Topics in spanish periodicals
This visualization of topics and periodicals is a force directed graph created using the algorithms Fruchterman Reingold and Force Atlas 2 and the modularity method from Blondel et al. (2008) with the analysis and visualization software Gephi 0.9.2.
The goal was to represent a network of topic modeling results (topics) and periodicals with their manually assigned keywords. Therefore, there are two node types: periodical nodes and topic nodes. The periodical nodes are pie charts showing in how many periodical issues a keyword was assigned to. The same keyword-color correlation was used for each periodical. To see the color legend and numerical data for the pie charts, select Pie charts data under the Menu button .
To make the pie charts more readable, only keywords that were assigned a minimum of 100 times in the whole Spectators corpus (which are 22 keywords) were included, due to the otherwise too large amount of colors. The size of a periodical node (pie chart) indicates whether the number of analyzed periodical issues from the topic modeling set is large or small in comparison to other periodicals. Note that numerous issues does not mean the same as a large amount of text, since some issues can be very long and others very short. Also, the number of the analyzed issues is not always the same as the whole amount of issues in a periodical in the Spectators edition, since issues without manually assigned keywords (often, for example, the tables of contents) were left out from the topic modeling analysis.
The size of the topic nodes indicates whether a topic has a high or low possibility in the analyzed set of texts. The same results are shown as heatmaps and bar charts in the section Topics in periodicals under Topic modeling results. The Topic modeling data is provided in the Download section under the Menu button (a ZIP file containing two TXT files), together with this visualization in PNG format and the Gephi data as a GraphML, but the topic keywords are also listed under Topic keywords in the Menu. Nodes with the same color belong to the same community. This means that the densities of the edges within these nodes are higher than from these nodes towards the rest of the network. But, since this is a small network where all topics occur in all periodicals to some degree, the weighted modularity of this network is low and the community structure is not perfectly clear. Nevertheless, it is possible to detect topics that often co occurred in certain the periodicals.
Because this is a directed graph, the edges are round and in clockwise direction from the topic nodes to the periodical nodes. They are higher weighted (thicker) if the likelihood of a topic in a periodical is higher. The weight of a topic in a periodical is documented with the keywords in the Topic modeling data and Topic keywords.
See also:
Topics in periodicalsTopic prevalence over time
Analysis results of other collections
- 0 0,58895 público tratar costumbre idea talento causar educacion asir formar ciencia útil sociedad gustar personar pasar pensar estudiar presentar conocimiento clase
- 1 0,1875 riqueza lujo industriar comerciar rico necesidad aumentar nacion tierra producir arte fruto agricultura pobre utilidad clase poblar comercio pueblo orar
- 2 0,67662 venir salir pasar tomar mano año llegar llamar mil quedar mundo volver entender contar hora amigar oír acabar noche palabra
- 3 0,10684 tribunal periódico real regañon carta madrid presidente imprenta papel autor asir crítico obrar publicar público arbitrio privilegio administracion escribir beneficencia
- 4 0,15658 tierra aguar cuerpo mar sol orar pie fuego argonauta cielo llamar pasar ayre formar luz pequeño señalar cabeza bachiller voz
- 5 0,06758 rey españa duende guerra gobernar europa pueblo francia vasallo nación excelencia año dejar soberano paz ciudad reyno armar mandar españolar
- 6 0,09689 comedia poeta teatro representar pieza autos verso accion drama tragedia poesía calderon arte theatro autor cómico voz reglar pedro entender
- 7 0,54954 ley derecho dudar palabra contrario error causar tratar efecto punto pensar dios oponer natural servir naturaleza precisar comun ningun menester
- 8 0,61136 virtud vida amor corazon honor vivir placer alma amar mirar mundo viciar honrar noble despreciar felicidad desear efecto naturaleza ojo
- 9 0,15673 españa siglo lengua ciencia escribir nacion mundo estudiar historia leer libro arte letra sabio idioma españoles ciencias llamar antiguo naciones
- 10 0,12404 dios gastar andar mil real burro sacar papel cargar pobre menester allá padre animal cabeza asir salir moda zapato pelar
- 11 0,14752 hijo mujer padre madre criar maridar niño familia amor amar edad tratar año marido cuidar matrimoniar señora esposo hermano cariño
- 12 0,06729 causar hombres mirar procurar cuidar desgraciar entendimiento discurrir contrario pluma vivir objetar estimacion vida empeñar pensadora racional mundo cadiz assumpto
- 13 0,11496 santo dios san iglesia religion francisco est sagrado santos milagro página christo capítulo barba santa jesuchristo christianos año templo demonio
- 14 0,18383 obrar autor escribir censor público discurso obra pensador papel publicar discursos carta noticiar escrito censurar pluma leer materia sátira crítico
- 15 0,12867 dejar personar serà tratar pensador gente espiritu jamàs havian assunto duende materia gustar fortuna moda merito quièn genio desear sugetos
- 16 0,09902 cuerpo sentir dios entender causar llamar ciencia enfermedad asir idea médico medicinar infinito sistema incitabilidad alma naturaleza curar imposible filosofía
- 17 0,19166 mujer moda dama damas gustar hermoso adornar hermosura personar gracia sexo figurar señora bayle cortejo señoras mirar cabeza naturaleza bayles