Textnets

This version adds a `color_clusters` option to `Textnet` plotting methods. This colors nodes according to their partition using a bespoke color palette.

* The `Corpus` class now handles missing data (13).
* Support for more corpus languages. If no statistical language model is available, `Corpus` tries to use a basic ("blank") model.
* Improved documentation around dependencies and language support.
* Added tests.

- `Textnet.plot` and `ProjectedTextnet.plot` now accept arguments to selectively suppress node or edge labels. `node_label_filter` and `edge_label_filter` optionally take a function that is mapped to the iterator of nodes and edges. Only nodes or edges for which the function returns `True` are displayed in the plot. For example, `node_label_filter=lambda n: n.degree() > 2` ensures that only nodes with a degree greater than 2 are displayed.
- Noun phrases created by `Corpus.noun_phrases()` can now be normalized (lemmatized) by passing `normalize=True` (defaults to `False`).
- `Corpus` now has a useful string representation.
- Documentation updates, particularly to show the label filter functionality.

- Python 3.7 compatibility is here, closing issue 8.
- New `circular_layout` option for `Textnet.plot`. This is based "[Tidier Drawings](https://www.reingold.co/graph-drawing.shtml)" and looks very nice for some bipartite graphs.
- String representation of `Textnet` instances now gives helpful information.
- Updated documentation to note changed Python version requirement.

- `ProjectedTextnet.plot` now takes an argument, `alpha`, that allows for pruning the graph in order to visualize the "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 (Sugiyama). Simply pass `sugiyama_layout=True` to `Textnet.plot`.
- Incremental improvements to documentation.

* 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.