Parameters
		----------
		terms : list or None
			If terms is list, make these the seed terms for the topoics
			If none, use the first 30 terms in get_scaled_f_scores_vs_background
		num_terms_per_topic : int, default 10
			Use this many terms per topic
		scorer : TermScorer
			Implements get_scores, default is RankDifferce, which tends to work best

		Returns
		-------
		dict: {term: [term1, ...], ...}

def get_topics_from_terms(self,
	                          terms=None,
	                          num_terms_per_topic=10,
	                          scorer=RankDifference()):
		'''
		Parameters
		----------
		terms : list or None
			If terms is list, make these the seed terms for the topoics
			If none, use the first 30 terms in get_scaled_f_scores_vs_background
		num_terms_per_topic : int, default 10
			Use this many terms per topic
		scorer : TermScorer
			Implements get_scores, default is RankDifferce, which tends to work best

		Returns
		-------
		dict: {term: [term1, ...], ...}
		'''
		topic_model = {}

		if terms is None:
			terms = self.corpus.get_scaled_f_scores_vs_background().index[:30]

		for term in terms:
			termidx = self.termidxstore.getidxstrict(term)
			labels = self.sentX[:, termidx].astype(bool).todense().A1
			poscnts = self.sentX[labels, :].astype(bool).sum(axis=0).A1
			negcnts = self.sentX[~labels, :].astype(bool).sum(axis=0).A1
			scores = scorer.get_scores(poscnts, negcnts)
			topic_model[term] = [self.termidxstore.getval(i) for i in
			                     np.argsort(-scores)[:num_terms_per_topic]]
		return topic_model