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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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from __future__ import unicode_literals |
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from .utils import post_json |
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from .ds import Sentence, Document |
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from .processors import Message, SentencesMessage |
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import json |
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import six |
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class SentimentAnalysisAPI(object): |
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""" |
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API for performing sentiment analysis |
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""" |
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def __init__(self, address): |
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self._service = address |
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self.corenlp = CoreNLPSentimentAnalyzer(self._service) |
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class SentimentAnalyzer(object): |
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def __init__(self, address): |
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self._service = "{}/sentiment/score".format(address) |
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self._text_service = self._service |
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self._segmented_service = self._service |
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self._sentence_service = self._service |
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self._document_service = self._service |
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def score_document(self, doc): |
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""" |
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Sends a Document to the server for sentiment scoring |
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Returns a list of scores (one for each sentence) |
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""" |
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try: |
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sentiment_scores = post_json(self._document_service, doc.to_JSON()) |
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return sentiment_scores["scores"] |
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except Exception as e: |
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#print(e) |
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return None |
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def score_sentence(self, sentence): |
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""" |
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Sends a Sentence to the server for sentiment scoring |
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Returns a single score |
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""" |
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try: |
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sentiment_scores = post_json(self._sentence_service, sentence.to_JSON()) |
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return sentiment_scores["scores"][0] |
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except Exception as e: |
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print(e) |
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return None |
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def score_segmented_text(self, sentences): |
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""" |
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Sends segmented text to the server for sentiment scoring |
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Returns a score for each sentence |
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""" |
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try: |
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msg = SentencesMessage(sentences) |
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sentiment_scores = post_json(self._segmented_service, msg.to_JSON()) |
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return sentiment_scores["scores"] |
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except Exception as e: |
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#print(e) |
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return None |
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def score_text(self, text): |
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""" |
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Sends text to the server for sentiment scoring |
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Returns a list of scores (one for each sentence) |
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""" |
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service = self._text_service |
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try: |
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msg = Message(text) |
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sentiment_scores = post_json(self._text_service, msg.to_JSON()) |
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return sentiment_scores["scores"] |
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except Exception as e: |
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#print(e) |
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return None |
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def score(self, data): |
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""" |
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Sniff out data type and to properly send to the server for sentiment scoring |
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""" |
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if isinstance(data, six.text_type): |
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return self.score_text(data) |
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elif isinstance(data, Sentence): |
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return self.score_sentence(data) |
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elif isinstance(data, Document): |
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return self.score_document(data) |
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# a list of pre segmented sentences |
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elif isinstance(data, list): |
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return self.score_segmented_text(data) |
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else: |
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#print("Type of data: {}".format(type(data))) |
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return None |
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class CoreNLPSentimentAnalyzer(SentimentAnalyzer): |
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""" |
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Bridge to CoreNLP's tree-based sentiment analysis |
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""" |
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def __init__(self, address): |
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self._service = "{}/sentiment/corenlp/score".format(address) |
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self._text_service = self._service |
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self._segmented_service = "{}/segmented".format(self._service) |
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self._sentence_service = self._service |
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self._document_service = self._service |
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myedibleenso /
py-processors
