TMP LDA Topic Modeling

var ldavis_el1699621986152649444191164175_data = {“mdsDat”: {“x”: [-0.0013367391884918228, -0.0017577318950415752, 0.003094471083533397], “y”: [0.0026517613730187763, -0.002421686043837239, -0.00023007532918153597], “topics”: [1, 2, 3], “cluster”: [1, 1, 1], “Freq”: [43.69567012449632, 30.48336023297555, 25.820969642528134]}, “tinfo”: {“Term”: [“discuss”, “refer”, “time”, “request”, “expect”, “point”, “archiv”, “need”, “write”, “servic”, “book”, “grant”, “archivist”, “late”, “question”, “descript”, “particip”, “attend”, “group”, “address”, “offic”, “statement”, “follow”, “sourc”, “format”, “email”, “ethic”, “practic”, “project”, “social”, “cote”, “syllabusinfo”, “objectivesat”, “endors”, “gh”, “spradlin”, “bibliometr”, “icitexlsx”, “sugimoto”, “scientometr”, “mergeddf”, “nonprogram”, “mannerd”, “authorityxlsx”, “av”, “contd”, “cbc”, “datafram”, “projectsb”, “rasiel”, “column”, “row”, “gadd”, “clientsc”, “authorcount”, “mckinseydu”, “thelwal”, “mpa”, “pmid”, “comstockupd”, “septoct”, 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