The classifier aglo is more or less un-changed since almost 10 years. I think a good reason for that could be, for example, NB, SVM have been able to achieve relatively high accuracy since long time back, provided with optimal/sub-optimal parameters.
While at the same time, a good approach to bump up the accuracy of overall text classification result is by data/corpus preparation, including stopwords, POS,TF-IDF etc, based on my experience.
Saw a good post on accuracy of text classification, echoing this:
6 Practices to enhance the performance of a Text Classification Model
libsvm is the first supervised machine learning library i have used extensively, more than 10 years back.
It was pretty awesome that time back, seeing a 78% text classification accuracy of against more than 100,000 hotel reviews, i have crawled from ctrip.com.
While, at version 3, they are able to achieve 96.875% for text classification results now, as: