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?