Merged into monorepo

sentiment-vader/README.md

@@ -1,10 +1,12 @@
 # Sentimet-vader plugin
 
-=========
-
 Vader is a plugin developed at GSI UPM for sentiment analysis.  
+The response of this plugin uses [Marl ontology](https://www.gsi.dit.upm.es/ontologies/marl/) developed at GSI UPM for semantic web.
+
+## Acknowledgements
+
+This plugin uses the vaderSentiment module underneath, which is described in the paper:
 
-For developing this plugin, it has been used the module vaderSentiment, which is described in the paper:
   VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text
   C.J. Hutto and Eric Gilbert
   Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
@@ -15,16 +17,16 @@ For more information about the functionality, check the official repository
 
 https://github.com/cjhutto/vaderSentiment
 
-The response of this plugin uses [Marl ontology](https://www.gsi.dit.upm.es/ontologies/marl/) developed at GSI UPM for semantic web.
-
 ## Usage
 
-Params accepted:
+Parameters:
+
 - Language: es (Spanish), en(English).
 - Input: Text to analyse.
 
 
 Example request: 
+
 ```
 http://senpy.cluster.gsi.dit.upm.es/api/?algo=sentiment-vader&language=en&input=I%20love%20Madrid
 ```

sentiment-vader/README.txt

@@ -1,16 +0,0 @@
-==========
-
-This README file describes the plugin vaderSentiment.
-
-For developing this plugin, it has been used the module vaderSentiment, which is described in the paper:
-  VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text
-  C.J. Hutto and Eric Gilbert
-  Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
-
-If you use this plugin in your research, please cite the above paper
-
-For more information about the functionality, check the official repository
-
-https://github.com/cjhutto/vaderSentiment
-
-========

sentiment-vader/sentiment-vader.senpy

@@ -1,25 +0,0 @@
-{
-    "name": "sentiment-vader",
-    "module": "sentiment-vader",
-    "description": "Sentiment classifier using vaderSentiment module. Params accepted: Language: {en, es}. The output uses Marl ontology developed at GSI UPM for semantic web.",
-    "author": "@icorcuera",
-    "version": "0.1",
-    "extra_params": {
-            "language": {
-            "@id": "lang_rand",
-            "aliases": ["language", "l"],
-            "required": false,
-            "options": ["es", "en", "auto"]
-        },
-
-            "aggregate": {
-             "aliases": ["aggregate","agg"],
-             "options": ["true", "false"],
-             "required": false,
-             "default": false
-
-        }
-   
-    },
-    "requirements": {}
-}

sentiment-vader/test.py

@@ -1,44 +0,0 @@
-import os
-import logging
-logging.basicConfig()
-try:
-    import unittest.mock as mock
-except ImportError:
-    import mock
-from senpy.extensions import Senpy
-from flask import Flask
-from flask.ext.testing import TestCase
-import unittest
-
-class vaderTest(unittest.TestCase):
-
-    def setUp(self):
-        self.app = Flask("test_plugin")
-        self.dir = os.path.join(os.path.dirname(__file__))
-        self.senpy = Senpy(plugin_folder=self.dir, default_plugins=False)
-        self.senpy.init_app(self.app)
-
-    def tearDown(self):
-        self.senpy.deactivate_plugin("vaderSentiment", sync=True)
-
-    def test_analyse(self):
-        plugin = self.senpy.plugins["vaderSentiment"]
-        plugin.activate() 
-
-        texts = {'I am tired :(' : 'marl:Negative',
-                 'I love pizza' : 'marl:Positive',
-                 'I like going to the cinema :)' : 'marl:Positive',
-                 'This cake is disgusting' : 'marl:Negative'}
-
-        for text in texts:
-            response = plugin.analyse(input=text)
-            expected = texts[text]
-            sentimentSet = response.entries[0].sentiments
-
-            max_sentiment = max(sentimentSet, key=lambda x: x['marl:polarityValue'])
-            assert max_sentiment['marl:hasPolarity'] == expected
-
-        plugin.deactivate()
-
-if __name__ == '__main__':
-    unittest.main()

sentiment-vader/vader_plugin.py

@@ -5,15 +5,37 @@ from senpy.plugins import SentimentPlugin, SenpyPlugin
 from senpy.models import Results, Sentiment, Entry
 import logging
 
-logger = logging.getLogger(__name__)
 
-class vaderSentimentPlugin(SentimentPlugin):
+class VaderSentimentPlugin(SentimentPlugin):
+    '''
+    Sentiment classifier using vaderSentiment module. Params accepted: Language: {en, es}. The output uses Marl ontology developed at GSI UPM for semantic web.
+    '''
+    name = "sentiment-vader"
+    module = "sentiment-vader"
+    author = "@icorcuera"
+    version = "0.1.1"
+    extra_params = {
+        "language": {
+          "@id": "lang_rand",
+          "aliases": ["language", "l"],
+          "default": "auto",
+          "options": ["es", "en", "auto"]
+        },
 
-    def analyse_entry(self,entry,params):
+        "aggregate": {
+            "aliases": ["aggregate","agg"],
+            "options": ["true", "false"],
+            "default": False
+        }
 
-        logger.debug("Analysing with params {}".format(params))
+    }
+    requirements = {}
 
-        text_input = entry.get("text", None)
+    def analyse_entry(self, entry, params):
+
+        self.log.debug("Analysing with params {}".format(params))
+
+        text_input = entry.text
         aggregate = params['aggregate']
 
         score = sentiment(text_input)
@@ -22,15 +44,18 @@ class vaderSentimentPlugin(SentimentPlugin):
                              marl__hasPolarity= "marl:Positive",
                              marl__algorithmConfidence= score['pos']
             )
+        opinion0.prov(self)
         opinion1 = Sentiment(id= "Opinion_negative",
             marl__hasPolarity= "marl:Negative",
             marl__algorithmConfidence= score['neg']
             )
+        opinion1.prov(self)
         opinion2 = Sentiment(id= "Opinion_neutral",
             marl__hasPolarity = "marl:Neutral",
             marl__algorithmConfidence = score['neu']
             )
-        
+        opinion2.prov(self)
+
         if aggregate == 'true':
             res = None
             confident = max(score['neg'],score['neu'],score['pos'])
@@ -47,3 +72,25 @@ class vaderSentimentPlugin(SentimentPlugin):
             entry.sentiments.append(opinion2)
 
         yield entry
+
+    test_cases = []
+
+    test_cases = [
+        {
+            'input': 'I am tired :(',
+            'polarity': 'marl:Negative'
+        },
+        {
+            'input': 'I love pizza :(',
+            'polarity': 'marl:Positive'
+        },
+        {
+            'input': 'I enjoy going to the cinema :)',
+            'polarity': 'marl:Negative'
+        },
+        {
+            'input': 'This cake is disgusting',
+            'polarity': 'marl:Negative'
+        },
+        
+    ]