pytreebank
所属分类：数据可视化
开发工具：Python
文件大小：145KB
下载次数：0
上传日期：2020-02-18 06:05:24
上 传 者：
sh-1993
说明： Python中的斯坦福情感树库加载程序
(Stanford Sentiment Treebank loader in Python)
文件列表:
.travis.yml (353, 2020-02-18)
LICENSE.md (1082, 2020-02-18)
MANIFEST.in (89, 2020-02-18)
examples (0, 2020-02-18)
examples\scrape.py (410, 2020-02-18)
pytreebank (0, 2020-02-18)
pytreebank\__init__.py (177, 2020-02-18)
pytreebank\box_office.py (1830, 2020-02-18)
pytreebank\download.py (2048, 2020-02-18)
pytreebank\javascript (0, 2020-02-18)
pytreebank\javascript\__init__.py (1706, 2020-02-18)
pytreebank\javascript\c3.min.js (6408, 2020-02-18)
pytreebank\javascript\d3.min.js (42799, 2020-02-18)
pytreebank\javascript\sentree.min.js (23966, 2020-02-18)
pytreebank\javascript\sentrees.min.js (7118, 2020-02-18)
pytreebank\javascript\tree_visualization.css (3904, 2020-02-18)
pytreebank\javascript\tree_visualization.js (456, 2020-02-18)
pytreebank\labeled_trees.py (6676, 2020-02-18)
pytreebank\parse.py (6305, 2020-02-18)
pytreebank\treelstm.py (2983, 2020-02-18)
pytreebank\utils.py (1988, 2020-02-18)
release.sh (191, 2020-02-18)
setup.py (1070, 2020-02-18)
tests (0, 2020-02-18)
tests\test_download.zip (1042, 2020-02-18)
tests\test_load.py (3640, 2020-02-18)
visualization_example.png (129980, 2020-02-18)
SST Utils
---------
Utilities for downloading, importing, and visualizing the [Stanford Sentiment Treebank](http://nlp.stanford.edu/sentiment/treebank.html), a dataset capturing fine-grained sentiment over movie reviews.
See examples below for usage. Tested in Python `3.4.3` and `2.7.12`.
Javascript code by Jason Chuang and Stanford NLP modified and taken from [Stanford NLP Sentiment Analysis demo](http://nlp.stanford.edu:8080/sentiment/rntnDemo.html).
[](https://badge.fury.io/py/pytreebank)
[](https://travis-ci.org/JonathanRaiman/pytreebank)
[](LICENSE.md)
### Visualization
Allows for visualization using Jason Chuang's Javascript and CSS within an IPython notebook:
```python
import pytreebank
# load the sentiment treebank corpus in the parenthesis format,
# e.g. "(4 (2 very ) (3 good))"
dataset = pytreebank.load_sst()
# add Javascript and CSS to the Ipython notebook
pytreebank.LabeledTree.inject_visualization_javascript()
# select and example to visualize
example = dataset["train"][0]
# display it in the page
example.display()
```
### Lines and Labels
To use the corpus to output spans from the different trees you can call the `to_labeled_lines` and `to_lines` method of a `LabeledTree`. The first returned sentence in those lists is always the root sentence:
```python
import pytreebank
dataset = pytreebank.load_sst()
example = dataset["train"][0]
# extract spans from the tree.
for label, sentence in example.to_labeled_lines():
print("%s has sentiment label %s" % (
sentence,
["very negative", "negative", "neutral", "positive", "very positive"][label]
))
```
### Download/Loading control:
Change the save/load directory by passing a path (this will look for
`train.txt`, `dev.txt` and `test.txt` files under the directory).
```
dataset = pytreebank.load_sst("/path/to/sentiment/")
```
To just load a single dataset file:
```
train_data = pytreebank.import_tree_corpus("/path/to/sentiment/train.txt")
```
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