<jupyter_start><jupyter_text>Import Data<jupyter_code>df = pd.read_csv("data/kompas.csv")
df.head()<jupyter_output><empty_output><jupyter_text>Extract BoW<jupyter_code>from sklearn.feature_extraction.text import CountVectorizer
bow = CountVectorizer(ngram_range=(1,2), tokenizer=word_tokenize, stop_words=sw_indo, min_df=5)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12464589235127478, 'fraction_numerical': 0.0084985835694051, 'mean_word_length': 15.857142857142858, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '73039', 'license': 'MIT', 'n_tokens_mistral': 140, 'n_tokens_neox': 139, 'n_words': 13, 'path': '18 - Unsupervised Learning/02 - Dimensionality Reduction/.ipynb_checkpoints/Part 4 - Topic Modeling menggunakan LSA dan LDA-checkpoint.ipynb', 'repo_name': 'rendywijayaa/Unsupervised_Deep_Learning', 'size': '353'}