sead | Decompilation of sead : the standard C++ library | Reverse Engineering library

As @sergey-bushmanov's comment notes, dense word embeddings (as from word2vec or similar algorithms) may help.

They will convert words to dense high-dimensional vectors, where words with similar meanings/usages are close to each other. And even: certain directions in space will often be roughly associated with the kinds of relationships between words.

So, word-vectors trained on sufficiently-representative (large and varied) text will place the vectors for '4G' and 'mobile' somewhat near each other, and then if your sentence-representations are bootstrapped from word-vectors, that may help your clustering.

One quick way to use individual word-vectors to model sentences is to use the average of all a sentence's word-vectors as the sentence vector. That's too simple to model many shades of meaning (especially those that come from grammar and word-order), but often works as a good baseline, especially for matters of broad topicality.

Another calculation, "Word Mover's Distance", treats sentences as sets-of-word-vectors (without averaging them), and can do sentence-to-sentence distance calculations that work better than simple averages – but become very expensive to calculate for longer sentences.

The last problem you describe, saving multiple posts per user, is because you are trying to use the save method to save multiple items. Instead you should be using the saveMany method when you have an array or collection.

If there is always the v in the strings, you should be able to use: (?<=,)(v[^:]+)