This document discusses using sentiment analysis and machine learning techniques to analyze political texts in Italian. It summarizes work done with the Manifesto Project dataset of Italian political party manifestos. A proof-of-concept web app was developed that predicts the political views of Italian newspaper articles using a bag-of-words model and sentiment index trained on the manifesto texts. Future work is mentioned to continue expanding this analysis of Italian political texts.

1. RES PUBLICARES PUBLICA
SENTIMENT ANALYSIS OF ITALIANSENTIMENT ANALYSIS OF ITALIAN
POLITICAL TEXTSPOLITICAL TEXTS
Roberto Reale

2. reale.me/respublica

3. The future offers very little hope for
those who expect that our new
mechanical slaves will offer us a world
in which we may rest from thinking.
— Norbert Wiener, God & Golem, Inc.,
1964

4. AI AND POLITICSAI AND POLITICS

5. MAJOR TOPICSMAJOR TOPICS
influence
governance
transparency
analytics

6. SENTIMENT ANALYSISSENTIMENT ANALYSIS
The use of computational methods to systematically
identify, extract, quantify, and study affective states
and subjective information.

7. TOOLSTOOLS
natural language
processing
text analysis
computational linguistics
biometrics

8. THE MANIFESTO PROJECTTHE MANIFESTO PROJECT

9. The provides the scientific
community with parties’ policy positions derived from
a content analysis of parties’ electoral manifestos.
It covers over 1000 parties from 1945 until today in
over 50 countries on five continents.
Manifesto Project

10. COLLECTIONSCOLLECTIONS
Countries: Democratic countries, mostly member
countries of the OECD.
Elections: Parliamentary (lower house) elections
since the first democratic election in a country.
Parties: Programs of parties that gained at least one
seat in parliament.
Documents: An authoritative document enacted
and published by a party before an election that
outlines a party’s policy plan for the time a er the
election and covers a broad range of policy issues.

11. TRAINING AND RULESTRAINING AND RULES
The coding (or annotation) is conducted by country
experts.
The country expert coders are mostly political scientist
or political science students and native speakers.

12. STRUCTURE OF THE MAIN DATASETSTRUCTURE OF THE MAIN DATASET
Each row in the dataset represents one electoral
program.
The variables party and date jointly uniquely identify
every row in the dataset.
It covers 4282 manifestos issued at 715 elections in 56
countries.

13. RES PUBLICARES PUBLICA

14. DATA SETSDATA SETS
Italian Parliament
Manifesto texts of
parties

15. BOW VECTORIZATIONBOW VECTORIZATION
Segmentation into semantic units
Tokenization into Bag-of-Words vectors (scikit-
learn)

16. CLASSIFICATION MODELCLASSIFICATION MODEL
p(y = k|x, W) = , = x
e
zk
∑
K
j=1
e
zk
zk w
⊺
k

17. CLASSIFICATION MODELCLASSIFICATION MODEL
L(W, x, γ) = − log + γ||W|
e
zk
∑
K
j=1
e
zk
|
F

18. SENTIMENT INDEXSENTIMENT INDEX
xs
⊺
||s||||x||

19. A PROOF-OF-CONCEPTA PROOF-OF-CONCEPT
A has been developed, as a fork and
evolution of the project.
Predicts political views of texts and newspaper
articles.
Downloads, parses and analyzes political articles from
six major Italian newspapers on the whole political
spectum (Il Fatto Quotidiano, il Giornale, Libero, la
Rebubblica, Il Sole 24 Ore).
web app
fipi

20. A PROOF-OF-CONCEPTA PROOF-OF-CONCEPT
Based on Python (flask, scipy, scikit-learn, pandas and
bs4), Docker and AWS Elasticbeanstalk.
Code available on GitHub

21. TO BE CONTINUEDTO BE CONTINUED