Sentiment_Analysis_Code II

データ検証

df_train = pd.read_csv("train.txt",delimiter=';',names=['text','label'])
df_val = pd.read_csv("val.txt",delimiter=';',names=['text','label'])

df = pd.concat([df_train,df_val])

print("Shape of the DataFrame:",df.shape)

df.reset_index(inplace=True,drop=True)

df.head()

sns.countplot(df.label)

def custom_encoder(df):
    df.replace(to_replace ="surprise", value =1, inplace=True)
    df.replace(to_replace ="love", value =1, inplace=True)
    df.replace(to_replace ="joy", value =1, inplace=True)
    df.replace(to_replace ="fear", value =0, inplace=True)
    df.replace(to_replace ="anger", value =0, inplace=True)
    df.replace(to_replace ="sadness", value =0, inplace=True)
    
    
custom_encoder(df['label'])
sns.countplot(df.label)
#이분법적으로 나눠보니 비슷하게 분포가 되어있다.

df.head()

Data Pre-processing

#object of WordNetLemmatizer: Transformation수행
lm = WordNetLemmatizer()


def text_transformation(df_col):
    corpus = []
    for item in df_col:
        new_item = re.sub('[^a-zA-Z]',' ',str(item))
        new_item = new_item.lower()
        new_item = new_item.split()
        new_item = [lm.lemmatize(word) for word in new_item if word not in set(stopwords.words('english'))]
        corpus.append(' '.join(str(x) for x in new_item))
    return corpus
    
corpus = text_transformation(df['text'])

#Word Cloud: 데이터 시각화 기술로 내포된 text 보여줌
#

rcParams['figure.figsize'] = 20,8
word_cloud = ""
for row in corpus:
    for word in row:
        word_cloud+=" ".join(word)

wordcloud = WordCloud(width = 1000, height = 500,background_color ='white',min_font_size = 10).generate(word_cloud)
plt.imshow(wordcloud)

Bag of Words

from sklearn.feature_extraction.text import CountVectorizer

cv = CountVectorizer(ngram_range=(1,2))
traindata = cv.fit_transform(corpus)

X = traindata
y = df.label

GridSearchCV()

parameters = {'max_features': ('auto','sqrt'),
             'n_estimators': [500, 1000],
             'max_depth': [10, None],
             'min_samples_split': [5],
             'min_samples_leaf': [1],
             'bootstrap': [True]}
             
grid_search = GridSearchCV(RandomForestClassifier(),parameters,cv=5,return_train_score=True,n_jobs=-1)
grid_search.fit(X,y)
grid_search.best_params_