Neural Network parse string data?
Using a neural network for prediction on natural language data can be a tricky task, but there are tried and true methods for making it possible.  

In the Natural Language Processing (NLP) field, text is often represented using the bag of words model.  In other words, you have a vector of length n, where n is the number of words in your vocabulary, and each word corresponds to an element in the vector.  In order to convert text to numeric data, you simply count the number of occurrences of each word and place that value at the index of the vector that corresponds to the word. Wikipedia does an excellent job of describing this conversion process.  Because the length of the vector is fixed, its difficult to deal with new words that don't map to an index, but there are ways to help mitigate this problem (lookup feature hashing).  

This method of representation has many disadvantages -- it does not preserve the relationship between adjacent words, and results in very sparse vectors.  Looking at n-grams helps to fix the problem of preserving word relationships, but for now let's focus on the second problem, sparsity.  

It's difficult to deal directly with these sparse vectors (many linear algebra libraries do a poor job of handling sparse inputs), so often the next step is dimensionality reduction. For that we can refer to the field of topic modeling:  Techniques like Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) allow the compression of these sparse vectors into dense vectors by representing a document as a combination of topics.  You can fix the number of topics used, and in doing so fix the size of the output vector producted by LDA or LSA. This dimensionality reduction process drastically reduces the size of the input vector while attempting to lose a minimal amount of information.  

Finally, after all of these conversions, you can feed the outputs of the topic modeling process into the inputs of your neural network.