Which classifier to use? - machine-learning

Question

I have a course project that I need to finish. I'm using Weka 3.8 and I need to classify text. The result needs to be as accurate as possible. We received a train and a test .arff file. We need to train it with the train file of course, and then let it classify the test file. The professor uploaded a 100% accurate classification of the test file. We need to upload our own results and than the system compares the two files. For now I've been using a FilteredClassifier composed of SMO and StringToWordVector with Snowball stremmer, but I can't get a better accuracy than 65.9% for some reason (this is not the split accuracy, but the one I get when the system compares my results to the 100% accurate one). I can't figure out why.
The train.arff file:
#relation train
#attribute index numeric
#attribute ingredients string
#attribute cuisine {greek,southern_us,filipino,indian,jamaican,spanish,italian,mexican,chinese,british,thai,vietnamese,cajun_creole,brazilian,french,japanese,irish,korean,moroccan,russian}
#data
0,'romaine lettuce;black olives;grape tomatoes;garlic;pepper;purple onion;seasoning;garbanzo beans;feta cheese crumbles',greek
1,'plain flour;ground pepper;salt;tomatoes;ground black pepper;thyme;eggs;green tomatoes;yellow corn meal;milk;vegetable oil',southern_us
2,'eggs;pepper;salt;mayonaise;cooking oil;green chilies;grilled chicken breasts;garlic powder;yellow onion;soy sauce;butter;chicken livers',filipino
3,'water;vegetable oil;wheat;salt',indian
...
and 4995 more lines like these.
The test.arff is similar to this:
#relation test
#attribute index numeric
#attribute ingredients string
#attribute cuisine {greek,southern_us,filipino,indian,jamaican,spanish,italian,mexican,chinese,british,thai,vietnamese,cajun_creole,brazilian,french,
japanese,irish,korean,moroccan,russian}
#data
0,'white vinegar;sesame seeds;english cucumber;sugar;extract;Korean chile flakes;shallots;garlic cloves;pepper;salt',?
1,'eggplant;fresh parsley;white vinegar;salt;extra-virgin olive oil;onions;tomatoes;feta cheese crumbles',?
... and 4337 more lines, like these.
This is my weka configuration:
He told us that there are some instances when in the .arff file some ingredients amongst the #data are seperated with ',' by accident and that there are words that occur frequently and that those might not help much. I don't know if this is important or not. Is there any way I could improve the classification accuracy? Am I even using the right classifier for the job? Thanks in advance!