Tools and Techniques for Analyzing German Automotive Discussion Forum Posts I work for a German online disussion forum around all things automotive, a bit like a “StackOverflow for cars”, if you will.

We would like to train a model using TensorFlow with our high quality content, to be able to evaluate the quality of new content our users post on our platform.

Our ultimate goal is to be able to put a link to the best answer to a question on our discussion forums. 

We (two backend Java developers and myself, a JavaScript frontend web developer) are very new to the field of data science and machine learning, currently going through the tutorials from Google and trying to figure out where to start.

Which tools and technologies would you recommend to use for this project?

How can we train a model to suit our needs?

Are there any tutorials that demonstrate how to train a model to use German text as input?
You could go with different approaches:

Lemme point few,


You could just extract the keywords or tokenize out of the query using libraries like spacy or nltk, both support german languages. Then go for like page ranking approach, based on query optimization: Uber.
You could go for Attention based seq2seq model, where you feed the inputs as questions and the answers as output. More like how you train chatbots and language translation models. This is popularly known as Neural Machine translation. Tensorflow has an open source implementation. nmt


The second one is feasible because it has tonnes of examples out there. But try to use spacy and nltk for tokenizing according to German language and also try to use German word embeddings: example, which contains pre-trained weights on huge corpora representing each German word.

Hope this helps.