Why does linear SVM perform better than deep learning techniques on my problem? I am doing a multiclass text classification problem. My data consists of tweets. I tried many variations of deep learning models (ex: LSTM, GRU and pre-trained word embeddings) and I also tried linear SVM. I applied 2-gram TF-IDF to the data before I feed it to the SVM.
The results are very similar but the linear SVM with 2-gram TF-IDF gives better results than the best performing deep learning model.
The accuracy of linear SVM with 2-gram TF-IDF is .538
The accuracy of the best performing deep learning model is .528
My questions are:
Isn't deep learning supposed to surpass machine learning models on such complex task like text classification?
Why does the machine learning model give better results?
Should I try harder to improve the deep learning model to surpass the machine learning model's accuracy?
 A: In general there is no single algorithm that is superior to all others at solving all problems, which is why so many classification algorithms exist.  Some models/algorithms excel at certain problems, and we often don't know which will do best at a particular task until testing them.

*

*Isn't deep learning supposed to surpass machine learning models on
such complex task like text classification?

To my knowledge, this has never been shown to be true for all classes of problems.  What constitutes deep learning is not well defined, but even within the broad classification of deep learning, some models perform better at certain tasks than others.


*Why does the machine
learning model give better results?

Impossible to say for sure, but SVMs are powerful models.  It could be your data preparation and feature selection favor SVMs in some way.


*Should I try harder to improve
the deep learning model to surpass the machine learning model's
accuracy?

I don't see why you would want a certain type of model to succeed over other types. I wouldn't overlook the importance of feature engineering in model performance; it's often more important than model selection.
