Assignment 5 or “The Importance of Being Gephi”

As with Assignment 3, I utilized the CHILDES corpus of child speech in order to create the data-sets used in my visualizations displayed below. As before, the data I pooled from had already been separated into categories based off of the syntactical level of the speaker. The specific pool I utilized was from children of the lowest level of capability (and thus, age as well). Getting the extra information required for my visualizations consisted of parsing through the original data and organizing it into sets based off of vocab use, age, and gender. Due to the unorganized structure of the CHILDES corpus (there being only loose structural guidelines for it, with few contributors actually making use of all the data tags supplied) getting this information together didn’t prove easy, but ended up being quite fruitful.

Age Vocab Visualization

This first visualization was created utilizing the Palladio platform and resulted in an immediately interesting (if complicated) visualization of age versus vocabulary use. As can be observed above, the largest number of utterances came from those who were 1 or 2 years old (as represented by the larger nodes on either side of the central structure). Though there are a good amount of words connecting these two age sets, as well as to the other ages at the top of the central section, the truly interesting thing about this data depiction is just how many words are not connected by common usage among age groups (as shown by the large clusters of nodes on the proximity of the visualization). But, due to the fact that Palladio has trouble with this much data, as well as the sheer amount of it, trying to inspect it closely proves difficult, if not impossible. This is still a much more interesting visualization than the ones that I was able to create using Google Fusion Tables, which were structurally unclear and muddied, but nonetheless this picture still leaves something wanting.

Gender Vocab Visualization

This second visualization, depicting the correlation between spoken vocabulary and gender, is immediately more interesting than the previous one due to its clarity in relationship between each subsection. The structure of the visual is as intriguing as it is logical. The three large nodes with a majority of connections represent Male, Female, and Unspecified genders. Each are connected to each other as well as having their own collection of unique words not shared with the others. What is interesting about this is just how many more unique words the Female gender has than the others. But, even though there are a lot clearer conclusions to be drawn from this visual, it’s still lacking in the way of interaction and close inspection.

This is where Gephi comes in.

 

age visual gephi

The above picture is a static image of a visualization of the same age versus spoken word data used in the first Palladio visual. While it is readily apparent just from this still alone how much cleaner the Gephi visual is to its Palladio counterpart, I suggest that you follow this link (Age vs Word Visualization) that will take you to an interactive version of this visual to see the true power of a Gephi visualization.

This interactive visualization was created by first making the graph of connections in Gephi, and then loading into the Gexf-JS Web Viewer program created specifically for viewing interactive Gephi visualizations online. I originally tried using the Sigma JS exporter instead, but the platform was unable to handle the size of my data set. Gexf-JS gives many of the same interactive benefits of Sigma JS, while also being a bit cleaner in its search interface as well as its responsiveness and drawing capabilities.

This interactivity gives a user the ability to not only see overall connections and structure of a network, but also the individual connections between items in the network itself. Nodes are colored based off of the connection grouping that they are most affiliated with. For instance, the word “write” was most used by those of 1 year of age and is colored blue, while “next” is green because it’s mostly connected to the 2 years old age group. From there, the visual is structured by the Fruchterman Reingold layout, meaning that nodes with more connections are centrally located and those along the perimeter are the least connected. As can be seen with the age visualization, this leads to some very interesting layouts which can tell a user a lot more about a set of data than Palladio was able to, especially in the case of a large data set that is as hard to read statically as this one. But, once you play around with the visual for a little bit, you can make some interesting conclusions about word use and overall connectivity than you might be able to make otherwise. As well, this is all for a visualization which is inherently muddied in structure. When you get something more structured, things get even more interesting.
gender visual gephi

This above picture shows a static version of the interactive visualization which can be found at the following link (Gender vs Word Visualization). In the visualization, red is associated with Female, blue Male, and purple is Unspecified genders. While this may be somewhat similar to the visualization made via Palladio, it’s ability to display the centrality of nodes, and even the overlapping connections of each gender group, are made much more readily apparent. We see a lot of overlap between Unspecified and female (which may suggest that this data may have been spoken by female children) as well as strong central connections between all groups. This layout makes this exploration much easier, as well as allowing for much clearer conclusions as well.

Overall, I would have to say that my visualizations with Gephi (especially after exported to a web interface allowing for interactivity) are far superior to those created using Palladio. While Palladio did a good job with showing the degree of node connections with the sets being studied, organizing the data so it centered around the subsections being measured, these structures didn’t allow for much exploration or even insight beyond the top-layer of general connectivity. The Gephi visuals not only are easier on the eyes, but also give the degree of each individual word once selected, as well as all connectivity and the relative orderings between each node. So while the general eigenvector may have been comparably similar, what each platform was able to say was drastically different.

This whole process, from data collection, to database construction, to platform testing, has shown just how important iterative design can be. While Gephi is definitely my platform of choice for my data, if I hadn’t done the work prior of testing the data on other platforms, not only would I have had nothing to compare it to, but I also wouldn’t have had as strong a grasp on my data itself. I believe Lima would have supported this process and even actively encouraged it. After all, it helped produce a visualization which not only looks good in its own right, showing some general conclusions about my data sets, but also allows for the user to go out and discover their own conclusions. Because, at the end of the day, visualizations mean nothing without context. So giving the viewer the keys to their own conclusions opens up a new realm of discoveries, even beyond what the original creator could have imagined.

 

 

Assignment 3: Networks and the Inter-Connectivity of Child Vocabulary

After my initial findings from assignment 2, I was curious to see what other conclusions could be drawn from my corpus of children’s speech. The most interesting finding from that assignment was the ability to plot the relative relation of individual word usage by both frequency and spatial relation. It became apparent from these findings, somewhat intuitively, that mapping the spoken utterances to the actual individuals who spoken them would be the next step in visualizing the data set. Instead of merely mapping the words with each other, determining the relation between words spoken and characteristics of the speaker is something which has the potential to lead to some interesting, and hopefully enlightening, conclusions.

Determining which aspects of the speakers to map to word-usage (and how exactly to do this) was initially a challenge, especially in converting the data into a csv data format. I contemplated whether or not individual word frequencies would be a useful metric for analysis, or if dividing up my given word data into sub-categories for various aspects of speech would prove more fruitful. As far as speaker characteristics, I decided that two of the most general (but also most insightful) factors would be individual age and individual age. After parsing back through my original data set in order to map this gender and age data, I realized that individual word categories might not be as informational as using a mapping of all word-utterances in relation to speaker characteristics instead. While breaking up the words into parts of speech or by noun types might have been interesting, seeing the connection between overall word-usage appeared to be indicative of a stronger visualization as a whole.

Age Vocab Visualization

This first visualization maps vocabulary usage to age of individual speaker. The highlighted nodes represent different ages while the remaining nodes represent the actual words uttered by individuals of the ages which connect them. This visualization is very interesting in mapping the intersecting nature of vocabulary and word-usage among different age groups. We see a large concentration of words branching off of the tow lower-most age nodes (representing the ages of  1 and 2), but also a large number of intersection between the two. As well, as the age goes up, the interconnectedness of vocabulary only grows, with higher age groups clustered together higher above the lower age groups. If this wasn’t so hard for Palladio to render on its own, I’d be very interested in increasing the data size with an increased vocabulary and number of age groups to see just how extensive this age-related connectivity really is.

Gender Vocab Visualization

My second visualization maps vocabulary usage to the recorded genders of the individual speakers. I find this visualization to be particularly interesting in how clearly it is able to convey the obvious differentiation between vocabularies of the various genders. While one might intuitively assume that essentially all, if not at least a majority, of vocabulary should be spread evenly between speakers of each gender, we can see that this doesn’t appear to be the case. The three recorded gender subsections (male, female, unknown) map together to have a good deal of intersection between them, but an even greater amount of bisection in unique vocabulary usage. From the network, we can analyze the varying ways in which individuals of different genders form vocabularies and where they overlap.

Both of these visualizations, though capable of spawning analysis and conclusions, are more representations than they are knowledge generators. This is largely due to the fact that despite the various lines denoting connections between nodes, the actual spatial relation between nodes doesn’t carry in meaning in itself. It is the connections themselves which have the meaning. Because of this, we are able to look upon these visualizations and see a particular mapping of information, but aren’t able to use the mappings themselves to discover some vastly different amount of information. The current arrangement of nodes and connections was done automatically by the Palladio system in order to better display the central nodes and more clearly represent the connections between each branching path. Nodes on opposite ends of the mapping are no more unrelated than the node unconnected in its immediate vicinity. To view networks we must not think in terms of place, but in terms of connection.

Assignment 2: Delving into the words of a child

My corpus is comprised of data collected and stored as a part of the Child Languages Data Exchange System database a part of the TalkBank system of collected speech transcriptions. The database is maintained by Professor Brian MacWhinney at Carnegie Mellon University since the 1990s, and has become one of, if not the largest single collection of spoken child utterances available. The data within the system dates as far back as the 1960s and is continually updated with additional transcriptions from more recent studies. This corpus was then analyzed using CHILDES’s open-source analysis software, CLAN, in order to divide the large pool of data into smaller subsets organized by Roger Brown’s Stages for Syntactical and Morphological Development. This model divides the different stages of a child’s syntactical speech progression into 5 stages representing the most basic child speech to more syntactically advanced sentence structures. One interesting thing to note here is that the data itself is not actually organized by the individual speaker’s age whatsoever, but merely by the various stages I have previously outlined. That being said, there are some general age mappings for Brown’s stages that happen to appear in the data present. For instance, simpler sentences are more likely spoken by younger children while more complex sentences are more likely spoken by older ones. These divisions have proven very interesting in visualization analysis for my accumulated corpora.

Child Utterance RelationsChild Utterance Relations             The above images display a scatter plot of my corpus’ word data created using the Voyant platform. The above visualization breaks down the 1000 most frequently used words from my corpus and then break them into clusters by relative usage (displayed via different colors). These different nodes are then placed on a plot in relation to each other based off of their relative use and connections within the text. This visualization is novel in its ability to display the interconnected nature of early speech sentence structure. All spoken utterances are cleanly related to their counterparts, branching off into three separate off-shoots from the main base of language. What surprises me the most about this visualization is how clean this relation is, and how geometric it is as well.

 

Word Cluster

family relation

The above visualization was created using the Jigsaw platform. The main take-away that this visualization presents immediately is the direct relation between words of urgency and between utterances of “mommy” or “daddy”. While this may appear obvious from a distance, seeing these rather dissimilar words that merely share the trait of urgency all having higher frequency of relations to utterances for parents is very interesting. As well, the sheer number of utterances of “mommy” compared to other types of names or actions is quite interesting to behold.

The obvious difference between the Voyant and Jigsaw platforms is in the way each handles the data that it processes. Voyant is more interested in word frequencies and relative originality of individual terms while Jigsaw is more focused on putting context into the words that it is given by dividing them into entities for analysis. Because of the nature of this context-based approach, Jigsaw isn’t very useful for large-scale text files that haven’t been properly parsed yet. For instance, Jigsaw is quite good at reading books or formal reports because of the way in which subjects are formatted and displayed within the texts. But for my corpora, I have a very large number of spoken utterances by children which aren’t always as syntactically literate as these. Because of this, I was tasked with defining my own set of entities based off of the components of my text that I wished to explore. Some examples of entities that I used are different pronoun forms, family members, and words of urgency. From there, I was able to make connections between these various groups using Jigsaw’s extensive document analysis and clustering tools. Voyant doesn’t allow you this much specific control. But, where Jigsaw succeeds in entity and core analysis, Voyant makes up for in large text analysis. Using Voyant I was able to make over-arching analytical conclusions about word usage which isn’t as clear when using Jigsaw. Both platforms are quite extensive in their offerings, as long as the data you are working with is tailored to what each platform provides.

The creation of this corpus, as well as the process of analyzing it with these two similar yet disparate platforms has yielded an interesting insight into what Clement was trying to get at in her piece on Analysis and Visualizations. On one hand, these images in front of me are displaying concrete information which was gathered from valid sources for analysis. Yet, all of this visualization is taking place in a completely virtual environment. None of it is physical, unless it were to be printed out or written down manually. This incongruity is interesting in the fact that it gives the researcher a reminder of the constraints of a virtual analysis process, while also appreciating that without the humanistic element to the analysis, no real conclusions could be drawn. We are simultaneously working as humanists and computer scientists in these moments, and are capable of making connections that neither could do alone.