<jupyter_start><jupyter_text>Different topics:    1.     2.    3.    4.<jupyter_code>## Now applying LDA (4 categories) to our dataset

text_pred = id2word.doc2bow(posts['processed_title_no_stop'][0].split())
lda_model_bigrams.get_document_topics(text_pred)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12195121951219512, 'fraction_numerical': 0.027874564459930314, 'mean_word_length': 6.384615384615385, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '591819', 'license': 'MIT', 'n_tokens_mistral': 113, 'n_tokens_neox': 109, 'n_words': 17, 'path': 'notebooks/02-vm-title-modeling.ipynb', 'repo_name': 'vmmadathil/forums_aca_needs', 'size': '287'}