I have extracted ~550 video scripts (subtitles) from 11 free courses on the Coursera platform. I have pre-processed them in terms of punctuation removal, stop words removal, tokenization, stemming and lemmatization. Now, I've been advised that for my task I can attempt to use a simple Bag of Words. However I am not sure how exactly would that help me towards classifying my text into one out of six categories. The categories are related to the intent a video material was created with and more precisely, which part explains a concept, which part discusses an example, which part gives practical advice etc. Below are my categories:
ConceptDescription-> Explanation of the Main concept(s)
ConceptMention-> Mentioning of a concept, related to the main concept
Methodology / Technique-> To achieve something, what should one do
Summary-> Summary of the discussed material or of the whole course
Application-> Practical advise for the concept
Example-> Concept example
By manually reading several files from 3 of the courses, I created a dictionary, containing spoken language words, that may help me identify which class a specific sentence/paragraph falls into. However I do NOT have a train dataset for a classifier. So my idea was to use that dictionary to label my data, e.g. sentence 1 as
Summary, sentence 4 as
ConceptDescription and sentence 12 as
Example and then marking sentences 2 and 3 the same as 1, sentence 5-11 like sentence 4 etc.
My question is, is this idea too lame? And is there a way to create at least an average quality training dataset in a way that is not manual? Or if manual check is the only option, is there an option where I would need to do manual labeling on only a small fraction of the files, say 50 out of 550 and classification would still produce average to good result? I don't aim at perfect result, but I aim at something less time-consuming due to limited time.
I also played with tf-idf which outputs terms, but of course, not really what I need, so that was a bit random.
Thanks in advance for your help. Any specific ideas and algorithms would be very welcome.