Automatic Topic Discovery in Political Blog Posts

Web data mining is a major component in the data science activities landscape. With its use, we can track brand mentions from social media statuses, policy sentiment on forums and "hot" topics from political blog posts.

In this article, we'll ding into the posts from the politics's rubric of the site Journal Du Cameroun (JDC) to find what are the topics developed there. The scraping was done on May the 10th 2017 and the dataset includes 136 articles between February 17, 2017 and May 9, 2017.

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The Voice Afrique Tweets Mining Part 4

Previously, we explore topic modeling an algorithm used to discover what users are talking about. But often, its not important to know what users are saying, but how they are saying it. Sentiment analysis seeks to automatically associate a piece of text with a sentiment score, a positive or negative emotional score. Aggregating sentiment can give an idea of how people are responding to an event or a topic.

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The Voice Afrique Tweets Mining Part 3

In the social network analysis part, we explored a model that exploits the links between the entities to help us find the key players in the data. Here, we will focus on the tweet’s text to better understand what the users are talking about. We move away from the network model we’ve used previously and discuss other methods for text analysis. We first explore topic modeling, an approach that finds natural topics within the text. We then move on to sentiment analysis, the practice of associating a document with a sentiment score

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