Automatic language identification of short texts

The world is growing more connected through the use of online communication, exposing software and humans to all the world's languages. While devices are able to understand and share the raw data between themselves and with humans, the information itself is not expressed in a monolithic format. This causes issues both in the human to computer interaction and human to human communication. Automatic language identification (LID) is a field within artificial intelligence and natural language processing that strives to solve a part of these issues by identifying languages from text, sign language and speech. One of the challenges is to identify the short pieces of text that can be found online, such as messages, comments and posts on social media. This is due to the small amount of information they carry. The goal of this thesis has been to build a machine learning model that can identify the language for these short pieces of text. A long short-term memory (LSTM) machine learning model was built and benchmarked towards Facebook's fastText model. The results show how the LSTM model reached an accuracy of around 95% and the fastText model used as comparison reached an accuracy of 97%. The LSTM model struggled more when identifying texts shorter than 50 characters than with longer text. The classification performance of the LSTM model was also relatively poor in cases where languages were similar, like Croatian and Serbian. Both the LSTM model and the fastText model reached accuracy's above 94% which can be considered high, depending on how it is evaluated. There are however many improvements and possible future work to be considered; looking further into texts shorter than 50 characters, evaluating the model's softmax output vector values and how to handle similar languages