【snownlp相关文件】上传自己的模型，调用utils/mynlp

utils/mynlp/sentiment/__init__.py

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+# -*- coding: utf-8 -*-
+from __future__ import unicode_literals
+
+import os
+import codecs
+
+from .. import normal
+from .. import seg
+from ..classification.bayes import Bayes
+
+data_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+                         'sentiment.marshal')
+
+
+class Sentiment(object):
+
+    def __init__(self):
+        self.classifier = Bayes()
+
+    def save(self, fname, iszip=True):
+        self.classifier.save(fname, iszip)
+
+    def load(self, fname=data_path, iszip=True):
+        self.classifier.load(fname, iszip)
+
+    def handle(self, doc):
+        words = seg.seg(doc)
+        words = normal.filter_stop(words)
+        return words
+
+    def train(self, neg_docs, pos_docs):
+        data = []
+        for sent in neg_docs:
+            data.append([self.handle(sent), 'neg'])
+        for sent in pos_docs:
+            data.append([self.handle(sent), 'pos'])
+        self.classifier.train(data)
+
+    def classify(self, sent):
+        ret, prob = self.classifier.classify(self.handle(sent))
+        if ret == 'pos':
+            return prob
+        return 1-prob
+
+
+classifier = Sentiment()
+classifier.load()
+
+
+def train(neg_file, pos_file):
+    neg = codecs.open(neg_file, 'r', 'utf-8').readlines()
+    pos = codecs.open(pos_file, 'r', 'utf-8').readlines()
+    neg_docs = []
+    pos_docs = []
+    for line in neg:
+        neg_docs.append(line.rstrip("\r\n"))
+    for line in pos:
+        pos_docs.append(line.rstrip("\r\n"))
+    global classifier
+    classifier = Sentiment()
+    classifier.train(neg_docs, pos_docs)
+
+
+def save(fname, iszip=True):
+    classifier.save(fname, iszip)
+
+
+def load(fname, iszip=True):
+    classifier.load(fname, iszip)
+
+
+def classify(sent):
+    return classifier.classify(sent)