M'interessa   Learning by choosing

Presentation

Presentation of M’interessa project

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Model assessment

M’interessa project is, in fact, an information retrieval solution, a binary classification product. So, information retrieval metrics must be used in order to assess the model of the project.

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Feature extraction

The features extracted from each tweet and used in the model are based on the API reference page about the Tweet objects (https://dev.twitter.com/overview/api/tweets), we’ve got the following attributes into each Tweet:

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Data pipeline

The process that the data follows is the one shown in the diagram:

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Kafkian: or how we deploy and set-up our Kafka broker

What is Apache Kafka?

Apache Kafka is an industry standard solution for creating real-time data pipelines involving several subsystems. It is a queing system based on a publish-subscribe (PubSub) model, where producers publish messages to a topic (or several topics) and consumers subscribe to topics. Each topic is similar to a queue, hence consumers remove messages from the queue. It comes with built-in replication, and offers high scalability. As such, in general we talk about interacting with a Kafka cluster.

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ETL from Kafka logs

Objective

The main objective is to fetch the data from Kafka and perform some (more or less sophisticaded) filters over the gathered tweets in order to:

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Scrapping the web from twitter

Objectives

The main objective is to analyze the web pages mentioned by the tweet to determine if they are interesting for the user.

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Near duplicate detection

Twitter is full of duplicated or near-duplicated content (ND for short from now onwards). Flooding a user’s candidate tweet list with a bunch of items that have (almost) the same content can only lead to a bad user experience.

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