Have you ever read a book and found that this book was similar to another book that you had read before? I have already. Practically all self-help books that I read are similar to Napolean Hill’s books.
So I wondered if Natural Language Processing (NLP) could mimic this human ability and find the similarity between documents.
To find the similarity between texts you first need to define two aspects:
Frequency and Sentiment Analysis.
In part 1, I used transformers trained in the task of Questions and Answering to answer the question: “ Can a machine learning model answer questions about Finance and Economy?”. If you haven’t read this article yet, don’t waste any more time and click here to read it.
In this article I will use NLP techniques to answer the questions:
1. What are the most used terms of Buffet and if those terms changed along the time?
2. How was his feeling about the Economy and Stocks Market over the years?
A curiosity that I had…
Warren Buffet is an American investor, philanthropist, and the actual number 6 on the Forbes billionaires list. He has an overall gain of 2,810,526% from 1965 to 2020 while the S&P500 (index of the 500 largest U.S. publicly traded companies) rose 23,454%. He is considered the best investor of all time and an inspiration for a lot of people, I included.
To combine my passion for technology and the financial market, I will write a series of posts to see if the use of Artificial Intelligence (AI) could help me understand the mind of Buffet by answering the 3 questions…
Since the R-CNN Object Detection (OD) was released in 2014 until now, deep learning approaches to object detection problems are becoming more and more popular.
OD is now used in almost every field from agriculture to autonomous driving. But one of the main disadvantages of the OD techniques is the lack of interpretability of its outputs that are not human-readable and this is a big concern.
Since that prediction without interpretable explanation will have restricted applicability eventually.
Until recently the only way we could measure how good the outcome of a model is was to evaluate its predictions using a…
Arvato Financial Solutions is a company that provides services that helps other companies through the complexity of credit management since 1961. Nowadays, 7,000 experts are delivering efficient credit management solutions in around 15 countries around the globe.
For the Capstone Project of Udacity Data Science Nanodegree, Arvato Financial Solutions kindly made its data of a real problem available so the students could apply the theory learned to a practical problem.
In this Capstone Project, the goal was to analyze demographics data for customers of a mail-order sales company in Germany, comparing it against demographics information for the general population.
Joe Biden won the election and he is going to be the 46th President of the United States, there are rumours about a second covid-19 wave, the covid-19 vaccines are closer than ever. Reading all those headlines and as a stock market investor a question came to my mind: How does exogenous factors influence the stocks prices?
So as an activity of the Data Science Nanodegree I am taking at Udacity and to try to combine the two things that I love the most that is data analysis and finance I decide to analyse the stock market data, using Data…
Data Scientist @ Virtus | UCI & UFCG Alumni