A Classifier-Based Approach to Predict the Approval of Legislative
Propositions
Ilo C
´
esar Duarte Cabral and Glauco Vitor Pedrosa
a
Graduate Program in Applied Computing (PPCA), University of Brasilia (UnB), Brasilia, Brazil
Keywords:
Decision Support System, Natural Language Processing, Data Mining, Supervised Classification, Imbalanced
Dataset.
Abstract:
This paper presents a data mining-based approach to predict the approval of Legislative Propositions (LPs)
based on textual documents. We developed a framework using machine learning and natural language
processing algorithms for automatic text classification to predict whether or not a proposition would be
approved in the legislative houses based on previous legislative proposals. The major contribution of this
work is a novel kNN-based classifier less sensitive to imbalanced data and a time-wise factor to weight similar
documents that are distant in time. This temporal factor aims to penalize the approval of LPs with subjects that
are far from current political, social and cultural trends. The results obtained show that the proposed classifier
increased the F1-score by 30% when compared to other traditional classifiers, demonstrating the potential of
the proposed framework to assist political agents in the legislative process.
1 INTRODUCTION
Legislative Propositions (LPs) consists of documents
addressing a subject that will be deliberate in the
legislative houses. It is from the formulation of LPs
that political agents establish the laws, which will
govern society, regularizing social, mercantile, labor
practices, among others. In most of the modern
government systems, such as the Westminster system
and the federal system of the United States, the
core function of the legislative process is to vote on
LPs (Cheng et al., 2017). In this sense, predicting
which LPs are more likely to be approved can
assist public and private entities to direct their
strategic planning. Examples of these entities are:
Companies, Service Providers, Financial Market,
Civil Society Organizations, Federal Government,
State Governments and Municipal Governments.
The big challenge for machine learning algorithms
to predict the approval of LPs is dealing with
imbalanced data. A dataset is imbalanced when
there is a clear disproportion between the number of
examples of one or more classes in relation to the
other classes. For example, from the beginning of
2001 to the end of 2021, only 8.2% of proposals were
approved in the Brazilian legislative houses. Most
a
https://orcid.org/0000-0001-5573-6830
classifiers in the area of machine learning face serious
problems in a context where there is an imbalance,
which can lead to, among other consequences,
classification biases, affecting the efficiency and
reliability of the models. Therefore, learning from
imbalanced datasets is one of the top 10 challenging
problems in data mining research (Yang and Wu,
2006).
In this work, we developed a data-mining based
approach comprising machine learning and natural
language processing algorithms in order to predict
the chance of approval of LPs using its textual
content. The developed framework consists of: (i)
representing LPs documents in sparse vectors using
Inverse Document Frequency (TF-IDF); (ii) applying
a dimensionality reduction technique using Singular
Value Decomposition (SVD); (iii) introducing a
cost sensitive factor of misclassification in the
classification algorithm to mitigate the imbalance of
the database; (iv) introducing a time factor in the
classification to penalize proposals with subjects
outside current trends.
The main contribution of our framework is a
classifier-based approach to dealing with imbalanced
data and a time-wise analysis. The proposed classifier
is a kNN-based approach comprising two weighted-
based factors: time and dataset imbalance. The time
factor aims to generate predictions based on the
436
Cabral, I. and Pedrosa, G.
A Classifier-Based Approach to Predict the Approval of Legislative Propositions.
DOI: 10.5220/0011728800003467
In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 1, pages 436-442
ISBN: 978-989-758-648-4; ISSN: 2184-4992
Copyright
c
 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

composition of the current chamber in order to reflect
the trends (political, economic, financial and cultural)
of the current time. For example, it is expected that
LPs addressing similar issues with other proposals
already approved in a short period of time will also
be approved. In this sense, when comparing the
similarity of propositions, the time frame between
the LPs is an important issue that must be considered
by the classifier. Besides that, kNN faces difficulty
in imbalanced datasets as it treats all neighbors of
the query instance equally and most of the neighbors
will be of the majority class. To deal with this issue,
we use a distance-based approach to provide more
importance to neighbors of the minority class with a
higher proximity weighted confidence.
The proposed framework was validated using the
public database of LPs available by the Brazilian
Chamber of Deputies. In our experiments, we
used LPs related to laws and amendments and
with the status completed between 2001 and 2021.
Experimental tests were performed to compare the
proposed classifier with different modifications in
the kNN algorithm and with other machine learning
classifiers.
This paper is structured as follows: Section 2
presents the context of the work and related works;
Section 3 formalizes the classification problem;
Section 4 presents the proposed approach; Section
5 shows the results of the experimental tests carried
out to evaluated the technique proposed and, finally,
Section 6 presents the conclusions of this work.
2 CONTEXT AND RELATED
WORKS
The legislative process comprises the elaboration,
analysis and voting of various types of proposals,
such as: Ordinary Laws, Provisional Measures,
Amendments to the Constitution, Legislative
Decrees, Resolutions, among others. It is from
the formulation of these propositions that political
agents establish the laws that will govern society and
regulate social, mercantile, labor practices, among
others.
The work of (Nay, 2017) presented a model
to predict whether or not a proposition would be
approved in the US Congress. They used a database
with the proposed laws from 1993 to 2015. From
this database, they extracted 12 characteristics and
performed data analysis. A model was trained using
word2vec (Le and Mikolov, 2014) and tree-based
models as well as ensemble stacking techniques.
The technique developed had a 96% success rate in
predicting the approval of a legislative project.
The work of (Cheng et al., 2017) also focused
on the US Congress. They presented a technique to
analyze legislators’ profile data and also the textual
data of the propositions. To carry out the text analysis
of the projects, the authors used Bag-of-Words
model, and also the way in which the ideological
profile data of legislators were used in a Euclidean
spatial model called policy location.
Historically, the rate of approval of laws in Brazil
is less than 0.9% of the total of propositions presented
in the legislative houses. In the US Congress, this
rate is approximately 4%. In this scenario of so many
disapprovals of propositions, it is necessary to know
in advance which projects are worth paying attention
to, that is, what is the probability of each project
being approved. Therefore, proposing mechanisms
based on natural language processing and machine
learning is one of the ways to develop computational
mechanisms capable of helping the entities that
follow the legislative work to direct their strategic
planning.
In the last decades, the automatic categorization
of textual documents has become an area with wide
application in the treatment of a large amount of
text data and in the literature there is a considerable
number of works related to this topic (Sebastiani,
2002). Many statistical classification methods and
machine learning techniques were used, such as the
kNN (k-Nearest Neighbor) (Tan, 2006) classifier,
Naive Bayes algorithms, decision trees, generative
probabilistic classifiers, multivariate regression
models, among others.
However, most classifiers face serious problems
in a context where there is an imbalance in the
distribution of classes. Currently, there are many
approaches in the literature to mitigate classification
with imbalanced datasets (Wang et al., 2021), ranging
from more basic techniques of subsampling and
oversampling the dataset to computationally more
sophisticated methods that combine neural networks
with ensemble models and achieve good results
(Li and Zhang, 2021). Algorithmic level-based
classification methods and sensitive cost functions
are also widely used to deal with problems related to
imbalanced datasets (Barot and Jethva, 2021).
3 PROBLEM FORMULATION
There are different machine learning based algorithms
for classification tasks. According to (Wu et al.,
2007), one of the top-10 data mining classification
algorithms is the kNN (k-Nearest-Neighbors), which
A Classifier-Based Approach to Predict the Approval of Legislative Propositions
437

is also considered by some works as the most popular
algorithm for classifying textual data and kNN has
shown superior performance for textual classification
when compared to other classifiers (Imandoust et al.,
2013) (Trstenjak et al., 2014) (Jiang et al., 2012).
However, kNN is a sample-based learning method,
which uses all documents in the database to predict
the labels (classes) of new documents. Classification
using kNN is done assuming that similar documents
will belong to the same category.
In the case of this work, there is a binary
classification (two classes): a document that
represents the text of a LP can be classify as
“approved” or “disapproved”. Formally, considering
a set of documents D = {d
1
, d
2
, d
3
, ...., d
m
}, each
d
j
∈ D is associated with a label/class using the
following function:
δ(d
j
) =
(
1, if d
j
is approved
0, otherwise.
(1)
To classify a new document d
q
, the kNN algorithm
searches for the k -documents in D most similar to d
q
using a similarity distance. For example, to measure
the similarity between two documents d
j
and d
q
, we
can use the similarity by cosines, which is defined by:
sim(d
j
, d
q
) =
d
i
· d
q
||d
i
|| ·||d
q
||
(2)
The advantage of using similarity by cosines over
other distance functions is based on the fact that when
working with Natural Language Processing - and
even more when making a dimensionality reduction
- the angles between the vectors are better preserved
than their distances (Rahutomo et al., 2012).
Considering D
k
⊆ D the set of k-documents most
similar to the document d
q
, the classic version of kNN
algorithm attributes the class of the new document as
the majority class of its k- neighbors, that is:
kNN
traditional
(D
k
, d
q
) = arg max
c∈{0,1}
k
∑
j=1
I(c, δ(d
j
)) (3)
where:
I(a, b) =
(
1, if a = b
0, otherwise.
(4)
In the traditional version of the kNN algorithm (Eq.
3), the class of each neighbor has the same weight for
the classifier. However, we can give more weight to
the nearest neighbors and less weight to the distant
ones. In other words, the distance-based weighted
version of kNN can be defined as:
kNN
dist
(D
k
, d
q
) = arg max
c∈{0,1}
k
∑
j=1
sim(d
q
, d
j
) · I(c,δ(d
j
))
(5)
However, both kNN-traditional (Eq. 3) and kNN-
dist (Eq. 5) face the problem of imbalance database.
This means that, in both approaches, the majority
class will contribute to more neighbors which will
tend to define the class of the new document. Figure
1 shows this problem: we can note that among the
k-neighbors closest to the yellow circle there are
more blue squares than red triangles, so the yellow
circle will be classified as belonging to the class of
blue squares, even when its k-neighbors are all the
red triangles in the database.
Figure 1: Example of classification in imbalanced datasets:
the majority class (blue squares) contributes with more
neighbors than the minority class (red triangles).
4 PROPOSED APPROACH
The proposed approach aims to represent and classify
LPs based on its textual documents. For this purpose,
we developed a framework defined in three steps
carried out as follows:
1. representing textual documents in sparse vectors
based on the TF-IDF technique;
2. reducing the vectors dimensionality using the
SVD (Singular Value Decomposition) approach;
3. incorporating a distance-weighted factor to
the kNN algorithm in order to mitigate the
database imbalance and a temporal-based factor
to maximize the similarity of documents close in
time.
Each of these steps will be detailed as follows.
4.1 Textual Document Representation
To represent the textual documents of LPs, the
proposed approach uses the TF-IDF model (Term
Frequency-Inverse Document Frequency), which
measures the importance of a word for a document
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based on a collection (or corpus). The TF-IDF model
is composed of two calculations: the first computes
the Term Normalized Frequency (TF) and the second
computes the Inverse Document Frequency (IDF).
Consider D the document set (corpus) with a
vocabulary of words of size n. Let d
j
∈ D such that
d
j
= {x
1
, x
2
, x
3
, ..., x
n
}, where x
i
denotes the number
of occurrences of the i-th term (word) in d
j
. Formally,
the TF calculation of the ith word is defined as:
T F(i) =
x
i
∑
n
k=0
x
k
(6)
The IDF is incorporated to decrease the weight of
words that occur more frequently in D and increase
the weight of those that occur rarely. Formally, the
IDF calculation of the ith word is defined as:
IDF(i) = log

|D|
t
i
+ 1

(7)
where |D| is the number of documents in the corpus
and t
i
denotes the number of documents in D that
contain the ith word.
The final representation of each document d ∈ D
is given by the product TF and IDF of each of the n
words in the vocabulary, that is:
T F IDF(i) = T F(i) × IDF(i) (8)
for i = 1, 2, ..., n.
4.2 Dimensionality Reduction
The document representation using the TF-IDF
model will generate sparse n-dimensional vectors,
where n is the number of words (vocabulary) in
the corpus. This means that, for each document,
the generated vector is of high dimensionality and,
because of this, the efficiency of machine learning
algorithms may be affected.
To reduce dimensionality, we use the Singular
Value Decomposition (SVD) technique (Karl et al.,
2015) to decompose the document matrix D
(m×n)
in
the following matrix:
D = USV
T
(9)
where U
(m×m)
and V
(n×n)
are two orthogonal matrices
and S
(m×n)
is a diagonal matrix, and m is the number
of documents in the corpus and n is the number of
words.
Using the decomposition of the matrix D by Eq.
9, it is possible to reconstruct the matrix D in a p-
dimensional space (where p << n) considering the
sub-matrix (m × p) formed by the first p columns and
the m rows of the respective original matrices U, S
and V.
However, it is not trivial to choose the size of
the dimension p that best represents the original
data without losing the discriminative power of
the vector. Therefore, in this work, this choice
was made using brute force, where we analyzed
different dimensionality values and we choose the
dimensionality value with the best F1 score. This
procedure will be discuss in Section 5.
4.3 Time and Imbalace Factors
After representing the LP documents in compact
vectors, the next step is to use a classifier to predict
the approval (or disapproval) of these documents. For
this purpose, we developed two modifications in the
kNN-dist algorithm (Eq. 5) to:
1. weight those documents that are closer in time;
2. handle database imbalance.
In order to weight the documents closer in time,
a time-based factor was defined to measure how far
two documents are in time. That is, how far two LPs
were presented in the legislative houses for voting. In
other words, the time factor between two documents
d
i
and d
q
is given by the difference of the years of its
proposals, which is formally defined as:
f actor
time
(d
j
, d
q
) = log

TimeFrame
|Year(d
j
) −Year(d
q
)| + 1

(10)
where, Year(d
i
) refers to the year that the document
d
i
was proposed and TimeFrame refers to the time
lapse calculated by the difference between the year of
the oldest document and the newest document in the
database. The smaller the value of the time-factor, the
further apart (in time) two documents will be.
The proposal to deal with the imbalance consists
of increasing the distance of k-neighbors belonging
to the majority class, which in the case of this work
refers to the class of disapproved propositions. This
imbalance factor is a constant “α” that is multiplied
by the similarity distance of documents d
j
∈ D that
belong to the majority class:
f actor
imbalance
(d
j
) =
(
1, if δ(d
j
) = 1
α δ(d
j
) = 0
(11)
Formally, by integrating the time factor (Eq. 10)
and the imbalance factor (Eq. 11) in the kNN
dist
, we
have the following proposal:
kNN
proposed
(D
k
, d
q
) = arg max
c∈{0,1}
k
∑
j=1
sim(d
q
, d
j
)·
I(c, δ(d
j
)) · f actor
imbalace
(d
j
) · f actor
time
(d
j
, d
q
)
(12)
A Classifier-Based Approach to Predict the Approval of Legislative Propositions
439

5 EXPERIMENTAL RESULTS
In this section, we present the experimental tests
conducted to evaluated the proposed technique in the
classification of LPs. First, we discuss the dataset we
used to perform the experiments, next we present the
performance metrics used to compare the proposed
technique with other classifiers and, the obtained
results.
5.1 Database
To perform the experiments, we used the open
data website of the Chamber of Deputies
1
which
provides annual data files with information on
each LP presented in the respective year. In this
work, we downloaded the files referring to the years
between 2001 and 2021 and we considered only the
propositions of laws amendments types and with
completed process, that is, with situation approved
or disapproved. The database resulted in 28,049
documents, where 25,753 are LPs disapproved and
2,296 are LPs approved. It means that, from 2001 to
2021 there was an approval rate of only 8.2%.
After defining the database, we pre-processed
the text files in order to clean and standardize
them for the modeling stage. This “cleaning” and
standardization in the texts is an approach carried
out in natural language processing to guarantee
the quality in the processing of textual documents
and it contributes, among other things, to reduce
the generated dictionary, as some words will be
suppressed and/or encoded in the same pattern.
Figure 2 shows the sequence of the four steps used in
this work for the pre-processing of the LPs document
texts.
5.2 Performance Evaluation Metrics
In the context of imbalanced data, it is important
to consider performance measurements that provide
insight about the imbalance of the database.
According to (Brzezinski et al., 2020), the measure
used to evaluate classifiers in an imbalanced
environment must be selected individually, as each of
these problems comes with its own set of challenges,
that is, classical metrics are not a reliable means of
evaluating a model trained on imbalanced data.
Most of the classic evaluation measures derive
from a table called the Confusion Matrix, which
contains the amount of correct classifications versus
the predicted classifications for each class over a
1
https://dadosabertos.camara.leg.br/swagger/api.html
set of examples, that is, it indicates the errors and
successes of the model comparing with the expected
results. For each class, four values can be extracted:
• TP: true positive is an outcome where the model
correctly predicts the positive class;
• TN: true negative is an outcome where the model
correctly predicts the negative class;
• FP: false positive is an outcome where the model
incorrectly predicts the positive class;
• FN: false negative is an outcome where the model
incorrectly predicts the negative class.
Based on these four variables, four evaluation metrics
can be defined:
• Precision =
T P
T P+FP
• Recall =
T P
T P+FN
• F1-Score =
2×Accuracy×Recall
Accuracy+Recall
• Accuracy =
T P+T N
T P+T N+FP+FN
Among these four metrics, the most important for
this work is the F1-Score, which is a harmonic mean
between Precision and Recall, which is much closer
to the smallest values than a simple arithmetic mean.
That is, when we have a low F1-Score, it is an
indication that either the accuracy or the recall is low.
5.3 Techniques Evaluated and
Parameter Settings
To perform comparative results, four classification
techniques were evaluated:
• kNN
proposed
(Eq. 12)
• kNN
traditional
(Eq. 3)
• kNN
dist
(Eq. 5)
• XGBoost
The implementation of the kNN
traditional
and
kNN
dist
classifiers need the definition of parameter K.
In our experiments, this value was defined using brute
force: we performed exhaustive test using different
values of K within the interval 1 and 150 and, for
each value, the F1-Score was calculated. Then, the
value of K choose for each technique was that one
with best value of F1-Score. Figure 3 shows these
results.
The kNN
proposed
needs two setup parameters: the
value of K and the value of the imbalance factor
(Eq. 11). To choose the best values for these two
parameters, we also performed tests varying the value
of the imbalance factor and the value of K. Figure 4
shows a heat map of the results obtained by varying
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Figure 2: Pre-processing steps performed on the texts of the LPs.
Figure 3: F1-score values obtained for each K in the
best result scenarios for the kNN
traditional
and kNN
dist
techniques.
these two parameters, where warm colors indicate
high values for the F1-Score. It can be seen from
Figure 4 that the best values are obtained for low
values of the imbalance factor and for high values of
K.
Figure 4: Heat map of the values obtained by varying
the two parameters necessary to execute the kNN
proposed
:
warmer colors indicate higher F1-Score values.
The best value for the dimensionality reduction
of the TF-IDF vector was also chosen by brute force:
we started with a dimensionality equal to 100 (with
increments of 100) and for each dimensionality value
we computed the F1-Score. The results obtained can
be seen in Figure 5. For performance reasons, in
cases where there was a tie in the highest F1-Score,
the dimensionality chosen was the lowest.
Table 1 summarizes the parameters used in each
classifier that generated the best F1-Score values.
Figure 5: F1-Score values obtained by reducing the
dimensionality of the original TF-IDF vector using the SVD
technique.
5.4 Obtained Results
Table 2 shows the values obtained for each of
the classifiers evaluated. All techniques had high
accuracy values. However, this performance is not
enough, as all the classifiers had low Recall values
because of the imbalance in the database
The best Revocation result was obtained by
kNN
proposed
, which correctly classified most docu-
ments of the minority class. Due to this, kNN
proposed
also had the best F1-Score among the evaluated
classifier, being 30% higher than the others. This
shows that, in fact, the proposals presented in this
work allowed increasing the discriminative power of
the classifier when predicting the approval of PLs.
6 CONCLUSIONS
This work presented an approach that uses Natural
Language Processing and Machine Learning
algorithms to predict the approval of Legislative
Propositions based on its textual documents. The
main challenge of the proposed approach was to deal
with imbalanced datasets, as there are more proposals
that are disapproved than approved. Most machine
learning techniques will ignore the minority class
and consequently will perform poorly in that class.
A Classifier-Based Approach to Predict the Approval of Legislative Propositions
441

Table 1: Parameters that generated the best F1-Score for each evaluated classifier.
kNN
proposed
kNN
traditional
kNN
dist
XGBoost
Vector Dimensionality 700 800 1200 800
K 37 2 4 -
Imbalace Factor 0.29 - - -
Table 2: Results obtained for each of the techniques evaluated.
kNN
proposed
kNN
traditional
kNN
dist
XGBoost
F1-Score 0.55 ? 0.44 0.44 0.44
Accuracy 0.93 0.95 0.93 0.93
Precision 0.57 0.75 0.66 0.75
Recall 0.52 0.31 0.33 0.31
However, in general, the performance in the minority
class is the most important to consider, especially for
the application scenario of this work.
The approach proposed in this work considered
two important aspects when predicting the approval
of LPs: (i) the database imbalance factor and (ii) the
time in which proposals were submitted. These two
aspects were mitigated by modifying the traditional
kNN algorithm: we increased the distance between
disapproved documents and those that were further
away in time. Experimental results showed that the
two modifications proposed in the kNN algorithm
allowed to increase the classifier’s performance. The
proposed technique obtained a high recall rate among
the evaluated techniques, showing its potential in
predicting propositions with high chances of approval
in legislative houses, contributing with a valuable
tool to be used in the legislative process.
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