Tired of word clouds? Want to do better sentiment analysis? Not sure how to look at the words underneath your measures?
Our long overdue paper on generalized word shift graphs is finally here!
So what are they?
Proportions, Shannon entropy, the KLD, the JSD, and dictionary methods can all be written as weighted averages
If the measure goes up, what does that mean? Why did it do that? Can we trust it?
Word shift graphs help us address those concerns
All weighted averages are a sum of contributions from individual words. We can pull out those words, and rank which ones contribute the most to the difference between two texts
Consider dictionary sentiment analysis. We don't just know the scores of words. We also have an understanding of which words are *more* or *less* positive
We know that there's a point on the sentiment scale that distinguishes positive from negative
For sentiment analysis, this gives us 4 qualitatively different ways a word can contribute:
1. + word is used more
2. - word is used less
3. + word is used less
4. - word is used more
This is how we construct basic word shift graphs. They give us details of both *what* words contribute and *how* they do so
And we've made it easy for you to make your own:
You can word shifts with multiple dictionaries, entropy-based measures, and any metric that can be written as a weighted avg
This ends up giving us 8 qualitatively different ways a word can contribute to the diff between two texts
We hope this paper can be the ultimate field guide for those are interested in using word shift graphs to help validate their own text-as-data analyses
We have new documentation that includes a comprehensive cookbook for using word shift graphs in Python
Please reach out w/ Qs
The Shifterator code has come a long way since then, and I've put a lot of time into it. I hope that people find it useful for their own work
I had a great time collaborating with @mrfrank5790 @lewis_math @andyreagan @ChrisDanforth @peterdodds and Aaron Schwartz on this Story Lab piece!
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It's technically brilliant, combining BERT, seq2seq, and Transformer XL
It's also a wonderful example of leveraging and customizing the fastai framework in a deep & thoughtful way.
Here's the full set of blog posts diving in to this
Amazing Research Software Engineer / Research Data Scientist positions within the @turinghut23 group at the @turinginst, at Standard (permanent) and Junior levels 🤩
👇 Here below a thread on who we are and what we
We are a highly diverse and interdisciplinary group of around 30 research software engineers and data scientists 😎💻 👉 https://t.co/KcSVMb89yx #RSEng
We value expertise across many domains - members of our group have backgrounds in psychology, mathematics, digital humanities, biology, astrophysics and many other areas 🧬📖🧪📈🗺️⚕️🪐
/ @DavidBeavan @LivingwMachines
In our everyday job we turn cutting edge research into professionally usable software tools. Check out @evelgab's #LambdaDays 👩💻 presentation for some examples:
We create software packages to analyse data in a readable, reliable and reproducible fashion and contribute to the #opensource community, as @drsarahlgibson highlights in her contributions to @mybinderteam and @turingway: https://t.co/pRqXtFpYXq #ResearchSoftwareHour
First step is to create the underlying network data. We need one file of "nodes" - i.e. the people and organizations. And one file of "edges" - i.e. the connections between them.
I created these by hand, based on excellent investigate journalism:
Now we can pull these together to create a network visualization!
You'll notice that I included a column for "type" in the nodes file. This allows me to use different icons for people vs firms vs political organizations.
All the icons are taken from @fontawesome. I *think* the visNetwork 📦 currently only works with fontawesome version 4.7, which is a bit limited – e.g. I decided to use a book icon to represent the fringe Evangelical Christian sect "Exclusive Brethren"! 😂
I very much enjoyed getting to use the "incognito" icon to represent all the unknown donors that have funded Tory MP Owen Paterson's overseas jaunts!
(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.
(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).
(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.
(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.
It has a robust data structure, Dataframe for manipulation and analyzing data.
Here's some tips to help you work better with pandas. Let's go! ↓
If you're not aware about what a Dataframe is, It's an optimized data structure for loading data, analysing it, manipulating data in it, and Mostly gathering insights.
It uses Cython backend which transpiles into C for optimized code.
Here's how a dataframe looks like,
Before we start, You need to ensure, you have pandas installed. If you don't, Do that before moving ahead!
Here are the tips, Let's go!
1/ Convert PD series to Dataframe
We all have struggled, when we deal with pandas series. It's always easier to work with Dataframes, rather than series. Here is how you can convert series to dataframe easily.
2/ How to create dummy Dataframe for testing
We always need dataframes for testing and analysing normally, if we do not have data ready. Here is how you can use Pandas API to generate different types of data.
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Czego w artykule brakuje, to informacji, że SMP prawdopodobnie przekazało Williamsowi część środków na sfinansowanie sezonu 2019. W przypadku zakończenia współpracy ekipa z Grove będzie musiała zwrócić te środki. #F1pl
To tłumaczy wysokie kwoty jakich Williams ma oczekiwać za fotel od nowego kierowcy. Jest pewnie próg opłacalności i dopóki nie zostanie osiągnięty, to zmiana z finansowego punktu widzenia nie będzie się zwyczajnie opłacała. #F1pl
Tyle można znaleźć w oświadczeniach prasowych... 😂😂😂
For three years I have wanted to write an article on moral panics. I have collected anecdotes and similarities between today\u2019s moral panic and those of the past - particularly the Satanic Panic of the 80s.— Ashe Schow (@AsheSchow) September 29, 2018
This is my finished product: https://t.co/otcM1uuUDk
The 3 big things that made the 1980's/early 1990's surreal for me.
1) Satanic Panic - satanism in the day cares ahhhh!
2) "Repressed memory" syndrome
3) Facilitated Communication [FC]
All 3 led to massive abuse.
"Therapists" -and I use the term to describe these quacks loosely - would hypnotize people & convince they they were 'reliving' past memories of Mom & Dad killing babies in Satanic rituals in the basement while they were growing up.
Other 'therapists' would badger kids until they invented stories about watching alligators eat babies dropped into a lake from a hot air balloon. Kids would deny anything happened for hours until the therapist 'broke through' and 'found' the 'truth'.
FC was a movement that started with the claim severely handicapped individuals were able to 'type' legible sentences & communicate if a 'helper' guided their hands over a keyboard.
Seen a couple of these panels make the rounds from time to time, so here's the complete set of 10 (something to amuse and/or offend almost everyone).
Chapter 1: The Super Patriot
Chapter 2: The Ku Klux Klansman
Chapter 3: The American Student
Chapter 4: The Right-Wing Extremist
1/ I've had an unhealthy fascination with metrology (the study of measurement) ever since my 2nd year as a physics major when I took a class devoted to duplicating historic physics experiments, so please indulge me for going into heavy detail (get it?) about the kilogram.
2/ So what actually *defines* a unit of measurement? If you're American, you probably know a mile is 5280 feet and a foot is 12 inches and an inch is 2.54 centimeters etc. But where does this chain of definitions end? Is it turtles all the way down?
3/ It's actually not! For all units (even the imperial units used in America) the answers all end with the Système International (SI) unit definitions established and maintained for over 100 years by the Bureau International des Poids et Mesures (BIMP) in France.
4/ At the base of this tower are the SI base units. Just 7 SI base units define every other unit in existence. They are:
Kilogram, kg (mass)
Meter, m (distance)
Second, s (time)
Kelvin, K (temp)
Ampere, A (electric current)
Candela, cd (luminous intensity)
Mole, mol (quantity)
India had a well developed education system centuries before the westerners arrived and called us uncivilized. Education was always given a great importance in Indian civilisation since times immemorial.
Ancient India had the Gurukulas and Ashramas as the epicentres of the knowledge and enlightenment. Bigger Gurukulas served as the centres of higher education called universities. Besides these universities, temples also emerged as the major learning centres.
Studying the Holy Scriptures, character building, personality development, responsibilities towards self,family and society,discipline and preservation of the ancient culture and heritage were the key embodiments of education.
This kind of education system made ancient India, a centre of knowledge all over the world. Many foreign students came to India for education and India was called the 'Vishwaguru'.
Takshshila and Nalanda were the two prominent universities of ancient India.
But were these only Universities of ancient India?The answer is 'No'.Let's learn about these gems of our education system that flourished across ancient India & be proud.
This was the oldest university of ancient India.Situated in Nalanda distt. of Bihar...