정감성 지수:  0.125

 

from nltk.corpus import wordnet as wn

# 간단한 NTLK PennTreebank Tag를 기반으로 WordNet기반의 품사 Tag로 변환
def penn_to_wn(tag):
    if tag.startswith('J'):
        return wn.ADJ
    elif tag.startswith('N'):
        return wn.NOUN
    elif tag.startswith('R'):
        return wn.ADV
    elif tag.startswith('V'):
        return wn.VERB
    return

 

from nltk.stem import WordNetLemmatizer
from nltk.corpus import sentiwordnet as swn
from nltk import sent_tokenize, word_tokenize, pos_tag

def swn_polarity(text):
    # 감성 지수 초기화 
    sentiment = 0.0
    tokens_count = 0
    
    lemmatizer = WordNetLemmatizer()
    raw_sentences = sent_tokenize(text)
    # 분해된 문장별로 단어 토큰 -> 품사 태깅 후에 SentiSynset 생성 -> 감성 지수 합산 
    for raw_sentence in raw_sentences:
        # NTLK 기반의 품사 태깅 문장 추출  
        tagged_sentence = pos_tag(word_tokenize(raw_sentence))
        for word , tag in tagged_sentence:
            
            # WordNet 기반 품사 태깅과 어근 추출
            wn_tag = penn_to_wn(tag)
            if wn_tag not in (wn.NOUN , wn.ADJ, wn.ADV):
                continue                   
            lemma = lemmatizer.lemmatize(word, pos=wn_tag)
            if not lemma:
                continue
            # 어근을 추출한 단어와 WordNet 기반 품사 태깅을 입력해 Synset 객체를 생성. 
            synsets = wn.synsets(lemma , pos=wn_tag)
            if not synsets:
                continue
            # sentiwordnet의 감성 단어 분석으로 감성 synset 추출
            # 모든 단어에 대해 긍정 감성 지수는 +로 부정 감성 지수는 -로 합산해 감성 지수 계산. 
            synset = synsets[0]
            swn_synset = swn.senti_synset(synset.name())
            sentiment += (swn_synset.pos_score() - swn_synset.neg_score())           
            tokens_count += 1
    
    if not tokens_count:
        return 0
    
    # 총 score가 0 이상일 경우 긍정(Positive) 1, 그렇지 않을 경우 부정(Negative) 0 반환
    if sentiment >= 0 :
        return 1
    
    return 0

 

review_df['preds'] = review_df['review'].apply( lambda x : swn_polarity(x) )
y_target = review_df['sentiment'].values
preds = review_df['preds'].values

 

from sklearn.metrics import accuracy_score, confusion_matrix, precision_score 
from sklearn.metrics import recall_score, f1_score, roc_auc_score

print(confusion_matrix( y_target, preds))
print("정확도:", accuracy_score(y_target , preds))
print("정밀도:", precision_score(y_target , preds))
print("재현율:", recall_score(y_target, preds))



[[7668 4832]
 [3636 8864]]
정확도: 0.66128
정밀도: 0.647196261682243
재현율: 0.70912

 

VADER lexicon을 이용한 Sentiment Analysis

from nltk.sentiment.vader import SentimentIntensityAnalyzer

senti_analyzer = SentimentIntensityAnalyzer()
senti_scores = senti_analyzer.polarity_scores(review_df['review'][0])
print(senti_scores)


# {'neg': 0.13, 'neu': 0.743, 'pos': 0.127, 'compound': -0.7943}

 

def vader_polarity(review,threshold=0.1):
    analyzer = SentimentIntensityAnalyzer()
    scores = analyzer.polarity_scores(review)
    
    # compound 값에 기반하여 threshold 입력값보다 크면 1, 그렇지 않으면 0을 반환 
    agg_score = scores['compound']
    final_sentiment = 1 if agg_score >= threshold else 0
    return final_sentiment

# apply lambda 식을 이용하여 레코드별로 vader_polarity( )를 수행하고 결과를 'vader_preds'에 저장
review_df['vader_preds'] = review_df['review'].apply( lambda x : vader_polarity(x, 0.1) )
y_target = review_df['sentiment'].values
vader_preds = review_df['vader_preds'].values

 

print('#### VADER 예측 성능 평가 ####')
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score 
from sklearn.metrics import recall_score, f1_score, roc_auc_score

print(confusion_matrix( y_target, vader_preds))
print("정확도:", accuracy_score(y_target , vader_preds))
print("정밀도:", precision_score(y_target , vader_preds))
print("재현율:", recall_score(y_target, vader_preds))


#### VADER 예측 성능 평가 ####
[[ 6736  5764]
 [ 1867 10633]]
정확도: 0.69476
정밀도: 0.6484722815149113
재현율: 0.85064