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https://github.com/quantum-software-development/fii-investor-intelligence-platform

Big Data and NLP platform for Real Estate Investment Fund (FII) intelligence, sentiment analysis, and distributed analytics.
https://github.com/quantum-software-development/fii-investor-intelligence-platform

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Big Data and NLP platform for Real Estate Investment Fund (FII) intelligence, sentiment analysis, and distributed analytics.

Awesome Lists containing this project

README 

          



**\[[πŸ‡§πŸ‡· PortuguΓͺs](README.pt_BR.md)\] \[**[πŸ‡¬πŸ‡§ English](README.md)**\]**








# 
 [Investor Intelligence Platform  πŸ‡§πŸ‡·  Brazilian FIIs]() 



### 
 Real Estate Investment Funds (FIIs) - Market Intelligence & Behavioral Analytics







$$\Huge {\textbf{\color{green} CRISP-DM} \space \textbf{\color{white} β€’} \space \textbf{\color{yellow}  Data Lakehouse} \space \textbf{\color{white} β€’} \space \textbf{\color{green} NLP} \space \textbf{\color{white} β€’} \space \textbf{\color{yellow} Responsible AI} \space \textbf{\color{white} β€’} \space \textbf{\color{green} Regulatory Alignment}}$$







### 
 ***An institutional-grade intelligence platform for monitoring, structuring, ranking, and interpreting Brazilian Real Estate Investment Fund (FII) signals from financial media, research portals, and investor communities.***







#### 
 [Big Data]() β€’ [PySpark]() β€’ [MapReduce Word Count]() β€’ [NLP]() β€’ [TF-IDF]() β€’ [BM25 Ranking]() β€’ [Hybrid Retrieval]() β€’ [FAISS + Multilingual Embeddings]() β€’ [Web Scraping]() β€’ [TOFU/MOFU/BOFU]() β€’ [CRISP-DM]() β€’ [FastAPI]() β€’ [Streamlit]() β€’ [Docker]() β€’ [Responsible AI]() β€’ [LGPD]() β€’ [EU AI Act Alignment]()








### 
 [![Sponsor Quantum Software Development](https://img.shields.io/badge/Sponsor-Quantum%20Software%20Development-brightgreen?logo=GitHub)](https://github.com/sponsors/Quantum-Software-Development)










   



 








## πŸŽ“ Academic 







[**Institution:**]() Pontifical Catholic University of SΓ£o Paulo (PUC-SP)  


[**School**:]() FACEI β€” Faculty of Exact Sciences and Informatics 


[**Bachelor’s Program:**]() Human-Centerd AI & Data Science β€’ 5th Semester β€’ 2026   


[**Course:**]() AI Security, Cybersecurity & Social Engineering   


[**Methodology:**]()  CRISP-DM (Cross-Industry Standard Process for Data Mining)  


**Professors** [✨ Carlos Eduardo Paes](https://www.linkedin.com/in/carlos-eduardo-de-barros-paes-ph-d-7b137a4/)  and  [✨ Eduardo Savino Gomes]() 


**Project Author:** [Fabiana ⚑️ Campanari](https://linktr.ee/fabianacampanari) 








#










  

    FIIs Marketing Intelligence Dashboard

  







  

    React Interactive FIIs Analytics Slides

  

  



  

    FIIs Strategic Presentation PPTX

  








  

    FIIs Market Analysis Executive Report

  









#








> [!TIP]

> 

> β†— **[Explore the Full Course Repository](https://github.com/Quantum-Software-Development/1-Cybersecurity-SocialEngineering_Hub))**

>

> ###  Real Estate Investment Funds (FIIs) πŸ‡§πŸ‡· Market Intelligence & Behavioral Analytics 



> 

> A scalable platform combining [**Big Data**](), [**PySpark**](), [**MapReduce**](), [**Word Count**](), [**NLP**](), [**TF-IDF**](), [**BM25**](), [**FAISS**](), [p**Multilingual Embeddings**](), [**Web Scraping**](), [**Hybrid RAG**]() and [**AI-Assisted Analytics**]() to transform large-scale financial discussions into actionable insights for FIIs.

>

> 


>

>  #

>

> 



>

> $$\Huge {\textbf{\color{green} Where market discussions become investment narratives…}}$$

>

> $$\Huge {\textbf{\color{yellow} because markets talk a lot...}}$$

> 

> $$\Huge {\textbf{\color{green} intelligent systems just listen better}}$$

>

> ### 
 ⚑








#








> [!WARNING]

>

> 


> ⚠️ Projects may be publicly shared when permitted.  

> The focus is on applied, hands-on learning with real datasets in AI governance and security contexts.  

> All sensitive content remains protected in private repositories when required.  

 

> 

> ⚠️ Disclaimer

> Plataforma exclusivamente educacional e analítica. Não constitui recomendação de investimento.










## Table of Contents



1. [Academic Context](#-academic-context)

2. [Overview](#overview)

3. [System Overview](#system-overview)

4. [What This Platform Delivers](#-what-this-platform-delivers)

5. [Why This Matters](#-why-this-matters)

6. [Architecture and Pipeline](#-architecture-and-pipeline)

7. [Notebooks Executive Summary](#-notebooks-executive-summary)

8. [Notebooks NB00–NB07: Technical Report](#-notebooks-nb00nb07-technical-report)

9. [The 3 Core Techniques + FAISS Semantic Layer](#-the-3-core-techniques--faiss-semantic-layer-mapreduce--tf-idf--bm25--embeddings)

10. [Data Sources β€” 21 Sources](#-data-sources--21-monitored-sources)

11. [Big Data Infrastructure](#-big-data-infrastructure)

12. [Dependencies and requirements.txt](#-dependencies-and-requirementstxt)

13. [CRISP-DM Methodology](#-crisp-dm-methodology)

14. [Marketing Funnel: TOFU, MOFU and BOFU in the Project](#-marketing-funnel-tofu-mofu-and-bofu-in-the-project)

15. [RAG Chatbot: Groq + Gemini (Automatic Fallback)](#-rag-chatbot-groq--gemini-automatic-fallback)

16. [Governance](#-governance)

17. [Folder Structure](#-folder-structure)

18. [How to Run](#-how-to-run)

19. [Deployment & Automation Workflow](#deployment--automation-workflow)

20. [Which requirements*.txt to Use in Each Scenario](#-which-requirementstxt-to-use-in-each-scenario)

21. [Makefile β€” Full Reference](#-makefile--full-reference)

22. [Expected Outputs](#-expected-outputs)

23. [Technical Glossary](#-technical-glossary)

24. [Repository and Project Links](#-repository-and-project-links)

25. [References](#-references)








## [πŸŽ“ Academic and Institutional Context]()







This project was developed at PUC-SP in the courses of cybersecurity, social engineering, data engineering and Big Data analytics applied to financial markets. The original requirement focused on demonstrating a distributed word count solution using PySpark and the MapReduce paradigm.



From this starting point, the repository was extended to incorporate more advanced analytical techniques (TF-IDF, BM25, contextual sentiment), a serving architecture with FastAPI + RAG and a structured pipeline oriented towards FII marketing intelligence.








## [Academic Requirements Met]()







| [Requirement]() | [Implementation]() |

| :-- | :-- |

| [Distributed Computing]() | PySpark Β· RDD MapReduce Β· SparkSession |

| [Big Data Architecture]() | Medallion (Bronze β†’ Silver β†’ Gold) |

| [Machine Learning]() | TF-IDF Β· BM25 Β· Semantic Embeddings Β· Sentiment Analysis |

| [Vector Search]() | FAISS Β· Dense Index Β· Multilingual PT-BR Embeddings |

| [NLP]() | PT-BR Tokenization Β· FII Lexicon Β· Signal Flags |

| [Data Governance]() | LGPD Β· EU AI Act Β· Responsible AI Β· XAI |

| [REST API]() | FastAPI Β· Uvicorn |

| [RAG / LLM]() | Groq (openai/gpt-oss-20b, primary) + Gemini 2.5 Flash (automatic fallback) |

| [Visualization]() | Streamlit Β· Plotly |

| [Cybersecurity]() | Narrative surface analysis Β· Social Engineering awareness |








## [Product Overview and Definition]()







The [**Investor Intelligence Platform πŸ‡§πŸ‡· FIIs Brazil**]() is not just an academic Big Data exercise. It is an investor intelligence platform for Brazilian Real Estate Investment Funds (FIIs), designed to transform fragmented public financial discussions into structured, searchable, explainable and decision-oriented market intelligence.



Instead of being a simple dashboard, the system operates as an end-to-end analytical environment that:



[-]() collects data from 21 sources (RSS Β· scraping Β· Reddit)


[-]()organizes them in a Bronze/Silver/Gold architecture 


[-]() enriches them with hybrid retrieval (TF-IDF + BM25 + FAISS semantic search with multilingual PT-BR embeddings), FII PT-BR sentiment and explainable marketing intelligence signals   


[-]() exposes results via [**FastAPI + RAG + Groq chatbot + Streamlit**]()







```mermaid

%%{init:{

'theme':'dark',

'themeVariables':{

'background':'#090d13',

'primaryTextColor':'#F5F7FA',

'lineColor':'#2dd4bf'

}}}%%



graph LR



SRC["21 SOURCES
RSS β€’ Scraping β€’ Social"]:::setup



PIPE["NLP PIPELINE
PySpark β€’ MapReduce
TF-IDF β€’ BM25 β€’ FAISS"]:::gold



GOLD["GOLD LAYER
Parquet artifacts"]:::bronze



API["FASTAPI
REST + RAG"]:::dash



DASH["STREAMLIT
Analytics Dashboard"]:::dash



LLM["LLM LAYER
Groq + Gemini"]:::llm



SRC --> PIPE --> GOLD

GOLD --> API

GOLD --> DASH

API --> LLM

DASH --> LLM



classDef setup fill:#0d2137,stroke:#00d2ff,color:#F5F7FA,stroke-width:2.5px;

classDef bronze fill:#2a1512,stroke:#a85a4a,color:#F5F7FA,stroke-width:2.5px;

classDef silver fill:#1b2430,stroke:#b0b7c3,color:#F5F7FA,stroke-width:2.5px;

classDef gold fill:#2a2208,stroke:#e6c35a,color:#F5F7FA,stroke-width:2.5px;

classDef dash fill:#06363d,stroke:#2dd4bf,color:#F5F7FA,stroke-width:2.5px;

classDef llm fill:#231433,stroke:#b56cff,color:#F5F7FA,stroke-width:2.5px;

```








## [🎯 Objectives]()







1. [**Complete end-to-end pipeline**]() β€” from ingestion to analytical output. 


2. [**Distributed processing + NLP**]() β€” PySpark MapReduce combined with TF-IDF, BM25 and contextual sentiment. 


3. [**RAG over FII corpus**]() β€” hybrid retrieval via TF-IDF, BM25 and FAISS-backed multilingual PT-BR embeddings, followed by contextual generation via Groq. 


4. [**Cybersecurity and Social Engineering**]() β€” security perspective in interpreting channels and narratives.








## [πŸ‘₯ Target Audience]()







[-]() Asset and fund managers who monitor investor perception 


[-]() Financial analysts who track market narratives 


[-]() Marketing teams interested in FII visibility and engagement 


[-]() Academic evaluators assessing Big Data, Spark, NLP and RAG 


[-]() Recruiters and technical portfolio reviewers








## [ Why This Matters ❓]()







Analysts, managers and financial communication teams face:



[-]() information dispersed across dozens of portals and communities 


[-]() high noise-to-signal ratio in market discussions 


[-]() difficulty tracking how sentiment and narratives evolvev 


[-]()  lack of transparent tools aligned with LGPD and the EU AI Act







> [!TIP]

> This platform addresses this gap with 21 monitored sources, a Bronze/Silver/Gold pipeline and reproducible, interpretable analytics.








## [ Source Coverage]()



The platform monitors a curated set of editorial and behavioral sources relevant to the Brazilian FII ecosystem. Instead of treating all inputs as an undifferentiated corpus, the project distinguishes:







[-]() [**Editorial RSS sources**]() β€” collected via structured feeds >


[-]() [**Editorial portals via scraping**]() β€” controlled extraction of public metadata >


[-]() [**Behavioral social sources**]() β€” Reddit as a community sentiment layer







> [!IMPORTANT]

> Detailed documentation per source: [`docs/data_sources.md`](https://github.com/Quantum-Software-Development/5-cybersecurity-social-engineering-fii-marketing-intelligence-platform/blob/2b697bb54a78f4d31424ecd334466f9fc4a8d6e0/docs/data_sources.md)








## [Collection Strategy by Source Type]()







### [***RSS-First***]()



When available, [**RSS**]() is preferred: lower extraction cost, native structured metadata, no risk of breakage due to HTML layout changes, reliable scheduling.







### [***Scraping as Controlled Fallback***]()



When RSS is unavailable or unstable, controlled HTML extraction of public pages. It does not simulate human navigation β€” it collects observable metadata (titles, links, timestamps, categories, excerpts).



### [***Collection of Social Sources***]()



Reddit follows a separate logical path because it represents conversational and community-based data.

It is treated as a behavioral and discursive input layer that complements editorial coverage with public sentiment and emerging narratives.



### [***3-level strategy:***](docs/data_collection.md)







| [Level]() | [Method]() | [Requires]() |

|---|---|---|

| [1]() | PRAW (Python Reddit API Wrapper) | `REDDIT_CLIENT_ID` + `REDDIT_CLIENT_SECRET` |

| [2]() | Public API `/new.json` + `/hot.json` | None |

| [3]() | Committed frozen Parquet | None |








###  [ Official Data Sources β€” 21 Monitored Sources]()







| #  | [Source]()                                    | [Category]()  | [Primary Method]() | [Fallback]() | [Endpoint]()                        |

| -- | --------------------------------------------- | ------------- | ------------------ | ------------ | ----------------------------------- |

| 1  | [InfoMoney]()                                 | Editorial     | RSS                | β€”            | infomoney.com.br/feed/              |

| 2  | [Empiricus]()                                 | Editorial     | RSS                | Scraping     | empiricus.com.br/feed/              |

| 3  | [Money Times]()                               | Editorial     | RSS                | β€”            | moneytimes.com.br/feed/             |

| 4  | [Seu Dinheiro]()                              | Editorial     | RSS                | β€”            | seudinheiro.com/feed/               |

| 5  | [Exame Invest]()                              | Editorial     | RSS                | β€”            | exame.com/feed/                     |

| 6  | [CNN Brasil Business ]()                      | Editorial     | RSS                | β€”            | cnnbrasil.com.br/feed/              |

| 7  | [Suno Research]()                             | Editorial     | RSS (Secondary)    | β€”            | sunoresearch.com.br/feed/           |

| 8  | [E-Investidor]()                              | Editorial     | RSS (Secondary)    | β€”            | einvestidor.estadao.com.br/feed     |

| 9  | [NeoFeed]()                                   | Editorial     | RSS (Secondary)    | β€”            | neofeed.com.br/feed/                |

| 10 | [Toro Investimentos]()                        | Editorial     | RSS                | Scraping     | blog.toroinvestimentos.com.br/feed/ |

| 11 | [Funds Explorer]()                            | Portal        | Scraping           | β€”            | fundsexplorer.com.br                |

| 12 | [Status Invest]()                             | Portal        | Scraping           | β€”            | statusinvest.com.br                 |

| 13 | [Clube FII]()                                 | Portal        | Scraping           | β€”            | clubefii.com.br                     |

| 14 | [FIIs.com.br]()                               | Portal        | Scraping           | β€”            | fiis.com.br                         |

| 15 | [Portal do FII]()                             | Portal        | Scraping           | RSS          | portaldofii.com.br                  |

| 16 | [Investidor10]()                              | Portal        | Scraping           | β€”            | investidor10.com.br                 |

| 17 | [Eu Quero Investir]()                         | Portal        | Scraping           | β€”            | euqueroinvestir.com                 |

| 18 | [Bora Investir (B3)]()                        | Institutional | Scraping           | β€”            | borainvestir.b3.com.br              |

| 19 | [XP ConteΓΊdos]()                              | Institutional | Scraping           | β€”            | conteudos.xpi.com.br                |

| 20 | [Investing Brasil]()                          | Portal        | Scraping           | β€”            | br.investing.com                    |

| 21| [**Reddit / Google News (Fallback)**]() | [**Social / Behavioral**]() | [**PRAW (when available) + Google News RSS (fallback)**]() | `r/investimentos` Β· `r/farialimabets` Β· news.google.com |







> [!TIP]

> The original behavioral source uses Reddit subreddits (`r/investimentos` and `r/farialimabets`) as a [**social intelligence and market narrative layer**]().  

> Following changes to Reddit’s public API policy in April 2023 (HTTP 403 restrictions), the pipeline was redesigned to operate across three levels:







### [***Source 21 β€” Reddit / Google News (Fallback)***]()







1. [**Level 1 β€” PRAW**]() (when `REDDIT_API_AVAILABLE = True`) 

 

   Uses the authenticated Reddit API to collect recent posts from the target subreddits.



   



2. [**Level 2 β€” Google News RSS PT-BR (fallback)*]()

   

   - When Level 1 is unavailable (e.g., missing `REDDIT_CLIENT_ID` in `.env` or public API restrictions), NB01 triggers `collect_google_news_rss()`, which:

   - queries Google News in Portuguese using FII-specific search terms,

   - filters content using FII-related keywords (`FII_FILTER_TERMS`),

   - stores articles with `source='news.google.com'`, `source_type='reddit'`, `tags='google_news_rss'`, and `ingestion_method='feedparser_google_news'`.

  

    



3. [**Level 3 β€” Frozen Parquet (Resilient Snapshot)** ]()



   For reproducible evaluations and operational resilience, Source 21 data can be frozen in `data/external/` and reused without issuing new requests.



In the documented reference execution, the [Google News RSS fallback]() generated [**351 FII-related articles**]() for [Source 2]()1, preserving continuity of the behavioral intelligence layer even when direct access to Reddit’s public API was unavailable. [page:46]








## [πŸ—οΈ High-Level Architecture]()







```mermaid

%%{init:{

'theme':'dark',

'themeVariables':{

'background':'#090d13',

'primaryTextColor':'#F5F7FA',

'lineColor':'#2dd4bf'

}}}%%



graph TD



NB00["NB00
21 DATA SOURCES
RSS β€’ Scraping β€’ Reddit"]:::bronze



NB01["BRONZE LAYER
Ingestion
feedparser β€’ BS4 β€’ PRAW"]:::bronze



NB02["SILVER LAYER
Cleaning & Normalization
Quality Gates"]:::silver



NB03["SILVER LAYER
MapReduce Word Count"]:::silver



NB04["GOLD LAYER
TF-IDF + BM25
Retrieval Index"]:::gold



NB05["GOLD LAYER
Sentiment Analysis
PT-BR Lexicon"]:::gold



NB06["GOLD LAYER
Marketing Intelligence
Signals β€’ Funnel β€’ Insights"]:::gold



NB07["SERVING LAYER
Dashboard Dataset"]:::dash



API["FASTAPI
Serving Layer
REST API"]:::dash



ST["STREAMLIT
Serving Layer
Dashboard"]:::dash



BOT["GROQ CHATBOT
LLM Layer
GPT-OSS-20B"]:::llm



NB00 --> NB01 --> NB02



NB02 --> NB03

NB02 --> NB04

NB02 --> NB05



NB03 --> NB06

NB04 --> NB06

NB05 --> NB06



NB06 --> NB07

NB07 --> API

NB07 --> ST



API --> BOT

ST --> BOT



classDef bronze fill:#2a1512,stroke:#a85a4a,color:#F5F7FA,stroke-width:2.5px;

classDef silver fill:#1b2430,stroke:#b0b7c3,color:#F5F7FA,stroke-width:2.5px;

classDef gold fill:#2a2208,stroke:#e6c35a,color:#F5F7FA,stroke-width:2.5px;

classDef dash fill:#06363d,stroke:#2dd4bf,color:#F5F7FA,stroke-width:2.5px;

classDef llm fill:#231433,stroke:#b56cff,color:#F5F7FA,stroke-width:2.5px;

```







> [!TIP]

> 

> Detailed architecture diagram β†’ [docs/architecture.md](https://github.com/Quantum-Software-Development/5-cybersecurity-social-engineering-fii-marketing-intelligence-platform/blob/5e7c18a109c56f765ea7cdbf16b8a65ad41a0e2a/docs/architecture.md)


















































## 4. [Project Structure]()



```text

app/

β”œβ”€β”€ main.py

β”œβ”€β”€ api/

β”‚   └── routes.py

β”œβ”€β”€ services/

β”‚   β”œβ”€β”€ retrieval.py

β”‚   β”œβ”€β”€ embeddings.py

β”‚   β”œβ”€β”€ llm.py

β”œβ”€β”€ models/

β”‚   └── schemas.py

β”œβ”€β”€ db/

β”‚   └── vector_store.py

β”œβ”€β”€ core/

β”‚   └── config.py

```








## 5. [API Layer (FastAPI)]()







```python

from fastapi import FastAPI

from app.api.routes import router



app = FastAPI(

    title="Market Intelligence API",

    description="RAG-powered financial intelligence system",

    version="1.0.0"

)



app.include_router(router)

```








## 6. [Core Endpoint β€” Semantic Query]()







```python

@router.post("/query")

async def query_system(question: str):

    

    context = retrieve_context(question)

    answer = generate_answer(question, context)



    return {

        "question": question,

        "context": context,

        "answer": answer

    }

```








## 7. [Retrieval Layer (RAG)]()







```python

def retrieve_context(query: str, k: int = 5):

    query_embedding = embed_query(query)

    results = search_vectors(query_embedding, k=k)

    return [r["text"] for r in results]

```








## 8. [Embeddings Layer]()







```python

from sentence_transformers import SentenceTransformer



model = SentenceTransformer("all-MiniLM-L6-v2")



def embed_query(text: str):

    return model.encode(text)

```








## 9. [Vector Store (FAISS)]()







```python

index = faiss.IndexFlatL2(384)



def search_vectors(query_embedding, k=5):

    D, I = index.search(np.array([query_embedding]), k)

    return [{"text": f"doc_{i}"} for i in I[0]]

```








## 10. [LLM Generation Layer]()







```python

def generate_answer(question, context):

    prompt = f"""

    Context:

    {context}



    Question:

    {question}



    Answer:

    """

    return call_llm(prompt)

```








## 11. [End-to-End Flow]()







| [Layer]()   | [Function]()                         |

| ------- | -------------------------------- |

| πŸ₯‰ [Bronze]()  | Raw ingestion and storage        |

| πŸ₯ˆ [Silver]()  | Data cleaning and NLP processing |

| πŸ₯‡ [Gold]()    | Signal generation and ranking    |

| [RAG ]()    | Semantic retrieval               |

| [FastAPI]() | API interface                    |

| [LLM]()    | Natural language reasoning       |








## 12. [Example Query]()







```json

{

  "question": "What is the current investor sentiment on logistics REITs?"

}

```







➠ [**Response:**]()



```json

{

  "answer": "Recent data indicates a moderately positive sentiment driven by stable dividend yields and occupancy rates."

}

```







## [Final Note]()



This architecture transforms a traditional data pipeline into a **full-stack AI intelligence system**, enabling:



[*]() semantic search 


[*]()  investor sentiment  


[*]()  real-time insights 


[*]()  natural language interaction









































## [How to run this project locally]()



### [Prerequisites]()



[-]() Python 3.10+ installed

[-]() Git installed

[-]() (Optional) Python virtual environment (venv) to isolate dependencies







### [Clone the repository]()



```bash

git clone https://github.com/Quantum-Software-Development/5-cybersecurity-social-engineering-fii-marketing-intelligence-platform.git

cd 5-cybersecurity-social-engineering-fii-marketing-intelligence-platform

```







### [Create and activate the virtual environment]]()



```bash

# macOS / Linux

python3 -m venv .venv

source .venv/bin/activate



# Windows (PowerShell)

python -m venv .venv

.\.venv\Scripts\Activate.ps1

```



> Note: the `.venv/` folder is already ignored in `.gitignore`, so the virtual environment will not be versioned. 







### [Install dependencies]()



```bash

pip install --upgrade pip

pip install -r requirements.txt

```







### [Run notebooks / scripts]()



- Open the notebooks in the `2-FIIs_Final` folder in Jupyter Notebook, JupyterLab, or VS Code.

- Make sure the selected kernel is the `.venv` virtual environment.

- Adjust data paths if needed (under the `data/` directory). Local data layers such as `data/bronze`, `data/silver`, and `data/gold` are git-ignored by default.







### [Whenever you add or remove dependencies:]()



```bash

pip freeze > requirements.txt

git add requirements.txt

git commit -m "Update project dependencies"

```








## [References]()



- Barocas, S., & Selbst, A. D. (2016). Big Data’s Disparate Impact. *California Law Review*, 104(3), 671–732.

- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. *Journal of Machine Learning Research (JMLR)*, 3, 993–1022.

- Brasil. (2018). *Lei nº 13.709, de 14 de agosto de 2018: Lei Geral de Proteção de Dados Pessoais (LGPD)*.

- Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). *CRISP-DM 1.0: Step-by-step data mining guide*. SPSS.

- European Commission. (2019). *Ethics Guidelines for Trustworthy AI*. Brussels: High-Level Expert Group on Artificial Intelligence.

- Goodfellow, I., Bengio, Y., & Courville, A. (2016). *Deep Learning*. MIT Press.

- Jurafsky, D., & Martin, J. H. (2025). *Speech and Language Processing* (3rd ed.). Stanford University.

- Manning, C. D., Raghavan, P., & SchΓΌtze, H. (2008). *Introduction to Information Retrieval*. Cambridge University Press.

- Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model Cards for Model Reporting. In *Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT)* (pp. 220–229).

- Molnar, C. (2022). *Interpretable Machine Learning* (2nd ed.). Lulu.com.

- Robertson, S. E., Walker, S., Jones, S., Hancock-Beaulieu, M., & Gatford, M. (1995). Okapi at TREC-3. In *Text REtrieval Conference (TREC-3)*. NIST.

- Robertson, S. E., & Zaragoza, H. (2009). The Probabilistic Relevance Framework: BM25 and Beyond. *Foundations and Trends in Information Retrieval*, 3(4), 333–389.

- Russell, S., & Norvig, P. (2021). *Artificial Intelligence: A Modern Approach* (4th ed.). Pearson.













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