Classification with Scikit-Learn

Posted on Leave a commentPosted in Classification, scikit-learn

1. Introduction For python programmers, scikit-learn is one of the best libraries to build Machine Learning applications with. It is ideal for beginners because it has a really simple interface, it is well documented with many examples and tutorials. Besides supervised machine learning (classification and regression), it can also be used for clustering, dimensionality reduction, […]

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Regression, Logistic Regression and Maximum Entropy

Posted on 4 CommentsPosted in Classification, Machine Learning, Sentiment Analytics

update: The Python code for Logistic Regression can be forked/cloned from my Git repository. It is also available on PyPi. The relevant information in the blog-posts about Linear and Logistic Regression are also available as a Jupyter Notebook on my Git repository. 1. Introduction One of the most important tasks in Machine Learning are the Classification tasks […]

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Sentiment Analysis with the Naive Bayes Classifier

Posted on 12 CommentsPosted in Machine Learning, Sentiment Analytics

From the introductionary blog we know that the Naive Bayes Classifier is based on the bag-of-words model. With the bag-of-words model we check which word of the text-document appears in a positive-words-list or a negative-words-list. If the word appears in a positive-words-list the total score of the text is updated with +1 and vice versa. […]

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Sentiment Analysis with bag-of-words

Posted on 7 CommentsPosted in Machine Learning, Sentiment Analytics

update: the dataset containing the book-reviews of has been added to the UCI Machine Learning repository. Introduction: In my previous post I have explained the Theory behind three of the most popular Text Classification methods (Naive Bayes, Maximum Entropy and Support Vector Machines) and told you that I will use these Classifiers for the automatic […]

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Visualizing Data

Posted on Leave a commentPosted in Visualizations

We all know that visualizing data is an important part of Data Science. If it is done wrong, it can be boring not grabbing the attention of the readers, or even worse; convey the wrong message. If it done correctly, it can intrigue even the most indifferent reader (some people can even turn Data Visualizations into […]

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Text Classification and Sentiment Analysis

Posted on 10 CommentsPosted in Machine Learning, Sentiment Analytics

Introduction: Natural Language Processing (NLP) is a vast area of Computer Science that is concerned with the interaction between Computers and Human Language[1]. Within NLP many tasks are – or can be reformulated as – classification tasks. In classification tasks we are trying to produce a classification function which can give the correlation between a […]

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Collecting Data from Twitter

Posted on 30 CommentsPosted in Data Mining

update: The Python code for this TwitterScraper can be forked/cloned from my Git repository. ———– For most people, the most interesting part of the previous post, will be the final results. But for the ones who would like to try something similar or the ones who are also curious about the technical part, I will explain the […]

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Post-Election Analysis: Twitter vs traditional polls

Posted on 4 CommentsPosted in Twitter Analytics

As promised, here is the post-election analysis. Although my predicted voting percentage for AKP was much closer to the actual result compared to most of the traditional polls, it is also true that my predicted value for MHP is far off, making the overall prediction error bigger than most conventional polls (see table below).  Election results […]

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