Rusty  Shanahan

Rusty Shanahan

1599194520

Analysis of Presidential Speeches throughout American History

Natural Language Processing intrigued me from the beginning. I first heard about it at a presentation of an author predictor based on word patterns. It later was explained to me with song lyrics across the decades. So I sought to also use it historically. And where could I get a lot of data? Presidents: they love to talk! Or at least most did. So I created a NLP project on Presidential speeches in American history. It includes all 44 presidents from George Washington’s first inauguration in 1789 to speeches on Coronavirus at the end of April of 2020. Yes, I said 44 presidents as Grover Cleveland had two separate terms. Fair warning as you read along: I was an American Studies major and taught history for 13 years. So don’t mind me as I throw in some presidential knowledge along with my knowledge on Data Science. Enjoy!

Speech Organization

I obtained the majority of my speeches from UVA’s Miller Center. Their collection of speeches and other primary sources is considered top-notch, even being referenced by Harvard’s database. As I started cleaning up and analyzing my data, I realized some presidents should have had more speeches than were present in this collection. I could fix this for Truman and Eisenhower by adding in their missing State of the Union Speeches from the NLTK’s corpus. Every other president in this corpus had their SOU speeches already in the Miller Center collection. In total my analysis included 1018 speeches with approximately 23.8 million words. The math side of me did have to look at a few numbers and stats. The shortest speech came from George Washington’s Second Inaugural Address with 787 words. On the other hand, the longest speech goes to Harry Truman’s State of the Union address in 1946 at just shy of 170 thousand. He had to discuss such historic topics as the post-war economy, protection for veterans, the creation of the United Nations, communism concerns…and that doesn’t even cover the first half!

#history #data-science #president #nlp #data analysis

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Analysis of Presidential Speeches throughout American History
Rusty  Shanahan

Rusty Shanahan

1599194520

Analysis of Presidential Speeches throughout American History

Natural Language Processing intrigued me from the beginning. I first heard about it at a presentation of an author predictor based on word patterns. It later was explained to me with song lyrics across the decades. So I sought to also use it historically. And where could I get a lot of data? Presidents: they love to talk! Or at least most did. So I created a NLP project on Presidential speeches in American history. It includes all 44 presidents from George Washington’s first inauguration in 1789 to speeches on Coronavirus at the end of April of 2020. Yes, I said 44 presidents as Grover Cleveland had two separate terms. Fair warning as you read along: I was an American Studies major and taught history for 13 years. So don’t mind me as I throw in some presidential knowledge along with my knowledge on Data Science. Enjoy!

Speech Organization

I obtained the majority of my speeches from UVA’s Miller Center. Their collection of speeches and other primary sources is considered top-notch, even being referenced by Harvard’s database. As I started cleaning up and analyzing my data, I realized some presidents should have had more speeches than were present in this collection. I could fix this for Truman and Eisenhower by adding in their missing State of the Union Speeches from the NLTK’s corpus. Every other president in this corpus had their SOU speeches already in the Miller Center collection. In total my analysis included 1018 speeches with approximately 23.8 million words. The math side of me did have to look at a few numbers and stats. The shortest speech came from George Washington’s Second Inaugural Address with 787 words. On the other hand, the longest speech goes to Harry Truman’s State of the Union address in 1946 at just shy of 170 thousand. He had to discuss such historic topics as the post-war economy, protection for veterans, the creation of the United Nations, communism concerns…and that doesn’t even cover the first half!

#history #data-science #president #nlp #data analysis

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

Ian  Robinson

Ian Robinson

1623856080

Streamline Your Data Analysis With Automated Business Analysis

Have you ever visited a restaurant or movie theatre, only to be asked to participate in a survey? What about providing your email address in exchange for coupons? Do you ever wonder why you get ads for something you just searched for online? It all comes down to data collection and analysis. Indeed, everywhere you look today, there’s some form of data to be collected and analyzed. As you navigate running your business, you’ll need to create a data analytics plan for yourself. Data helps you solve problems , find new customers, and re-assess your marketing strategies. Automated business analysis tools provide key insights into your data. Below are a few of the many valuable benefits of using such a system for your organization’s data analysis needs.

Workflow integration and AI capability

Pinpoint unexpected data changes

Understand customer behavior

Enhance marketing and ROI

#big data #latest news #data analysis #streamline your data analysis #automated business analysis #streamline your data analysis with automated business analysis

Enos  Prosacco

Enos Prosacco

1598776206

History MCQs to Test Your History Knowledge

The beginning of the medieval period is ordinarily taken to be the moderate breakdown of the Gupta Empire from around 480 to 550, closure the “old style” period, just as “antiquated India”, albeit both these terms might be utilized for periods with broadly various dates, particularly in specific fields, for example, the historical backdrop of workmanship or religion. At any rate in northern India, there was no bigger state until maybe the Delhi Sultanate, or unquestionably the Mughal Empire. By 1413, the Tughlaq dynasty completely declined and the neighboring governor captured Delhi and this led to the start of the Sayyid Dynasty. In 1398, Timur attacked India and ransacked Indian riches. While returning back, he named Khizr Khan as the legislative head of Delhi.

#indian history tutorials #history mcqs #indian history mcqs #indian history quiz

Ray  Patel

Ray Patel

1623292080

Getting started with Time Series using Pandas

An introductory guide on getting started with the Time Series Analysis in Python

Time series analysis is the backbone for many companies since most businesses work by analyzing their past data to predict their future decisions. Analyzing such data can be tricky but Python, as a programming language, can help to deal with such data. Python has both inbuilt tools and external libraries, making the whole analysis process both seamless and easy. Python’s Panda s library is frequently used to import, manage, and analyze datasets in various formats. However, in this article, we’ll use it to analyze stock prices and perform some basic time-series operations.

#data-analysis #time-series-analysis #exploratory-data-analysis #stock-market-analysis #financial-analysis #getting started with time series using pandas