All the visualization tools can be classified as related to qualitative, quantitative or mixed data. Most of the tools are focused on qualitative research, and can to certain extent be considered as free (although less functional) analogs of NVivo.

To start with, http://voyeurtools.org looked rather disappointing. Currently a lot of software include an option of creating word clouds, including NVivo, which is widely used in Carleton. Its corpus-related tools are also implemented better in other software. I did not find anything new or attractive in this tool. Compared to free tools, it is less functional compared to Wordseer already discussed at our previous classes. SimpleTCT also reminds NVivo a lot, although more by its functions related to text coding, and its functionality does not impress much either.

Graphviz offers a lot of good options but requires knowledge of its own special algorithmic language, and is for this reason not easy to use for beginners. The same is applicable to D3, a successor to Protovis, which has even richer options but requires knowledge of JavaScript. RForge may have rich options but its documentation is too raw and obscure -- I could not even understand its main features by reading the site.

Compared to these, ManyEyes and NodeXL look much more attractive, they are very well documented, their interfaces are easy to master for beginners and include quite a rich set of functions. They offers multiple options of building logical trees representing data in large databases (libraries, archives etc.) and are in fact good ffreeware analogs to Nvivo. If I had to pick up a tool for a purely qualitative research, I'd pick up NodeXL or ManyEyes.

ImagePlot stands out of the row, as it works not as much with texts as with multimedia and allows creating multimedia clouds (pictures etc.).

Finally, Mondrian is probably the only one that combines features of a qualitative research tool with several quantitative functions, which allows creating maps and graphs based on numeric data. This one deserves a more profound analysis; as I do not have any other comparable tools, I will just evaluate this one as "very good for the reason of absence of competition".

My perception of textual analysis tool has been largely influenced by my previous experience as a translator between several languages. Modern translation relies upon translation memory software, such as Trados, Transit, DejaVu etc., whose principles and interface have a lot in common with the textual analysis tools proposed for discussion. All of the tools in question split texts into logical segments, analyse it statistically, and some of them propose even broader options, such as research of collocations (frequently occurring combinations of words) and concordance (regular agreement between words, which is narrower than simple collocation).
Out of the tools analysed Wordseer seems to offer the largest choice of possibilities; although it may be just a bit poorer by its functionality compared to professional translation tools, it is very comprehensive and easy to understand for beginner users, and includes best visualisation options. On the second place I would put Kaleidoscope. I included into consideration some other text analysis tools not listed in the syllabus, such as TAPoR, TextARC, Textalyser, but they do not seem to surpass Wordseer. What's more, a lot of linguistic corpora, such as COCA, include user interface much resembling those tools.
So far, the weakest side of these tools is that they analyse text by words, not by morphemes. I would not mind if a program revealed a "pseudo-morphem", a combination of characters that occurs often in words and may be misinterpreted as a meaningful part (such as the final "all" in such words as "ball", "call" or "small") - eventually, further analysis of overlaps between those word chunks, their statistics and their collocations will grade them by the measure of their "morphemic legality": in the given case, "all" at the end of a word in no way correlates with its position in a sentence or agreement with other words, while the final "ing" does; hence, the first is not a morpheme and the second is.
However, as one can see from the reading suggested, text analysis tools are not purely linguistic tools and are designed for much broader audience than just linguists. Their task is to help comprehend texts, to get as much information as possible from them, to accelerate text analysis, and even to represent the data found as diagrams or charts. Or at least it is meant to do so. Clement et at. (2008) describe purely linguistic methods of text analysis, and so do Michel et al. (2011). What they describe is nothing else as the classical corpus linguistics, and I wonder if analysis can go any further. What these tools add to linguistic analysis is better visualization of results that makes them easily comprehensible. Indeed, when words are represented in different sizes and colours, it is easier to get an idea of their frequency in the text considered than looking at tables and charts. It is like a GUI of operating systems that gradually supplanted the command-line interface and made computer a common tool instead of a tool for geeks. Text analysis tools may in the same way be "simpler" than professional linguistic tools, but they make linguistic analysis affordable to non-experts.
Works cited.
Jean-Baptiste Michel, et al. "Quantitative Analysis of Culture Using Millions of Digitized Books," Science Vol. 331, 176 (14 January 2011).
Tanya Clement et al., "How Not to Read a Million Books". http://people.lis.illinois.edu/~unsworth/hownot2read.html.
Rockwell, Geoffrey. "What is Text Analysis, Really?," Literary and Linguistic Computing 18.2 (2003): 209-220.
The Historian's Macroscope: Big Digital History.
http://www.themacroscope.org/?page_id=113.
Lev Manovich, "Trending: The Promises and Challenges of Big Social Data," in Gold, ed. http://www.manovich.net/DOCS/Manovich_trending_paper.pdf