Natural Language Processing of German texts - Part 2: Using LSTM neural-networks to predict ratings

Using a unique German data set containing ratings and comments on doctors, we build a Binary Text Classifier. In part 1 we've introduced a complete machine learning work flow that predicts ratings from comments. In this second part, we improve on our baseline by implementing a LSTM neural network model and using FastText embeddings. Using Keras for feature creation and prediction, we improve on the ability to understand comment sentiments.

more ...

Natural Language Processing of German texts - Part 1: Using machine-learning to predict ratings

Using a unique German data set containing ratings and comments on doctors, we build a Binary Text Classifier. To do so, we implement a complete machine learning work flow that predicts ratings from comments. In this first part, we start with basic methods. We go through text pre processing, feature creation (TF-IDF), classification and model optimization. Finally, we evaluate our model's ability to predict the sentiment of comments.

more ...

A match made in heaven: Tinder and Statistics — Insights from an unique Dataset of swiping

Tinder is hugely popular and offers fascinating data on online dating. Still, there are barely any analysis of tinder data. In this endeavor we change that, by analyzing an unique dataset of tinder profiles. The data contains thousands of female and male profiles collected by swiping as a hetero- and homosexual male. Using descriptive statistics, visualization and natural language processing we uncover exciting patterns.

more ...