Python을 사용하여 PDF에서 통찰력 추출: 종합 가이드

이 스크립트는 효율적이고 통찰력 있는 분석을 위해 맞춤화된 PDF 처리, 텍스트 추출, 문장 토큰화, 시각화를 통한 주제 모델링 수행을 위한 강력한 워크플로우를 보여줍니다.

라이브러리 개요

• os: 운영 체제와 상호 작용하는 기능을 제공합니다.
• matplotlib.pyplot: Python에서 정적, 애니메이션 및 대화형 시각화를 만드는 데 사용됩니다.
• nltk: 자연어 처리를 위한 라이브러리 및 프로그램 모음인 Natural Language Toolkit.
• pandas: 데이터 조작 및 분석 라이브러리.
• pdftotext: PDF 문서를 일반 텍스트로 변환하기 위한 라이브러리.
• re: 정규식 일치 작업을 제공합니다.
• seaborn: matplotlib를 기반으로 한 통계 데이터 시각화 라이브러리.
• nltk.tokenize.sent_tokenize: 문자열을 문장으로 토큰화하는 NLTK 함수입니다.
• top2vec: 주제 모델링 및 의미 검색을 위한 라이브러리.
• wordcloud: 텍스트 데이터에서 단어 구름을 만들기 위한 라이브러리.

초기 설정

모듈 가져오기

import os
import matplotlib.pyplot as plt
import nltk
import pandas as pd
import pdftotext
import re
import seaborn as sns
from nltk.tokenize import sent_tokenize
from top2vec import Top2Vec
from wordcloud import WordCloud
from cleantext import clean

다음으로 punkt 토크나이저가 다운로드되었는지 확인하세요.

nltk.download('punkt')

텍스트 정규화

def normalize_text(text):
    """Normalize text by removing special characters and extra spaces,
    and applying various other cleaning options."""

    # Apply the clean function with specified parameters
    cleaned_text = clean(
        text,
        fix_unicode=True,  # fix various unicode errors
        to_ascii=True,  # transliterate to closest ASCII representation
        lower=True,  # lowercase text
        no_line_breaks=False,  # fully strip line breaks as opposed to only normalizing them
        no_urls=True,  # replace all URLs with a special token
        no_emails=True,  # replace all email addresses with a special token
        no_phone_numbers=True,  # replace all phone numbers with a special token
        no_numbers=True,  # replace all numbers with a special token
        no_digits=True,  # replace all digits with a special token
        no_currency_symbols=True,  # replace all currency symbols with a special token
        no_punct=False,  # remove punctuations
        lang="en",  # set to 'de' for German special handling
    )

    # Further clean the text by removing any remaining special characters except word characters, whitespace, and periods/commas
    cleaned_text = re.sub(r"[^\w\s.,]", "", cleaned_text)
    # Replace multiple whitespace characters with a single space and strip leading/trailing spaces
    cleaned_text = re.sub(r"\s ", " ", cleaned_text).strip()

    return cleaned_text

PDF 텍스트 추출

def extract_text_from_pdf(pdf_path):
    with open(pdf_path, "rb") as f:
        pdf = pdftotext.PDF(f)
    all_text = "\n\n".join(pdf)
    return normalize_text(all_text)

문장 토큰화

def split_into_sentences(text):
    return sent_tokenize(text)

여러 파일 처리

def process_files(file_paths):
    authors, titles, all_sentences = [], [], []
    for file_path in file_paths:
        file_name = os.path.basename(file_path)
        parts = file_name.split(" - ", 2)
        if len(parts) != 3 or not file_name.endswith(".pdf"):
            print(f"Skipping file with incorrect format: {file_name}")
            continue

        year, author, title = parts
        author, title = author.strip(), title.replace(".pdf", "").strip()

        try:
            text = extract_text_from_pdf(file_path)
        except Exception as e:
            print(f"Error extracting text from {file_name}: {e}")
            continue

        sentences = split_into_sentences(text)
        authors.append(author)
        titles.append(title)
        all_sentences.extend(sentences)
        print(f"Number of sentences for {file_name}: {len(sentences)}")

    return authors, titles, all_sentences

데이터를 CSV로 저장

def save_data_to_csv(authors, titles, file_paths, output_file):
    texts = []
    for fp in file_paths:
        try:
            text = extract_text_from_pdf(fp)
            sentences = split_into_sentences(text)
            texts.append(" ".join(sentences))
        except Exception as e:
            print(f"Error processing file {fp}: {e}")
            texts.append("")

    data = pd.DataFrame({
        "Author": authors,
        "Title": titles,
        "Text": texts
    })
    data.to_csv(output_file, index=False, quoting=1, encoding='utf-8')
    print(f"Data has been written to {output_file}")

불용어 로드 중

def load_stopwords(filepath):
    with open(filepath, "r") as f:
        stopwords = f.read().splitlines()
    additional_stopwords = ["able", "according", "act", "actually", "after", "again", "age", "agree", "al", "all", "already", "also", "am", "among", "an", "and", "another", "any", "appropriate", "are", "argue", "as", "at", "avoid", "based", "basic", "basis", "be", "been", "begin", "best", "book", "both", "build", "but", "by", "call", "can", "cant", "case", "cases", "claim", "claims", "class", "clear", "clearly", "cope", "could", "course", "data", "de", "deal", "dec", "did", "do", "doesnt", "done", "dont", "each", "early", "ed", "either", "end", "etc", "even", "ever", "every", "far", "feel", "few", "field", "find", "first", "follow", "follows", "for", "found", "free", "fri", "fully", "get", "had", "hand", "has", "have", "he", "help", "her", "here", "him", "his", "how", "however", "httpsabout", "ibid", "if", "im", "in", "is", "it", "its", "jstor", "june", "large", "lead", "least", "less", "like", "long", "look", "man", "many", "may", "me", "money", "more", "most", "move", "moves", "my", "neither", "net", "never", "new", "no", "nor", "not", "notes", "notion", "now", "of", "on", "once", "one", "ones", "only", "open", "or", "order", "orgterms", "other", "our", "out", "own", "paper", "past", "place", "plan", "play", "point", "pp", "precisely", "press", "put", "rather", "real", "require", "right", "risk", "role", "said", "same", "says", "search", "second", "see", "seem", "seems", "seen", "sees", "set", "shall", "she", "should", "show", "shows", "since", "so", "step", "strange", "style", "such", "suggests", "talk", "tell", "tells", "term", "terms", "than", "that", "the", "their", "them", "then", "there", "therefore", "these", "they", "this", "those", "three", "thus", "to", "todes", "together", "too", "tradition", "trans", "true", "try", "trying", "turn", "turns", "two", "up", "us", "use", "used", "uses", "using", "very", "view", "vol", "was", "way", "ways", "we", "web", "well", "were", "what", "when", "whether", "which", "who", "why", "with", "within", "works", "would", "years", "york", "you", "your", "suggests", "without"]
    stopwords.extend(additional_stopwords)
    return set(stopwords)

주제에서 불용어 필터링

def filter_stopwords_from_topics(topic_words, stopwords):
    filtered_topics = []
    for words in topic_words:
        filtered_topics.append([word for word in words if word.lower() not in stopwords])
    return filtered_topics

워드 클라우드 생성

def generate_wordcloud(topic_words, topic_num, palette='inferno'):
    colors = sns.color_palette(palette, n_colors=256).as_hex()
    def color_func(word, font_size, position, orientation, random_state=None, **kwargs):
        return colors[random_state.randint(0, len(colors) - 1)]

    wordcloud = WordCloud(width=800, height=400, background_color='black', color_func=color_func).generate(' '.join(topic_words))
    plt.figure(figsize=(10, 5))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis('off')
    plt.title(f'Topic {topic_num} Word Cloud')
    plt.show()

주요 실행

file_paths = [f"/home/roomal/Desktop/Dreyfus-Project/Dreyfus/{fname}" for fname in os.listdir("/home/roomal/Desktop/Dreyfus-Project/Dreyfus/") if fname.endswith(".pdf")]

authors, titles, all_sentences = process_files(file_paths)

output_file = "/home/roomal/Desktop/Dreyfus-Project/Dreyfus_Papers.csv"
save_data_to_csv(authors, titles, file_paths, output_file)

stopwords_filepath = "/home/roomal/Documents/Lists/stopwords.txt"
stopwords = load_stopwords(stopwords_filepath)

try:
    topic_model = Top2Vec(
        all_sentences,
        embedding_model="distiluse-base-multilingual-cased",
        speed="deep-learn",
        workers=6
    )
    print("Top2Vec model created successfully.")
except ValueError as e:
    print(f"Error initializing Top2Vec: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

num_topics = topic_model.get_num_topics()
topic_words, word_scores, topic_nums = topic_model.get_topics(num_topics)
filtered_topic_words = filter_stopwords_from_topics(topic_words, stopwords)

for i, words in enumerate(filtered_topic_words):
    print(f"Topic {i}: {', '.join(words)}")

keywords = ["heidegger"]
topic_words, word_scores, topic_scores, topic_nums = topic_model.search_topics(keywords=keywords, num_topics=num_topics)
filtered

_search_topic_words = filter_stopwords_from_topics(topic_words, stopwords)

for i, words in enumerate(filtered_search_topic_words):
    generate_wordcloud(words, topic_nums[i])

for i in range(reduced_num_topics):
    topic_words = topic_model.topic_words_reduced[i]
    filtered_words = [word for word in topic_words if word.lower() not in stopwords]
    print(f"Reduced Topic {i}: {', '.join(filtered_words)}")
    generate_wordcloud(filtered_words, i)

주제 수를 줄이세요

reduced_num_topics = 5
topic_mapping = topic_model.hierarchical_topic_reduction(num_topics=reduced_num_topics)

# Print reduced topics and generate word clouds
for i in range(reduced_num_topics):
    topic_words = topic_model.topic_words_reduced[i]
    filtered_words = [word for word in topic_words if word.lower() not in stopwords]
    print(f"Reduced Topic {i}: {', '.join(filtered_words)}")
    generate_wordcloud(filtered_words, i)