Applying convolutional neural network over text documents using 1-D tf-idf feature vectors
In TFIDF representation "neighborhood" is not interpretable. The 1st number in a document vector has just as much relation to the 2nd and to the 100th.
While in case of a standard time series or sequence analysis "neighborhood" is interpretable.
On the other hand, if you could re-order the TFIDF representation in a way, that makes sense from the analysis perspective (eg: ordering based on the "sentiment" of the word), maybe 1D CNN would show some good results.