Topic stability in topic models

This question is cross-posted on CrossValidated. However, after stumbling on this site, I speculated that the community here might be better equipped to answer my question.

I am working on a project where I want to extract some information about the content of a series of open-ended essays. In this particular project, 148 people wrote essays about a hypothetical student organization as part of a larger experiment. Although in my field (social psychology), the typical way to analyze these data would be to code the essays by hand, I'd like to do this quantitatively, since hand-coding is both labor-intensive and a bit too subjective for my taste.

During my investigations about ways to quantitatively analyze free response data, I stumbled upon topic modeling. Since topic modeling appears to use a term-document matrix to extract groups of words supposedly generated by the latent topics in a corpus, it appears to be just the tool I was looking for. Unfortunately, when I've applied topic modeling to my data, I've discovered two issues:

1. The topics uncovered by topic modelling are sometimes hard to interpret
2. When I re-fit my topic models with a different random seed, the topics seem to change dramatically

Issue 2 in particular concerns me. Therefore, I have a two related questions:

1. Is there anything I can do in the LDA procedure to optimize my model fit procedure for interpretability and stability? Personally, I don't care as much about finding the model with the lowest perplexity and / or best model fit -- I mainly want to use this procedure to help me understand and characterize what the participants in this study wrote in their essays. However, I certainly do not want my results to be an artifact of the random seed!
2. Related to the above question, are there any standards for how much data you need to do an LDA? Most of the papers I've seen that have used this method analyze large corpora (e.g., an archive of all Science papers from the past 20 years), but, since I'm using experimental data, my corpus of documents is much smaller.

I have posted the essay data here for anyone who wants to get his or her hands dirty, and I have pasted the R code I'm using below.

require(tm)
require(topicmodels)

# Create a corpus from the essay 
c <- Corpus(DataframeSource(essays))
inspect(c)

# Remove punctuation and put the words in lower case
c <- tm_map(c, removePunctuation)
c <- tm_map(c, tolower)

# Create a DocumentTermMatrix.  The stopwords are the LIWC function word categories
# I have a copy of the LIWC dictionary, but if you want to do a similar analysis,
# use the default stop words in tm
dtm <- DocumentTermMatrix(c, control = list(stopwords = 
  c(dict$funct, dict$pronoun, dict$ppron, dict$i, dict$we, dict$you, dict$shehe, 
        dict$they, dict$inpers, dict$article, dict$aux)))

# Term frequency inverse-document frequency to select the desired words
term_tfidf <- tapply(dtm$v/rowSums(as.matrix(dtm))[dtm$i], dtm$j, mean) * log2(nDocs(dtm)/colSums(as.matrix(dtm)))
summary(term_tfidf)

dtm <- dtm[, term_tfidf >= 0.04]

lda <- LDA(dtm, k = 5, seed = 532)
perplexity(lda)
(terms <- terms(lda, 10))
(topics <- topics(lda))