I'm new to Topic Models, Classification, etc… now I'm already a while doing a project and read a lot of research papers. My dataset consists out of short messages that are human-labeled. This is what I have come up with so far:
- Since my data is short, I read about Latent Dirichlet Allocation (and all it's variants) that is useful to detect latent words in a document.
- Based on this I found a Java implementation of JGibbLDA but since my data is labeled, there is an improvement of this called JGibbLabeledLDA
- In most of the research papers, I read good reviews about Weka so I messed around with this on my dataset
- However, again, my dataset is labeled and therefore I found an extension of Weka called Meka that had implementations for Multi-labeled data
- Reading about multi-labeled data, I know most used approaches such as one-vs-all and chain classifiers...
Now the reason me being here is because I hope to have an answer to following questions:
- Is LDA a good approach for my problem?
- Should LDA be used together with a classifier (NB, SVM, Binary Relevance, Logistic Regression, …) or is LDA 'enough' to function as a classifier/estimator for new, unseen data?
- How do I need to interpret the output coming from JGibbLDA / JGibbLabeledLDA. How do I get from these files to something which tells me what words/labels are assigned to the WHOLE message (not just to each word)
- How can I use Weka/Meka do get to what I want in previous question (in case LDA is not what I'm looking for)
I hope someone, or more than one person, can help me figure out how I need to do this. The general idea of all is not the issue here, I just don't know how to go from literature to practice. Most of the papers don't give enough description of how they perform their experiments OR are too technical for my background about the topics.