8-4. IMDB 영화평 || 지도학습 기반 감성 분석

labeledTrainData.tsv.zip

12.96MB

지도학습 기반 감성 분석 실습

import pandas as pd

review_df = pd.read_csv('./labeledTrainData.tsv', header=0, sep="\t", quoting=3)
review_df.head(3)


id	sentiment	review
0	"5814_8"	1	"With all this stuff going down at the moment ...
1	"2381_9"	1	"\"The Classic War of the Worlds\" by Timothy ...
2	"7759_3"	0	"The film starts with a manager (Nicholas Bell...

 

print(review_df['review'][0])



"With all this stuff going down at the moment with MJ i've started listening to his music, watching the odd documentary here and there, watched The Wiz and watched Moonwalker again. Maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent. Moonwalker is part biography, part feature film which i remember going to see at the cinema when it was originally released. Some of it has subtle messages about MJ's feeling towards the press and also the obvious message of drugs are bad m'kay.<br /><br />Visually impressive but of course this is all about Michael Jackson so unless you remotely like MJ in anyway then you are going to hate this and find it boring. Some may call MJ an egotist for consenting to the making of this movie BUT MJ and most of his fans would say that he made it for the fans which if true is really nice of him.<br /><br />The actual feature film bit when it finally starts is only on for 20 minutes or so excluding the Smooth Criminal sequence and Joe Pesci is convincing as a psychopathic all powerful drug lord. Why he wants MJ dead so bad is beyond me. Because MJ overheard his plans? Nah, Joe Pesci's character ranted that he wanted people to know it is he who is supplying drugs etc so i dunno, maybe he just hates MJ's music.<br /><br />Lots of cool things in this like MJ turning into a car and a robot and the whole Speed Demon sequence. Also, the director must have had the patience of a saint when it came to filming the kiddy Bad sequence as usually directors hate working with one kid let alone a whole bunch of them performing a complex dance scene.<br /><br />Bottom line, this movie is for people who like MJ on one level or another (which i think is most people). If not, then stay away. It does try and give off a wholesome message and ironically MJ's bestest buddy in this movie is a girl! Michael Jackson is truly one of the most talented people ever to grace this planet but is he guilty? Well, with all the attention i've gave this subject....hmmm well i don't know because people can be different behind closed doors, i know this for a fact. He is either an extremely nice but stupid guy or one of the most sickest liars. I hope he is not the latter."

 

데이터 사전 처리 html태그 제거 및 숫자문자 제거

import re

# <br> html 태그는 replace 함수로 공백으로 변환
review_df['review'] = review_df['review'].str.replace('<br />',' ')

# 파이썬의 정규 표현식 모듈인 re를 이용하여 영어 문자열이 아닌 문자는 모두 공백으로 변환 
review_df['review'] = review_df['review'].apply( lambda x : re.sub("[^a-zA-Z]", " ", x) )

 

학습/테스트 데이터 분리

from sklearn.model_selection import train_test_split

class_df = review_df['sentiment']
feature_df = review_df.drop(['id','sentiment'], axis=1, inplace=False)

X_train, X_test, y_train, y_test= train_test_split(feature_df, class_df, test_size=0.3, random_state=156)

X_train.shape, X_test.shape

# ((17500, 1), (7500, 1))

 

Pipeline을 통해 Count기반 피처 벡터화 및 머신러닝 학습/예측/평가

from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, roc_auc_score

# 스톱 워드는 English, filtering, ngram은 (1,2)로 설정해 CountVectorization수행. 
# LogisticRegression의 C는 10으로 설정. 
pipeline = Pipeline([
    ('cnt_vect', CountVectorizer(stop_words='english', ngram_range=(1,2) )),
    ('lr_clf', LogisticRegression(C=10))])

# Pipeline 객체를 이용하여 fit(), predict()로 학습/예측 수행. predict_proba()는 roc_auc때문에 수행.  
pipeline.fit(X_train['review'], y_train)
pred = pipeline.predict(X_test['review'])
pred_probs = pipeline.predict_proba(X_test['review'])[:,1]

print('예측 정확도는 {0:.4f}, ROC-AUC는 {1:.4f}'.format(accuracy_score(y_test ,pred),
                                         roc_auc_score(y_test, pred_probs)))
                                         
                                         
                                         
# 예측 정확도는 0.8860, ROC-AUC는 0.9503

 

Pipeline을 통해 TF-IDF기반 피처 벡터화 및 머신러닝 학습/예측/평가

# 스톱 워드는 english, filtering, ngram은 (1,2)로 설정해 TF-IDF 벡터화 수행. 
# LogisticRegression의 C는 10으로 설정. 
pipeline = Pipeline([
    ('tfidf_vect', TfidfVectorizer(stop_words='english', ngram_range=(1,2) )),
    ('lr_clf', LogisticRegression(C=10))])

pipeline.fit(X_train['review'], y_train)
pred = pipeline.predict(X_test['review'])
pred_probs = pipeline.predict_proba(X_test['review'])[:,1]

print('예측 정확도는 {0:.4f}, ROC-AUC는 {1:.4f}'.format(accuracy_score(y_test ,pred),
                                         roc_auc_score(y_test, pred_probs)))
                                         
                                         
                                         
# 예측 정확도는 0.8936, ROC-AUC는 0.9598