i have alwayes this result however i changed the message
always detecting a postive sentiment with confidence 0.333
{‘intent’: {‘name’: ‘insultes’, ‘confidence’: 0.35888357915887636}, ‘entities’: [{‘value’: ‘pos’, ‘confidence’: 0.3333333333333333, ‘entity’: ‘sentiment’, ‘extractor’: ‘sentiment_extractor’}], ‘intent_ranking’: [{‘name’: ‘insultes’, ‘confidence’: 0.35888357915887636}, {‘name’: ‘salutations’, ‘confidence’: 0.07922486366609584}, {‘name’: ‘au_revoir’, ‘confidence’: 0.06963384023417138}, {‘name’: ‘out’, ‘confidence’: 0.057168641232126574}, {‘name’: ‘positive_answer’, ‘confidence’: 0.05699548462320999}, {‘name’: ‘congrats’, ‘confidence’: 0.05146011802312083}, {‘name’: ‘salaire’, ‘confidence’: 0.05105493986389435}, {‘name’: ‘annulation’, ‘confidence’: 0.04765725667849151}, {‘name’: ‘negative_answer’, ‘confidence’: 0.04180718673910084}, {‘name’: ‘merci’, ‘confidence’: 0.03432302984684233}], ‘text’: ‘connard’}
my data contains nlu ans stories .
my config file 
# Configuration for Rasa NLU.
Components
language: fr_core_news_md pipeline:
- name: “SpacyNLP”
- name: “SpacyTokenizer”
- name: “sentiment.SentimentAnalyzer”
- name: “SpacyFeaturizer”
- name: “WhitespaceTokenizer”
- name: “RegexFeaturizer”
- name: “CRFEntityExtractor”
- name: “EntitySynonymMapper”
- name: “CountVectorsFeaturizer”
- name: “SklearnIntentClassifier”
Configuration for Rasa Core.
Policies
policies:
- name: MemoizationPolicy
- name: KerasPolicy
- name: MappingPolicy
- name: FormPolicy
- name: “FallbackPolicy” nlu_threshold: 0.25 core_threshold: 0.3 fallback_action_name: “action_my_fallback”
labels:
pos pos neu neu neg neg
I’ve created also a registry file
from rasa_nlu.customcomponents.sentiment import MySentimentAnalyzer
from data import sentiment
# all import needed
component_classes = [
# all rasa components added no change
MySentimentAnalyzer
]
sentiment.py
from rasa.nlu.components import Component from rasa.nlu import utils from rasa.nlu.model import Metadata
import nltk from nltk.classify import NaiveBayesClassifier import os
import typing from typing import Any, Optional, Text, Dict
SENTIMENT_MODEL_FILE_NAME = “sentiment_classifier.pkl”
class SentimentAnalyzer(Component): “”“A custom sentiment analysis”"" name = “sentiment” provides = [“entities”] requires = [“tokens”] defaults = {} language_list = [“fr_core_news_md”] print(‘initialised the class’)
def __init__(self, component_config=None):
super(SentimentAnalyzer, self).__init__(component_config)
def train(self, training_data, cfg, **kwargs):
"""Load the sentiment polarity labels from the text
file, retrieve training tokens and after formatting
data train the classifier."""
with open('labels.txt','r') as f:
labels = f.read().splitlines()
training_data = training_data.training_examples #list of Message objects
tokens = [list(map(lambda x: x.text, t.get('tokens'))) for t in training_data]
processed_tokens = [self.preprocessing(t) for t in tokens]
labeled_data = [(t, x) for t,x in zip(processed_tokens, labels)]
self.clf = NaiveBayesClassifier.train(labeled_data)
def convert_to_rasa(self, value, confidence):
"""Convert model output into the Rasa NLU compatible output format."""
entity = {"value": value,
"confidence": confidence,
"entity": "sentiment",
"extractor": "sentiment_extractor"}
return entity
def preprocessing(self, tokens):
"""Create bag-of-words representation of the training examples."""
return ({word: True for word in tokens})
def process(self, message, **kwargs):
"""Retrieve the tokens of the new message, pass it to the classifier
and append prediction results to the message class."""
if not self.clf:
# component is either not trained or didn't
# receive enough training data
entity = None
else:
tokens = [t.text for t in message.get("tokens")]
tb = self.preprocessing(tokens)
pred = self.clf.prob_classify(tb)
sentiment = pred.max()
confidence = pred.prob(sentiment)
entity = self.convert_to_rasa(sentiment, confidence)
message.set("entities", [entity], add_to_output=True)
def persist(self, file_name, model_dir):
"""Persist this model into the passed directory."""
classifier_file = os.path.join(model_dir, SENTIMENT_MODEL_FILE_NAME)
utils.json_pickle(classifier_file, self)
return {"classifier_file": SENTIMENT_MODEL_FILE_NAME}
@classmethod
def load(cls,
meta: Dict[Text, Any],
model_dir=None,
model_metadata=None,
cached_component=None,
**kwargs):
file_name = meta.get("classifier_file")
classifier_file = os.path.join(model_dir, file_name)
return utils.json_unpickle(classifier_file)
