Create a data frame summarizing the contents of each topic in a model
A list (or S3 object) with three named matrices: phi, theta, and gamma. These conform to outputs of many of textmineR's native topic modeling functions such as FitLdaModel.
'prevalence' is normalized to sum to 100. If your 'theta' matrix has negative values (as may be the case with an LSA model), a constant is added so that the least prevalent topic has a prevalence of 0.
'coherence' is calculated using CalcProbCoherence.
'label' is assigned using the top label from LabelTopics. This requires an "assignment" matrix. This matrix is like a "theta" matrix except that it is binary. A topic is "in" a document or it is not. The assignment is made by comparing each value of theta to the minimum of the largest value for each row of theta (each document). This ensures that each document has at least one topic assigned to it.
An object of class
tibble with 6 columns: 'topic' is the
name of the topic, 'prevalence' is the rough prevalence of the topic
in all documents across the corpus, 'coherence' is the probabilistic
coherence of the topic, 'top_terms_phi' are the top 5 terms for each
topic according to P(word|topic), 'top_terms_gamma' are the top 5 terms
for each topic according to P(topic|word).
1 2 3 4
Loading required package: Matrix Attaching package: 'textmineR' The following object is masked from 'package:Matrix': update The following object is masked from 'package:stats': update dtm does not appear to contain ngrams. Using unigrams but ngrams will work much better. topic label_1 prevalence coherence t_1 t_1 health 2.81 0.054 t_2 t_2 cells 3.34 0.413 t_3 t_3 diabetes 2.72 0.167 t_4 t_4 cmybp 2.78 0.198 t_5 t_5 phd 3.30 0.154 t_6 t_6 infection 2.37 0.264 t_7 t_7 risk 3.60 0.247 t_8 t_8 mitochondrial 3.09 0.262 t_9 t_9 ma 3.29 0.165 t_10 t_10 research 4.53 0.091 t_11 t_11 cell 3.68 0.059 t_12 t_12 tumor 3.80 0.216 t_13 t_13 dna 4.20 0.176 t_14 t_14 imaging 3.75 0.112 t_15 t_15 cells 3.67 0.357 t_16 t_16 influenza 3.30 0.201 t_17 t_17 intervention 3.12 0.243 t_18 t_18 mast 2.05 0.486 t_19 t_19 treatment 3.65 0.153 t_20 t_20 sleep 3.27 0.377 t_21 t_21 microbiome 2.17 0.388 t_22 t_22 dr 3.73 0.032 t_23 t_23 research 3.20 0.044 t_24 t_24 ipf 3.08 0.240 t_25 t_25 rna 4.43 0.054 t_26 t_26 core 3.93 0.122 t_27 t_27 research 4.05 0.168 t_28 t_28 inflammation 3.01 0.085 t_29 t_29 difficile 3.78 0.049 t_30 t_30 develop 2.30 0.333 top_terms_phi t_1 health, data, women, studies, swan t_2 ptc, brain, metastatic, brafv, cells t_3 diabetes, influenza, numeracy, vaccine, centralized t_4 injury, cmybp, cdk, function, fragment t_5 phd, hif, epithelial, model, project t_6 muscle, sand, fly, infection, strength t_7 risk, factors, sud, early, study t_8 mitochondrial, metabolic, redox, tissue, radiation t_9 ma, activity, aim, cortex, mice t_10 research, program, cancer, students, prevention t_11 cells, cell, specific, lung, brain t_12 cancer, dcis, pancreatic, tumor, genetic t_13 dna, rna, transcription, repair, structure t_14 imaging, clinical, cancer, develop, time t_15 cells, carbon, metabolism, intracellular, cell t_16 response, hiv, env, antibodies, human t_17 intervention, fertility, health, behavior, community t_18 mast, cell, cells, fc, ri t_19 treatment, methods, evaluation, clinical, develop t_20 sleep, plasticity, synaptic, deficits, memory t_21 microbiome, gut, crc, psoriasis, composition t_22 dr, administrative, ucdc, research, te t_23 health, research, hiv, disease, testing t_24 ipf, lung, cns, expression, based t_25 structural, activity, natural, including, nmdar t_26 core, center, projects, data, research t_27 research, core, center, investigators, support t_28 inflammation, hiv, study, battery, capacity t_29 genetic, difficile, extinction, pd, approach t_30 wall, large, stiffening, effects, disease top_terms_gamma t_1 lepi, worker, mepi, biomechanical, mt t_2 reprograms, vegf, sorafenib, chemotherapy, micrometastatic t_3 immunologic, chlamydial, immunized, alaska, curricula t_4 cleavage, cardiomyocytes, stabilizes, occlusion, hyperactivation t_5 xenotransplantation, iv, heparin, pig, hs t_6 parasitic, sarcopenia, vector, west, elderly t_7 kendler, heavy, trajectories, nesarc, neurocognition t_8 couples, ratios, rt, adipocyte, reflected t_9 prefrontal, concerns, madr, impairments, arch t_10 accepted, undergraduate, journals, actively, sponsored t_11 allergen, reversible, ccr, asthma, multiphtoton t_12 taste, glycomic, origin, glycoform, shows t_13 genomes, tefb, elongation, pairing, ac t_14 false, nanoparticles, partial, nanosensors, emission t_15 virulence, shigella, adherence, plaques, cytoplasm t_16 mabs, vlbw, birth, enteric, ab t_17 births, hospitalized, youth, adjunctive, military t_18 truncation, interpreted, tubulin, resulted, attenuated t_19 rules, constructing, challenging, surveillance, accuracy t_20 eeg, impairment, psychiatric, spindle, eszopiclone t_21 psoriatic, baseline, lifestyle, fecal, nas t_22 fo, teprorm, stnar, sc, vnrrps t_23 abm, hsieh, grocery, hopkins, hub t_24 encode, overarching, mrna, srt, mirnas t_25 substituent, plms, nrs, antifungal, lactone t_26 nsls, bnl, computing, instruments, ray t_27 lipidomics, invertebrate, mdibl, ctsa, cobre t_28 recharge, gadgets, myocyte, practically, mybp t_29 seeking, vorinostat, ido, allogeneic, exciting t_30 cyclic, temporally, perivascular, doxycycline, tone
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