Try before doing LDA look at the data - like doing TF, IDF and TFIDF analysis to identify such words which happen in all subject. If You have some taxonomy of Your product definition - consider using it. In my case it was really helpful.
I experimented with LDA topic modeling for recommendation systems purposes. I've few runs for offers in our marketplace service. It is in some aspect similar to Your problem with products and a list of keywords. But in our case, we do not have a product definition and an offer description is created by a seller. So You can imagine what tokens soup You will get :-)
The key point for me wast to analyze TF, IDF and TFIDF inside categories where I've tested LDA. E.g. for Toys category (http://allegro.pl/zabawki-11818?ref=simplified-category-tree) ~ .5M items, LDA based only on its names results in:
And the rest looks almost the same. Before running LDA I just did basic text preprocessing - our dictionary based item-name stop words removal and text tokenization (but without steaming). Even giving more terms for topic descriptions results doesn't look promising. Then, after exporting word frequencies with its offer categories by looking at data and plots I've decided to remove terms with TF-IDF above some threshold. Yes, above - I've used the calculation provided by Spark 1.3.1 implementation (HasingTF + IDF from mllib, no ml). After doing this I received:
Where results started to differ from each other. Taking like last time only 10-terms for every subject description. Still, it's not perfect but better.
So, for me doing the experiments which few categories, the way to overcome problem was to remove it based on TF-IDF value. But, for every category the threshold was calculated separately. Mainly: mean(tf-idf) + 3*sd(tf-idf).
I know that Idf multiplication factor in Tf-Idf should resolve this problem by itself and the term should be punished for occurring in every document. But, using Spark implementation (idf = log((m + 1) / (d(t) + 1))) and our data it was simpler during a quick and dirty experiment to filter it that way.
When I find few sec. I will get back on this and place results with code and online to share this.