twic | Topic Words in Context | Data Visualization library
kandi X-RAY | twic Summary
kandi X-RAY | twic Summary
Thanks for trying out Topic Words in Context, a.k.a. TWiC. TWiC is a data visualization created by Jonathan Armoza that provides hierarchical, top-down and bottom-up views of LDA topic models generated by the popular topic modeler, MALLET. It was developed under the advisement and support of Stéfan Sinclair and Peter Gibian at McGill University. TWiC was born out of the need for digital humanities researchers to understand how the topic word list outputs from topic modelers like MALLET relate to the texts they attempt to model. The visualization presents users with multiple related views in the form of reorganizable and resizable panels, each of which portray how topics are distributed throughout the collection of texts (a.k.a. "corpus") being modeled. These views look from all the way above the model – the "big data" view – and dive downward to where topic words are situated in their original contexts: the model's texts themselves. Geometric icons, known in TWiC as data shapes represent the model data (topic distributions, clusters of texts, texts, and topic words) of each view. The primary view of topic model data in TWiC – what is termed a "level" in TWiC – shows a topic model from the perspective of the model data, that is, how the model situates texts by topic. However, TWiC also has the ability to reinsert these data shapes into original publication arrangements. This alternate level shares a few panels with TWiC's primary level, but its focus is a panel called the Publication View. TWiC has "server"- and "client"-side components. It consists of custom Python scripts (server-side) which run MALLET and re-interpret its outputs, and a web browser visualization (client-side) which uses D3, jQuery, and Packery as well as a custom JavaScript library that organizes and controls TWiC's data shapes and panels.
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Top functions reviewed by kandi - BETA
- Define getStyle and set the appropriate style for the current element .
- Add Plugin methods
- Splits a text .
- Initialize a new DatePicker .
- Get object size
- Convert HSL to RGB
- Sets up the style for the window .
- Normalizes effect arguments
- Bridge plugin methods
- Convert RGB values to HSV
twic Key Features
twic Examples and Code Snippets
Community Discussions
Trending Discussions on twic
QUESTION
Planning to use variables to authorize tweepy with Twitter's API, but it shows the following:
TypeError: Consumer key must be string or bytes, not NoneType when authentication
Code below:
...ANSWER
Answered 2022-Mar-08 at 12:42Perhaps a typo problem? NoneType is caused because one of the methods is receiving None, which is returned by get() if the environment variable does not exist.
Check if your environment variables' names are correct.
If your problem persists please indicate in which line the error occurs.
QUESTION
The dashboard include a dropdown, a table and a graph. By selecting a item from dropdown, some rows of the table is shown and figure plot the data in the table. I have two callback,
...ANSWER
Answered 2021-Aug-19 at 14:19The problem is that you have two inputs for the callback that's running twice:
QUESTION
I'm trying to create an outlined button with an icon in it
Here's the shortened version of my CSS for styling the SVG:
...ANSWER
Answered 2021-May-03 at 16:38It's because of the mask. You can replace it with the filled version:
QUESTION
Goal:
- On a low-end device (raspberry pi 3)
- Listen for a fixed set of audio phrase commands (my version of 'Hey Google' or 'Hey Siri')
- That can be a very constrained vocabulary (less than 10 commands)
- Trigger a Kotlin function when the command is detected.
- Without using a ton of CPU, or a ton of network bandwidth.
AFAIK modern edge devices (Echo, Smart Phones, Google Home, etc) have very fancy hardware+software solutions that allow them to continuously listen for keywords without sucking up a ton of CPU, and without having to send all audio up to a cloud server. I'd like to have the same, but am not sure if it is even possible - I'm sure they trained their minimal and efficient 'Hey Siri' ML model to handle all sorts of accents, volumes, cadence, ages, background noise, etc.
- The Java Speech API (JSAPI) seems... iffy. Many of the examples are old, and either point to unsupported libs, or ended up using Google Cloud Speech.
- This doesn't have to be a Java/Kotlin library, I could also wrap a native command listener process.
- I'm looking at ML Kit and Firebase ML, but didn't see audio to command conversion.
- It would be best if I could tune the sensitivity, small children wearing costume masks yell 'TWIC R TREET' or 'TMURMP... TWEEF' or whatever should still do it.
- ... But not a pure volume detector, a car driving by shouldn't trigger it.
Any suggestions? Or is this unreasonable to ask of an rpi?
...ANSWER
Answered 2020-Sep-11 at 20:55Yes there is a quite useful library that I recommend: https://cmusphinx.github.io/
QUESTION
I am newbie of spark. I have a use case which I am struggling to get it done. My use case is - There is a log file generated by web-server containing logs in following format.
...ANSWER
Answered 2020-Sep-09 at 19:47You can get the hour from the timestamp column (just take the first two digits) and then group by host and hour.
QUESTION
I am doing stemming
using Porter
and Lancaster
and I find these observations:
ANSWER
Answered 2020-Feb-25 at 05:59replied
. Why?
It's because Lancaster stemmer implementation is improved in https://github.com/nltk/nltk/pull/1654
If we take a look at https://github.com/nltk/nltk/blob/develop/nltk/stem/lancaster.py#L62, there's a suffix rule, to change -ied > -y
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