Textnets

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* `ProjectedTextnet.plot` now takes an argument, ``alpha``, that allows for
pruning the graph in order to visualize its "backbone." This is useful when
working with hairball graphs, which is common when creating textnets. Right
now, it uses Serrano et al.'s disparity filter. That means that edges with an
alpha value greater than the one specified are discarded, so lower values
mean more extreme pruning.
* Language models can now be specified using a short ISO language code.
* Bipartite networks can now be plotted using a layered layout (by Kozo
Sugiyama). Simply pass ``sugiyama_layout=True`` to `Textnet.plot`.
* Incremental improvements to documentation.

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* Documented `TextnetBase` methods to output lists of nodes ranked by various
centrality measures: `top_betweenness` and several more.
* Added `top_cluster_nodes` to output list of top nodes per cluster found via
community detection. This is useful when trying to interpret such clusters as
themes/topics (in the projected word-to-word graph) or as groupings (in the
document-to-document graph).
* Small additions to documentation.

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Lots of changes, some of them breaking, but overall just providing nicer
abstractions over the underlying pandas and igraph stuff.

* Introduced `TextnetBase` and `ProjectedTextnet` classes, and made `Textnet` a
descendant of the former.
* Improved code modularity to make it easier to add features.
* `Corpus` is now based on a Series rather than a DataFrame.
* Added methods for creating an instance of `Corpus`: `from_df`, `from_csv`,
`from_sql`.
* Expanded and improved documentation.
* Added bibliography to documentation using a Sphinx bibtex plugin.
* A first contributor!

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* Small change to *finally* get automatic deployments to PyPI to work.

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* Overall improvements to documentation.
* Added ``label_edges`` argument to `Textnet.plot`.

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* Integrated self-contained example that can be downloaded as Jupyter notebook
into tutorial.
* Still trying to get automatic deployments to PyPI working.