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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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from .utils import post_json |
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from .ds import Sentence, Document |
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import json |
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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".format(address) |
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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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service = "{}/document".format(self._service) |
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try: |
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sentiment_scores = post_json(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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#raise Exception("Connection refused! Is the server running?") |
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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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service = "{}/sentence".format(self._service) |
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try: |
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sentiment_scores = post_json(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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#raise Exception("Connection refused! Is the server running?") |
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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 = "{}/text".format(self._service) |
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try: |
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sentiment_scores = post_json(service, json.dumps({"text":"{}".format(text)})) |
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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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#raise Exception("Connection refused! Is the server running?") |
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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, str): |
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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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else: |
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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 = "{}/corenlp/sentiment".format(address) |
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myedibleenso /
py-processors
