I need to classify MOOC video scripts into one of 5 classes which specify the intent of the sentence, e.g. explanation, example, summary etc., sentence-wise.

I have 11 courses extracted and selected equal ratio of files from every course based on the number of files I have for each of them. Say course 1 has 100 files and course 2 has 5 times less, then I selected data for labeling such that the labeled daya for course 1 will be 5 times more than the labeled data for course 2.

Due to lack of time, I wanted to label 1/5th of my data, which is about ~8800 sentences in 111 files. My question is.. can this be enough to predict the rest of the data, or approx ~29600 sentences more.. maybe if i slowly introduce it to the model, not at once.. or i don't know.. any ideas are welcome, since I am not too experiences. Also specific text classification algorithms you think work best, would be very helpful to mention too. I am open to trying several, as long as their implementation is not too much time-consuming. I will of course try the most common ones first such as Naive Bayes and SVM.

Oh also, another noob question - do I need to convert my text into vectors for some reason? Or will the classifier do it's own work based on the textual data?


closed as too broad by Toros91, Stephen Rauch, Kiritee Gak, Aditya, Sean Owen Jun 23 '18 at 20:47

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

  • $\begingroup$ This question is too broad; you can learn the basics of text classification with this tutorial: nltk.org/book/ch06.html $\endgroup$ – polm23 Jun 18 '18 at 6:15