Contents
clone git@github.com:kevincobain2000/jProcessing.git
0.2
- Sentiment Analysis of Japanese Text
In Python
>>>from jNlp.jTokenize import jTokenize
>>>input_sentence = u'私は彼を５日前、つまりこの前の金曜日に駅で見かけた'
>>>list_of_tokens = jTokenize(input_sentence)
>>>print list_of_tokens
>>>print '--'.join(list_of_tokens).encode('utf-8')
Returns:
...[u'\u79c1', u'\u306f', u'\u5f7c', u'\u3092', u'\uff15'...]
...私--は--彼--を--５--日--前--、--つまり--この--前--の--金曜日--に--駅--で--見かけ--た
Katakana Pronunciation:
>>>print '--'.join(jReads(input_sentence)).encode('utf-8')
...ワタシ--ハ--カレ--ヲ--ゴ--ニチ--マエ--、--ツマリ--コノ--マエ--ノ--キンヨウビ--ニ--エキ--デ--ミカケ--タ
Run Cabocha with original EUCJP or IS0-8859-1 configured encoding, with utf8 python
In Python
>>>from jNlp.jCabocha import cabocha
>>>print cabocha(input_sentence).encode('utf-8')
Output:
<sentence>
<chunk id="0" link="8" rel="D" score="0.971639" head="0" func="1">
<tok id="0" read="ワタシ" base="私" pos="名詞-代名詞-一般" ctype="" cform="" ne="O">私</tok>
<tok id="1" read="ハ" base="は" pos="助詞-係助詞" ctype="" cform="" ne="O">は</tok>
</chunk>
<chunk id="1" link="2" rel="D" score="0.488672" head="2" func="3">
<tok id="2" read="カレ" base="彼" pos="名詞-代名詞-一般" ctype="" cform="" ne="O">彼</tok>
<tok id="3" read="ヲ" base="を" pos="助詞-格助詞-一般" ctype="" cform="" ne="O">を</tok>
</chunk>
<chunk id="2" link="8" rel="D" score="2.25834" head="6" func="6">
<tok id="4" read="ゴ" base="５" pos="名詞-数" ctype="" cform="" ne="B-DATE">５</tok>
<tok id="5" read="ニチ" base="日" pos="名詞-接尾-助数詞" ctype="" cform="" ne="I-DATE">日</tok>
<tok id="6" read="マエ" base="前" pos="名詞-副詞可能" ctype="" cform="" ne="I-DATE">前</tok>
<tok id="7" read="、" base="、" pos="記号-読点" ctype="" cform="" ne="O">、</tok>
</chunk>
Uses data/katakanaChart.txt and parses the chart. See katakanaChart.
Usage
>>>from jNlp.jConvert import *
>>>input_sentence = u'気象庁が２１日午前４時４８分、発表した天気概況によると、'
>>>print ' '.join(tokenizedRomaji(input_sentence))
>>>print tokenizedRomaji(input_sentence)
Output
...kisyoutyou ga ni ichi nichi gozen yon ji yon hachi hun hapyou si ta tenki gaikyou ni yoru to
...[u'kisyoutyou', u'ga', u'ni', u'ichi', u'nichi', u'gozen',...]
katakanaChart.txt
On English Strings
>>> from jNlp.jProcessing import long_substr
>>> a = 'Once upon a time in Italy'
>>> b = 'Thre was a time in America'
>>> print long_substr(a, b)
Output
...a time in
On Japanese Strings
>>> a = u'これでアナタも冷え知らず'
>>> b = u'これでア冷え知らずナタも'
>>> print long_substr(a, b).encode('utf-8')
Output
...冷え知らず
Uses MinHash by checking the overlap http://en.wikipedia.org/wiki/MinHash
| English Strings: | |
|---|---|
>>> from jNlp.jProcessing import Similarities
>>> s = Similarities()
>>> a = 'There was'
>>> b = 'There is'
>>> print s.minhash(a,b)
...0.444444444444
| Japanese Strings: | |
|---|---|
>>> from jNlp.jProcessing import *
>>> a = u'これは何ですか？'
>>> b = u'これはわからないです'
>>> print s.minhash(' '.join(jTokenize(a)), ' '.join(jTokenize(b)))
...0.210526315789
This package uses the EDICT and KANJIDIC dictionary files. These files are the property of the Electronic Dictionary Research and Development Group , and are used in conformance with the Group’s licence .
Edict Parser By Paul Goins, see edict_search.py Edict Example sentences Parse by query, Pulkit Kathuria, see edict_examples.py Edict examples pickle files are provided but latest example files can be downloaded from the links provided.
Two files
utf8 Charset example file if not using src/jNlp/data/edict_examples
To convert EUCJP/ISO-8859-1 to utf8
iconv -f EUCJP -t UTF-8 path/to/edict_examples > path/to/save_with_utf-8
ISO-8859-1 edict_dictionary file
Outputs example sentences for a query in Japanese only for ambiguous words.
Latest Dictionary files can be downloaded here
| author: | Paul Goins License included linkToOriginal: |
|---|
For all entries of sense definitions
>>> from jNlp.edict_search import *
>>> query = u'認める'
>>> edict_path = 'src/jNlp/data/edict-yy-mm-dd'
>>> kp = Parser(edict_path)
>>> for i, entry in enumerate(kp.search(query)):
... print entry.to_string().encode('utf-8')
| Note: | Only outputs the examples sentences for ambiguous words (if word has one or more senses) |
|---|---|
| author: | Pulkit Kathuria |
>>> from jNlp.edict_examples import *
>>> query = u'認める'
>>> edict_path = 'src/jNlp/data/edict-yy-mm-dd'
>>> edict_examples_path = 'src/jNlp/data/edict_examples'
>>> search_with_example(edict_path, edict_examples_path, query)
Output
認める
Sense (1) to recognize;
EX:01 我々は彼の才能を*認*めている。We appreciate his talent.
Sense (2) to observe;
EX:01 ｘ線写真で異状が*認*められます。We have detected an abnormality on your x-ray.
Sense (3) to admit;
EX:01 母は私の計画をよいと*認*めた。Mother approved my plan.
EX:02 母は決して私の結婚を*認*めないだろう。Mother will never approve of my marriage.
EX:03 父は決して私の結婚を*認*めないだろう。Father will never approve of my marriage.
EX:04 彼は女性の喫煙をいいものだと*認*めない。He doesn't approve of women smoking.
...
This section covers (1) Sentiment Analysis on Japanese text using Word Sense Disambiguation, Wordnet-jp (Japanese Word Net file name wnjpn-all.tab), SentiWordnet (English SentiWordNet file name SentiWordNet_3.*.txt).
The following classifier is baseline, which works as simple mapping of Eng to Japanese using Wordnet and classify on polarity score using SentiWordnet.
>>> from jNlp.jSentiments import *
>>> jp_wn = '../../../../data/wnjpn-all.tab'
>>> en_swn = '../../../../data/SentiWordNet_3.0.0_20100908.txt'
>>> classifier = Sentiment()
>>> classifier.train(en_swn, jp_wn)
>>> text = u'監督、俳優、ストーリー、演出、全部最高！'
>>> print classifier.baseline(text)
...Pos Score = 0.625 Neg Score = 0.125
...Text is Positive
>>> from jNlp.jSentiments import *
>>> jp_wn = '_dicts/wnjpn-all.tab' #path to Japanese Word Net
>>> en_swn = '_dicts/SentiWordNet_3.0.0_20100908.txt' #Path to SentiWordNet
>>> classifier = Sentiment()
>>> sentiwordnet, jpwordnet = classifier.train(en_swn, jp_wn)
>>> positive_score = sentiwordnet[jpwordnet[u'全部']][0]
>>> negative_score = sentiwordnet[jpwordnet[u'全部']][1]
>>> print 'pos score = {0}, neg score = {1}'.format(positive_score, negative_score)
...pos score = 0.625, neg score = 0.0
Author: pulkit[at]jaist.ac.jp [change at with @]