Sentiment Analysis for English, German, French and Italian Texts
We have published a new version of our REST Service for Sentiment Analysis. The machine learning model predicts whether the sentiment of a text is positive or negative. The model can predict sentiment for English, German, French and Italian texts. In conjunction with our Language Detection Service, the language of a text can be determined prior to the sentiment analysis. Continue reading “Multilingual Sentiment Analysis”
Determining the Language of a Text
Determining the language in which a text was written is one of the most important tasks in the automated processing of documents. A classification, sentiment, fake news or spam analysis can not be made without knowing in which language the text, tweet or review was written. Continue reading “Language Detection for English, German, French, Italian, Spanish and Romansh”
Positive or Negative Sentiment?
We have published a new version of our REST Service for Sentiment Analysis. The machine learning model predicts whether the sentiment of a text is positive or negative. The model can predict sentiment for English, German and French texts. In conjunction with our Language Detection Service, the language of a text can be determined prior to the sentiment analysis. Continue reading “Sentiment Analysis of English, German and French Texts”
Insights Into A Movie Review
Our Sentiment Analysis Model has achieved an accuracy of 89.3% in classifying reviews from the Internet Movie Database (IMDB). That’s good, amazing – the reviews are often tricky to read. But 1 review of 10 is still misclassified. Why is that?
In the series ML Insights we investigate misclassifications in more detail. We take wrongly classified examples and ask: Continue reading “ML Insights – Sentiment Analysis of a Movie Review”
In this tutorial, we’ll show you what it takes to be practical with Machine Learning. First, we’ll show you how to build a working environment for machine learning. Then you will immediately start working with your first machine learning model.
Continue reading “Machine Learning Tutorial for Beginners”
You have been classified countless times at school! The most popular application of text classification in machine learning is sentiment analysis, where texts are given an emotional label such as ‘positive’ or ‘negative’. However, there are many other text classification applications that can be realized today with machine learning. In the following, these five applications of text classification will be discussed: Continue reading “Applications of Text Classification in Machine Learning”
The progress in machine learning is amazing today. And machines are getting more amazing every day. They clearly beat humans in games like chess and Go. They translate texts of ever better quality from one language to another. They can recognize and describe contents of audio, picture or video documents. Even in art, a domain that until recently was reserved for humans, they are impressive. Machine Learning has taken giant steps lately.
But how far do we understand this progress? And how do we deal with the ever-improving machine learning based computer programs in our everyday life? These questions arise both from the perspective of the user and the provider of such ‘intelligent’ services. Learning machine learning will become a central task of the individual and of society in the future. Continue reading “Learning Machine Learning”