Basic textual analysis of the 5 electoral programs of the main Italian parties for the 2018 Parliament elections

Stampa una copiaStampa una copiaManda via emailManda via emailVersione EPUBVersione EPUB
  • Cirrus cloud 135 words by 5 main electoral programs at 2018 italian parliament election

Summary

The 5 analyzed electoral programs

The used software

https://voyant-tools.org

Documents Length

  1. Movimento 5 Stelle (113.662)
  2. Lega (34.667)
  3. Partito Democratico (26.679)
  4. Liberi e Uguali (5.861)
  5. Foza Italia (1.372)

Vocabulary Density (number of words divided by the total number of words)

  1. Forza Italia (0.51)
  2. Liberi e Uguali (0.35)
  3. Lega (0.23)
  4. Partito Democratico (0.22)
  5. Movimento 5 Stelle (0.12)

Average Words Per Sentence

  1. Lega (48.1)
  2. Movimento 5 Stelle (43.4)
  3. Liberi e Uguali (29.8)
  4. Partito Democratico (29.4)
  5. Forza Italia (15.1)
 

 

 

Distinctive terms (compared to the rest of the corpus)

  • Liberi e Uguali: cittadine (4), deve (25), essere (22), l’approfondimento (3), va (21).
  • Partito Democratico: impegniamo (17), mila (25), legislatura (42), anni (88), euro (34).
  • Forza Italia: domiciliato (3), azzeramento (3), n (7), sottoscritto (2), silvio (2).
  • Lega: buonsenso (37), essere (124), minibot (16), euro (41), fine (36), rivoluzione (76).
  • Movimento 5 Stelle: stelle (140), m5s (59), movimento (150), mtep (47), fine (146), essere (307).

 

 

Most frequent terms in the corpus

Term Frequency
essere 494
sistema 428
lavoro 363
stato 341
anni 306
nazionale 301
parte 300
legge 284
cittadini 265
attraverso 254
paese 243
solo 236
risorse 222
sviluppo 215
servizi 211
modo 203
rispetto 203
attività 202
oggi 200

 

 

Cirrus clouds 95 words

Liberi e Uguali

Partito Democratico

Forza Italia

Lega

Movimento 5 Stelle

 

 

 

Relative frequency distribution of the 10 most relatively frequent terms (with exclusion of "essere" and "deve")

 

 

Bubblelines of the 10 most relatively frequent terms (with exclusion of "essere" and "deve")

 

 

Bubblelines of 8 of the most recurring and significant terms

 

Bubblelines of some high frequency lexical root  linked with political themes

 

 

Co-occurrence network analysis of some high frequency terms among the corpus