diff --git a/emotion-depechemood/depechemood_plugin.py b/emotion-depechemood/depechemood_plugin.py
new file mode 100644
index 0000000..25389be
--- /dev/null
+++ b/emotion-depechemood/depechemood_plugin.py
@@ -0,0 +1,159 @@
+#!/usr/local/bin/python
+# coding: utf-8
+
+import os
+import re
+import string
+import numpy as np
+import pandas as pd
+from six.moves import urllib
+from nltk.corpus import stopwords
+
+from senpy import EmotionPlugin, TextBox, models
+
+
+class DepecheMood(TextBox, EmotionPlugin):
+    '''Plugin that uses the DepecheMood++ emotion lexicon.'''
+
+    author = 'Oscar Araque'
+    version = '0.1'
+
+    def __init__(self, *args, **kwargs):
+        super(DepecheMood, self).__init__(*args, **kwargs)
+        self.LEXICON_URL = "https://github.com/marcoguerini/DepecheMood/raw/master/DepecheMood%2B%2B/DepecheMood_english_token_full.tsv"
+        self.EMOTIONS = ['AFRAID', 'AMUSED', 'ANGRY', 'ANNOYED', 'DONT_CARE', 'HAPPY', 'INSPIRED', 'SAD',]
+        self._mapping = {
+            'AFRAID': 'wna:negative-fear',
+            'AMUSED': 'wna:amusement',
+            'ANGRY': 'wna:anger',
+            'ANNOYED': 'wna:annoyance',
+            'DONT_CARE': 'wna:indifference',
+            'HAPPY': 'wna:joy',
+            'INSPIRED': 'wna:awe',
+            'SAD': 'wna:sadness',
+        }
+        self._noise = self.__noise() 
+        self._stop_words = stopwords.words('english') + ['']
+        self._lex_vocab = None
+        self._lex = None
+
+    def __noise(self):
+        noise = set(string.punctuation) | set('«»')
+        noise = {ord(c): None for c in noise}
+        return noise
+
+    def activate(self):
+        self._lex = self.download_lex()
+        self._lex_vocab = set(list(self._lex.keys()))
+
+    def clean_str(self, string):
+        string = re.sub(r"[^A-Za-z0-9().,!?\'\`]", " ", string)
+        string = re.sub(r"[0-9]+", " num ", string)
+        string = re.sub(r"\'s", " \'s", string)
+        string = re.sub(r"\'ve", " \'ve", string)
+        string = re.sub(r"n\'t", " n\'t", string)
+        string = re.sub(r"\'re", " \'re", string)
+        string = re.sub(r"\'d", " \'d", string)
+        string = re.sub(r"\'ll", " \'ll", string)
+        string = re.sub(r"\.", " . ", string)
+        string = re.sub(r",", " , ", string)
+        string = re.sub(r"!", " ! ", string)
+        string = re.sub(r"\(", " ( ", string)
+        string = re.sub(r"\)", " ) ", string)
+        string = re.sub(r"\?", " ? ", string)
+        string = re.sub(r"\s{2,}", " ", string)
+        return string.strip().lower()
+
+    def preprocess(self, text):
+        if text is None:
+            return None
+        tokens = self.clean_str(text).translate(self._noise).split(' ')
+        tokens = [tok for tok in tokens if tok not in self._stop_words]
+        return tokens   
+
+    def estimate_emotion(self, tokens, emotion):
+        s = []
+        for tok in tokens:
+            s.append(self._lex[tok][emotion])
+        dividend = np.sum(s) if np.sum(s) > 0 else 0
+        divisor = len(s) if len(s) > 0 else 1
+        S = np.sum(s) / divisor
+        return S
+
+    def estimate_all_emotions(self, tokens):
+        S = {}
+        intersection = set(tokens) & self._lex_vocab
+        for emotion in self.EMOTIONS:
+            s = self.estimate_emotion(intersection, emotion)
+            emotion_mapped = self._mapping[emotion]
+            S[emotion_mapped] = s
+        return S
+
+    def download_lex(self, file_path='DepecheMood_english_token_full.tsv', freq_threshold=10):
+
+        try:
+            file_path = self.find_file(file_path)
+        except IOError:
+            filename, _ = urllib.request.urlretrieve(self.LEXICON_URL, file_path)
+
+        lexicon = pd.read_csv(file_path, sep='\t', index_col=0)
+        lexicon = lexicon[lexicon['freq'] >= freq_threshold]
+        lexicon.drop('freq', axis=1, inplace=True)
+        lexicon = lexicon.T.to_dict()
+        return lexicon
+
+    def output(self, output, entry, **kwargs):
+        s = models.EmotionSet()
+        s.prov__wasGeneratedBy = self.id
+        entry.emotions.append(s)
+        for label, value in output.items():
+            e = models.Emotion(onyx__hasEmotionCategory=label,
+                               onyx__hasEmotionIntensity=value)
+            s.onyx__hasEmotion.append(e)
+        return entry
+
+    def predict_one(self, input, **kwargs):
+        tokens = self.preprocess(input)
+        estimation = self.estimate_all_emotions(tokens)
+        return estimation
+
+    test_cases = [
+        {
+            'entry': {
+                'nif:isString': 'My cat is very happy',
+            },
+            'expected': {
+                'emotions': [
+                    {
+                        '@type': 'emotionSet',
+                        'onyx:hasEmotion': [
+                            {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:negative-fear',
+                             'onyx:hasEmotionIntensity': 0.05278117640010922, },
+                            {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:amusement',
+                             'onyx:hasEmotionIntensity': 0.2114806151413433, },
+                            {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:anger',
+                             'onyx:hasEmotionIntensity': 0.05726119426520887, },
+                            {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:annoyance',
+                             'onyx:hasEmotionIntensity': 0.12295990731053638, },
+                            {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:indifference',
+                             'onyx:hasEmotionIntensity': 0.1860159893608025, },
+                            {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:joy',
+                             'onyx:hasEmotionIntensity': 0.12904050973724163, },
+                            {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:awe',
+                             'onyx:hasEmotionIntensity': 0.17973650399862967, },
+                            {'@type': 'emotion', 'onyx:hasEmotionCategory': 'wna:sadness',
+                                'onyx:hasEmotionIntensity': 0.060724103786128455, },
+                        ]
+                    }
+                ]
+            }
+        }
+    ]
+
+
+if __name__ == '__main__':
+    from senpy.utils import easy, easy_load, easy_test
+    # sp, app = easy_load()
+    # for plug in sp.analysis_plugins:
+    #     plug.test()
+    easy()