Two More Text Analysis Tools from HDG

1. Coh-Metrix

Has anyone here experimented with this tool
(http://cohmetrix.memphis.edu/cohmetrixpr/)? It is described as follows:

Coh-Metrix is a computational tool that produces indices of the
linguistic and discourse representations of a text. These values can
be used in many different ways to investigate the cohesion of the
explicit text and the coherence of the mental representation of the
text. Our definition of cohesion consists of characteristics of the
explicit text that play some role in helping the reader mentally
connect ideas in the text (Graesser, McNamara, & Louwerse, 2003). The
definition of coherence is the subject of much debate. Theoretically,
the coherence of a text is defined by the interaction between
linguistic representations and knowledge representations. When we put
the spotlight on the text, however, coherence can be defined as
characteristics of the text (i.e., aspects of cohesion) that are
likely to contribute to the coherence of the mental representation.
Coh-Metrix provides indices of such cohesion characteristics.
http://141.225.213.52/CohMetrixWeb2/HelpFile2.htm

The tool has recently been used to analyse (surprise, surprise) the
language of the candidates in the US Presidential election
(http://wordwatchers.wordpress.com/). It would be particularly
interesting if this had been tried on more demanding text or with more
demanding questions.

2. Linguistic Inquiry and Word Count (LIWC)

LIWC (http://liwc.net/liwcdescription.php) seems at first glance to be
methodologically much simpler. As far as I can tell from a quick
reading, it computes scores based on occurrences of target words
pre-defined to belong to different affective categories, plus scores
based on counts of sentence length and the like. It depends centrally on
a dictionary of 4500 words:

The LIWC2007 Dictionary is the heart of the text analysis strategy.
The default LIWC2007 Dictionary is composed of almost 4,500 words and
word stems. Each word or word stem defines one or more word
categories or subdictionaries. For example, the word cried is part of
five word categories: sadness, negative emotion, overall affect,
verb, and past tense verb. Hence, if it is found in the target text,
each of these five subdictionary scale scores will be incremented. As
in this example, many of the LIWC2007 categories are arranged
hierarchically. All anger words, by definition, will be categorized
as negative emotion and overall emotion words. Note too that word
stems can be captured by the LIWC2007 system. For example, the
LIWC2007 Dictionary includes the stem hungr* which allows for any
target word that matches the first five letters to be counted as an
ingestion word (including hungry, hungrier, hungriest). The asterisk,
then, denotes the acceptance of all letters, hyphens, or numbers
following its appearance.

Not being up-to-date with research in this area (psycholinguistics?) I
don’t know how this tool compares with affective research via
text-analysis that has been going on for decades. Perhaps someone here
can say. How reliable is such research?