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 […]
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. […]
In the previous post we have learned how to do basic Sentiment Analysis with the bag-of-words technique. Here is a short summary: To keep track of the number of occurences of each word, we tokenize the text and add each word to a single list. Then by using a Counter element we can keep track […]
update: the dataset containing the book-reviews of Amazon.com 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 […]
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 […]
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 […]
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 […]
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 […]
Short summary: According to Analysis of Twitter data, the upcoming elections of 1 November will result in a victory for AKP. AKP: 47.13% CHP: 22.35% MHP: 18.84% HDP: 11.68% I will keep updating these numbers as more Twitter data is collected. Not so short Summary: Since the demographics of the Twitter users and the […]
Social Media and Automated Sentiment Analysis Social Media monitoring and analysis has become increasingly popular since the Web 2.0 because it provides an easy and effective way to directly measure the effect of a campaign. This can be done by KPI’s like the number of followers, likes, shares, comments, comments-per-post. Besides these hard and easy-to-measure KPI’s […]