Sure, here's the entire code:
```python
import pandas as pd
import numpy as np
import nltk
from nltk.corpus import stopwords, wordnet
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.utils.class_weight import compute_class_weight
from sklearn.metrics import f1_score
from sklearn.feature_extraction.text import TfidfVectorizer
from keras.models import Sequential
from keras.layers import Dense, LSTM
from keras.callbacks import EarlyStopping
from tqdm import tqdm
import seaborn as sns
import matplotlib.pyplot as plt

# Set the seed value for reproducibility
np.random.seed(42)

# Step 1: Load the data and perform EDA
train_df = pd.read_csv("train.csv")

class_counts = train_df["label"].value_counts()
num_classes = len(class_counts)
print(f"Class counts:\n{class_counts}")
print(f"\nNumber of classes: {num_classes}")

# Visualize the class distribution
sns.countplot(x="label", data=train_df)
plt.title("Class Distribution")
plt.show()

# Step 2: Download required NLTK resources
nltk.download('omw-1.4')
nltk.download('wordnet')

# Step 3: Tokenization
def tokenize(text):
    return word_tokenize(text)

# Step 4: Normalization
def normalize(words):
    return [word.lower() for word in words]

# Step 5: Stop word removal
def remove_stopwords(words):
    stop_words = set(stopwords.words('english'))
    return [word for word in words if word not in stop_words]

# Step 6: Spell correction
def correct_spelling(words):
    corrected_words = []
    for word in words:
        corrected_words.append(TextBlob(word).correct().string)
    return corrected_words

# Step 7: Lemmatization
def lemmatize(words):
    lemmatizer = WordNetLemmatizer()
    lemmatized_words = []
    for word in words:
        pos_tag = get_wordnet_pos(word)
        if pos_tag:
            lemmatized_words.append(lemmatizer.lemmatize(word, pos=pos_tag))
        else:
            lemmatized_words.append(word)
    return lemmatized_words

def get_wordnet_pos(word):
    """Map POS tag to first character used by WordNetLemmatizer"""
    tag = nltk.pos_tag([word])[0][1][0].upper()
    tag_dict = {"J": wordnet.ADJ,
                "N": wordnet.NOUN,
                "V": wordnet.VERB,
                "R": wordnet.ADV}
    return tag_dict.get(tag, wordnet.NOUN)

# Apply the pre-processing steps to the text data
def preprocess_text(text):
    words = tokenize(text)
    words = normalize(words)
    words = remove_stopwords(words)
    words = correct_spelling(words)
    words = lemmatize(words)
    return ' '.join(words)

train_df['text'] = train_df['text'].apply(preprocess_text)

# Step 8: Prepare the data
X_train, X_val, y_train, y_val = train_test_split(train_df['text'], train_df['label'], test_size=0.2, random_state=42)

# Compute class weights
class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)

# Convert text data into a matrix of TF-IDF features
vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(X_train)
X_val = vectorizer.transform(X_val
```
물론 여기 전체 코드가 있습니다:
'''python
임포트 판다를 PD로
import numpy as np
import nltk
nltk.corpus에서 중지어, 워드넷을 가져옵니다.
nltk.stem에서 워드넷 렘마타이저를 임포트합니다.
nltk.tokenize에서 word_tokenize를 가져옵니다.
sklearn.model_selection에서 train_test_split, StratifiedKFold를 임포트합니다.
sklearn.utils.class_weight에서 compute_class_weight를 가져옵니다.
sklearn.metrics에서 f1_score 임포트
sklearn.feature_extraction.text에서 TfidfVectorizer를 임포트합니다.
keras.models에서 Sequential 임포트
keras.layers에서 Dense, LSTM을 가져옵니다.
keras.callbacks에서 EarlyStopping을 가져옵니다.
tqdm에서 tqdm을 가져옵니다.
seaborn을 SNS로 가져 오기
matplotlib.pyplot을 plt로 가져옵니다.

# 재현성을 위한 시드 값 설정
np.random.seed(42)

# 1단계: 데이터 로드 및 EDA 수행
train_df = pd.read_csv("train.csv")

class_counts = train_df["label"].value_counts()
num_classes = len(class_counts)
print(f"클래스 수:\n{class_counts}")
print(f"\n클래스 수: {num_classes}")

# 클래스 분포 시각화하기
sns.countplot(x="label", data=train_df)
plt.title("클래스 분포")
plt.show()

# 2단계: 필요한 NLTK 리소스 다운로드
nltk.download('omw-1.4')
nltk.download('wordnet')

# 3단계: 토큰화
def tokenize(text):
    RETURN WORD_TOKENIZE(TEXT)

# 4단계: 정규화
def normalize(words):
    return [word.lower() for word in words]

# 5단계: 단어 제거 중지
def remove_stopwords(words):
    stop_words = set(stopwords.words('english'))
    return [단어가 stop_words에 없으면 단어의 단어에 대한 단어]

# 6단계: 맞춤법 수정
def correct_spelling(words):
    corrected_words = []
    단어의 단어에 대해
        corrected_words.append(TextBlob(word).correct().string)
    반환 corrected_words

# 7단계: 레매타이제이션
def lemmatize(words):
    lemmatizer = WordNetLemmatizer()
    lemmatized_words = []
    단어의 단어에 대해:
        pos_tag = get_wordnet_pos(word)
        if pos_tag:
            lemmatized_words.append(lemmatizer.lemmatize(word, pos=pos_tag))
        else:
            lemmatized_words.append(word)
    반환 lemmatized_words

def get_wordnet_pos(word):
    """POS 태그를 WordNetLemmatizer에서 사용하는 첫 번째 문자로 매핑"""
    tag = nltk.pos_tag([word])[0][1][0].upper()
    tag_dict = {"J": wordnet.ADJ,
                "N": wordnet.NOUN,
                "V": wordnet.VERB,
                "R": wordnet.ADV}
    return tag_dict.get(tag, wordnet.NOUN)

# 텍스트 데이터에 전처리 단계를 적용합니다.
def preprocess_text(text):
    words = 토큰화(텍스트)
    words = 정규화(words)
    words = remove_stopwords(words)
    words = correct_spelling(words)
    단어 = lemmatize(단어)
    반환 ' '.join(words)

train_df['text'] = train_df['text'].apply(preprocess_text)

# 8단계: 데이터 준비
X_train, X_val, y_train, y_val = train_test_split(train_df['text'], train_df['label'], test_size=0.2, random_state=42)

# 클래스 가중치 계산
class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)

# 텍스트 데이터를 TF-IDF 특징 행렬로 변환합니다.
벡터라이저 = TfidfVectorizer()
X_train = 벡터라이저.fit_transform(X_train)
X_val = 벡터라이저.변환(X_val
```