>>> from techminer2.topic_modeling import components_by_term_frame
>>> from sklearn.decomposition import LatentDirichletAllocation
>>> components_by_term_frame(
... field="author_keywords",
... #
... # TF PARAMS:
... is_binary=True,
... cooc_within=2,
... #
... # TF-IDF PARAMS:
... norm=None,
... use_idf=False,
... smooth_idf=False,
... sublinear_tf=False,
... #
... # TOP TERMS:
... n_top_terms=5,
... #
... # ITEM FILTERS:
... top_n=None,
... occ_range=(None, None),
... gc_range=(None, None),
... custom_terms=None,
... #
... # ESTIMATOR:
... sklearn_estimator=LatentDirichletAllocation(
... n_components=10,
... learning_decay=0.7,
... learning_offset=50.0,
... max_iter=10,
... batch_size=128,
... evaluate_every=-1,
... perp_tol=0.1,
... mean_change_tol=0.001,
... max_doc_update_iter=100,
... random_state=0,
... ),
... #
... # DATABASE PARAMS:
... root_dir="example/",
... database="main",
... year_filter=(None, None),
... cited_by_filter=(None, None),
... )
term FINTECH 31:5168 ... TRADING 01:0064
component ...
0 10.099987 ... 0.1
1 4.100046 ... 0.1
2 3.100012 ... 1.1
3 4.100041 ... 0.1
4 0.100000 ... 0.1
5 1.099938 ... 0.1
6 2.100033 ... 0.1
7 0.100000 ... 0.1
8 3.099955 ... 0.1
9 4.099988 ... 0.1
[10 rows x 148 columns]