The TweetLID shared task consists in identifying the language or languages in which tweets are written. Focusing on events, and news in the Iberian Peninsula, the main focus of the task is the identification of tweets written in the 5 top languages from the Peninsula (Basque, Catalan, Galician, Spanish, and Portuguese), and English We will provide the participants of the task with a training corpus that includes approximately 15,000 tweets manually annotated with the language(s). The participants will have a month to develop and tweak their language identification systems from this training corpus. They will have apply their system on the test set afterwards, and submit the output of the system, which will be evaluated and compared to the other participants’ systems. It is worth noting that some tweets are written in more than one language (e.g., partly in Portuguese, and partly in Galician), and that the language cannot be determined in some cases (e.g., “jajaja”). The corpus also takes into account these specific cases, providing annotations such as “ca+es” (written in Catalan and Spanish), “ca/es” (it can be either Catalan or Spanish, it does not make a difference in this case), “other” (it is written in a language that is not considered in the task), o “und” (when it cannot be determined).

sparkmagic

Jupyter magics and kernels for working with remote Spark clusters

stan-statespace

Stan models for state space time series

t81_558_deep_learning

Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks

taller

tensorflow_cookbook

Code for Tensorflow Machine Learning Cookbook

topic_modelling

Contains Jupiter Notebooks of stuff I am working on.

tweets_map

Notebooks explaining how to plot a tweets heatmap of a city

twitter-trends

Twitter Trends is a web-based application that automatically detects and analyzes emerging topics in real time through hashtags and user mentions in tweets. Twitter being the major microblogging service is a reliable source for trends detection. The project involved extracting live streaming tweets, processing them to find top hashtags and user mentions and displaying details for each trending topic using trends graph, live tweets and summary of related articles. It also included Topic Modelling and Entity Categorization to classify the tweets and extract valuable information about its contents and find similar tweets and related articles and URLs. A trending topic is represented as a word cloud created from set of keywords (hashtags or user mentions) that belong to that topic. Thus this application provides the required information to get an overhaul of the topics which are trending at that particular time. This data can be used to support social analysis, finance, marketing or news tracking.

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