The document contains a series of tweets urging people to take action to stop fast track approval of the Trans-Pacific Partnership (TPP) trade agreement. The tweets promote visiting StopFastTrack.com to tell Congress to stop fast track and encourage participation in rallies, protests, and other events happening around the country as part of a Week of Action against fast track and the TPP.
Estante virtual Rosane Domingues
-PS: se atente nas alterações das leis.
Alteração no ECA e revogação de artigo da Lei de Contravenções Penais
http://romulomoreira.jusbrasil.com.br/noticias/174735226/alteracao-no-eca-e-revogacao-de-artigo-da-lei-de-contravencoes-penais
Estante virtual Rosane Domingues
-PS: se atente nas alterações das leis.
Alteração no ECA e revogação de artigo da Lei de Contravenções Penais
http://romulomoreira.jusbrasil.com.br/noticias/174735226/alteracao-no-eca-e-revogacao-de-artigo-da-lei-de-contravencoes-penais
A maior competição esportiva do mundo. Os jogos mais conectados de toda a história. Por duas semanas, pessoas de todo o mundo pararam para assistir atletas superarem seus limites nos Jogos Olímpicos do Rio de Janeiro em agosto e setembro de 2016.
Hacktivistas de todo o mundo também se juntaram em torno da OpOlympicHacking, aproveitando o foco midiático para discutir os problemas sociais e econômicos atuais: conflito racial nos EUA, terrorismo e crise dos imigrantes na Europa, Israel versus Palestina, crise na Venezuela, coxinhas versus mortadelas no Brasil.
Só que não.
Nesta apresentação, realizada na H2HC 2016, discutimos a OpOlympicHacking e se o mundo ainda precisa do Hacktivismo.
A publication designed in response to and for Lauren Bratslavsky ,Nathan Carpenter & Joseph Zompetti's academic article "Twitter, incivility, and presidential communication: A theoretical incursion into spectacle and power" that emphasises the analysis of its subject matter.
Text mining, also known as text analytics or text data mining, is a data mining technique that involves extracting meaningful information and knowledge from unstructured textual data. It involves the process of analyzing and deriving insights from large volumes of text data to uncover patterns, trends, and relationships.
Here's an overview of text mining:
Data Collection: The first step in text mining is gathering relevant textual data from various sources, such as documents, web pages, social media, emails, customer reviews, or survey responses.
Text Preprocessing: Text data often requires preprocessing to clean and prepare it for analysis. This involves removing irrelevant information like stopwords (common words like "and" or "the"), punctuation, and special characters. It may also involve stemming or lemmatization to reduce words to their root form.
Tokenization: Tokenization is the process of splitting the text into individual words or tokens. It is a fundamental step that converts the text into a format suitable for analysis.
Text Mining Techniques:
Sentiment Analysis: Sentiment analysis aims to determine the sentiment or opinion expressed in a piece of text, whether it's positive, negative, or neutral. It is commonly used for analyzing customer feedback, social media sentiment, or online reviews.
Topic Modeling: Topic modeling is a technique used to discover latent topics or themes within a collection of documents. It helps identify the main subjects or areas of discussion in the text data.
Named Entity Recognition (NER): NER identifies and extracts specific entities such as names of people, organizations, locations, dates, or product names mentioned in the text.
Text Classification: Text classification involves categorizing or classifying documents into predefined categories or labels. It can be used for tasks such as spam detection, sentiment classification, or topic classification.
Text Clustering: Text clustering aims to group similar documents together based on their textual content. It helps in discovering patterns or similarities within the text data without predefined categories.
Information Extraction: Information extraction focuses on identifying structured information from unstructured text, such as extracting key phrases, relationships, or events mentioned in the text.
Text Summarization: Text summarization aims to generate a concise summary of a long text or document, capturing the main ideas and important information.
Visualization: Text mining often involves visualizing the results to gain insights and communicate findings effectively. Word clouds, bar charts, network diagrams, or heatmaps are examples of visualizations commonly used in text mining.
Interpretation and Applications: The interpretation of text mining results involves extracting meaningful insights, patterns, or knowledge from the analyzed text data. These insights can be used for various applications such as market research, customer feedback ana
A maior competição esportiva do mundo. Os jogos mais conectados de toda a história. Por duas semanas, pessoas de todo o mundo pararam para assistir atletas superarem seus limites nos Jogos Olímpicos do Rio de Janeiro em agosto e setembro de 2016.
Hacktivistas de todo o mundo também se juntaram em torno da OpOlympicHacking, aproveitando o foco midiático para discutir os problemas sociais e econômicos atuais: conflito racial nos EUA, terrorismo e crise dos imigrantes na Europa, Israel versus Palestina, crise na Venezuela, coxinhas versus mortadelas no Brasil.
Só que não.
Nesta apresentação, realizada na H2HC 2016, discutimos a OpOlympicHacking e se o mundo ainda precisa do Hacktivismo.
A publication designed in response to and for Lauren Bratslavsky ,Nathan Carpenter & Joseph Zompetti's academic article "Twitter, incivility, and presidential communication: A theoretical incursion into spectacle and power" that emphasises the analysis of its subject matter.
Text mining, also known as text analytics or text data mining, is a data mining technique that involves extracting meaningful information and knowledge from unstructured textual data. It involves the process of analyzing and deriving insights from large volumes of text data to uncover patterns, trends, and relationships.
Here's an overview of text mining:
Data Collection: The first step in text mining is gathering relevant textual data from various sources, such as documents, web pages, social media, emails, customer reviews, or survey responses.
Text Preprocessing: Text data often requires preprocessing to clean and prepare it for analysis. This involves removing irrelevant information like stopwords (common words like "and" or "the"), punctuation, and special characters. It may also involve stemming or lemmatization to reduce words to their root form.
Tokenization: Tokenization is the process of splitting the text into individual words or tokens. It is a fundamental step that converts the text into a format suitable for analysis.
Text Mining Techniques:
Sentiment Analysis: Sentiment analysis aims to determine the sentiment or opinion expressed in a piece of text, whether it's positive, negative, or neutral. It is commonly used for analyzing customer feedback, social media sentiment, or online reviews.
Topic Modeling: Topic modeling is a technique used to discover latent topics or themes within a collection of documents. It helps identify the main subjects or areas of discussion in the text data.
Named Entity Recognition (NER): NER identifies and extracts specific entities such as names of people, organizations, locations, dates, or product names mentioned in the text.
Text Classification: Text classification involves categorizing or classifying documents into predefined categories or labels. It can be used for tasks such as spam detection, sentiment classification, or topic classification.
Text Clustering: Text clustering aims to group similar documents together based on their textual content. It helps in discovering patterns or similarities within the text data without predefined categories.
Information Extraction: Information extraction focuses on identifying structured information from unstructured text, such as extracting key phrases, relationships, or events mentioned in the text.
Text Summarization: Text summarization aims to generate a concise summary of a long text or document, capturing the main ideas and important information.
Visualization: Text mining often involves visualizing the results to gain insights and communicate findings effectively. Word clouds, bar charts, network diagrams, or heatmaps are examples of visualizations commonly used in text mining.
Interpretation and Applications: The interpretation of text mining results involves extracting meaningful insights, patterns, or knowledge from the analyzed text data. These insights can be used for various applications such as market research, customer feedback ana
1. TODAY S TWEETS:
Tell Congress to #StopFastTrack for the #TPP! Join the Week of Action at
StopFastTrack.com pic.twitter.com/mY0ezfStSU
Obama & McConnell pledging cooperation on #TPP, but we pledge resistance!
#StopFastTrack Week of Action is on! StopFastTrack.com
Negotiators missed #TPP deadline for 3rd yr in row! Tell Congress to
#StopFastTrack & kill this beast forever StopFastTrack.com
No to privatization! Yes to our kids' future! Teachers are latest constituency
to #StopFastTrack for #TPP http://www.stopfasttrack.com/#aft @AFTunion
G20 leaders need to know, there will never be Fast Track for the #TPP! Take
action at StopFastTrack.com #StopFastTrack
More than 700,000 tell Congress to #StopFastTrack for the #TPP & #TTIP
http://sc.org/1oovwqB
PIC: Olympia urges @RepDennyHeck to #StopFastTrack for job-killing #TPP. Call
your Rep thru StopFastTrack.com pic.twitter.com/eGx37HyqW9
PIC: @FlushtheTPP brought its #StopFastTrack train back to DC to help launch the
new week of action http://tinyurl.com/monpuvv
Secret trade agreements flood our communities with fracking; let's flood
Congress' inboxes to #StopFastTrack! http://bit.ly/1dFSALl
TUES: Stop the sneak attack on democracy! Join the #StopFastTrack light brigade
2nite in Seattle StopFastTrack.com
VIDEO: San Diego keeps sending @RepSusanDavis msg to #StopFastTrack for #TPP -
Next rally Wed StopFastTrack.com http://tinyurl.com/omc625j
WED: Tell @RepSusanDavis to #StopFastTrack for the #TPP at San Diego action
StopFastTrack.com
Join @MoveOn for ‪# StopFastFast‬ rally in Denver tonight, 5pm, at
Veterans Park StopFastTrack.com #TPP #TTIP
Join #StopFastTrack leafleting Wednesday in Albany, NY --> or act now at
StopFastTrack.com #TPP
Join the students protecting access to medicine by urging Congress to
#StopFastTrack for the #TPP http://thndr.it/1xu1eTp
#StopFastTrack Week of Action is off to a roaring start!
http://tinyurl.com/qxfy637
Listen to the @RickSmithShow for daily updates on the #StopFastTrack Week of
Action from @citizenstrade & others http://ricksmithshow.com/
Some in Congress would rubber stamp secret trade agreements that harm our air &
water. Tell them to #StopFastTrack! http://bit.ly/1dFSALl
Watch Ana Tijoux's amazing "No al #TPP" video & then take action to
#StopFastTrack http://youtu.be/OBLtU-X5oVk