content.raw: I am developing a personal project to further support and help people living with Depression and Anxiety. As I suffer mainly from chronic depression I would like to create a tool based on AI that can monitor my moods but first I will collect information about myself, my moods and after collecting at least 6 months of my moods and my writings I will be able to formulate as a kind of recognition when my emotions are “out of control” I mean those states or feelings of emptiness. I think that sometimes not all of us have access to treatments and therapies so I would like to develop in a free way this project that I have just started today. I have already started the code to register events of my moods. I will share with you the updates :D 
content.type: text
content.value: I am developing a personal project to further support and help people living with Depression and Anxiety. As I suffer mainly from chronic depression I would like to create a tool based on AI that can monitor my moods but first I will collect information about myself, my moods and after collecting at least 6 months of my moods and my writings I will be able to formulate as a kind of recognition when my emotions are “out of control” I mean those states or feelings of emptiness. I think that sometimes not all of us have access to treatments and therapies so I would like to develop in a free way this project that I have just started today. I have already started the code to register events of my moods. I will share with you the updates :D 
content.type: new_line
content.type: new_line
content.type: new_line
content.code: import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.metrics import accuracy_score, classification_report
  import nltk
  from nltk.corpus import stopwords
  import string
  import matplotlib.pyplot as plt
  from datetime import datetime
  
  nltk.download('stopwords')
  
  data = {
      'text': [
          "Hoy me siento bien, aunque un poco cansado", 
          "Me siento triste y solo", 
          "Esto es frustrante, todo sale mal", 
          "Estoy nervioso por lo que va a pasar",
          "No puedo con este estrés", 
          "Todo está saliendo bien, me siento optimista", 
          "Siento miedo de lo que pueda suceder", 
          "Hoy fue un día horrible"
      ],
      'emotion': [
          'felicidad', 
          'tristeza', 
          'enojo', 
          'ansiedad', 
          'ansiedad', 
          'felicidad', 
          'miedo', 
          'tristeza'
      ]
  }
  
  df = pd.DataFrame(data)
  
  # Función para limpiar el texto
  def clean_text(text):
content.raw: ```
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.metrics import accuracy_score, classification_report
  import nltk
  from nltk.corpus import stopwords
  import string
  import matplotlib.pyplot as plt
  from datetime import datetime
  
  nltk.download('stopwords')
  
  data = {
      'text': [
          "Hoy me siento bien, aunque un poco cansado", 
          "Me siento triste y solo", 
          "Esto es frustrante, todo sale mal", 
          "Estoy nervioso por lo que va a pasar",
          "No puedo con este estrés", 
          "Todo está saliendo bien, me siento optimista", 
          "Siento miedo de lo que pueda suceder", 
          "Hoy fue un día horrible"
      ],
      'emotion': [
          'felicidad', 
          'tristeza', 
          'enojo', 
          'ansiedad', 
          'ansiedad', 
          'felicidad', 
          'miedo', 
          'tristeza'
      ]
  }
  
  df = pd.DataFrame(data)
  
  # Función para limpiar el texto
  def clean_text(text):
  ```
content.type: code_fence
content.type: new_line
content.raw: Yes, I speak Spanish :P too 
content.type: text
content.value: Yes, I speak Spanish :P too 
rawContent: I am developing a personal project to further support and help people living with Depression and Anxiety. As I suffer mainly from chronic depression I would like to create a tool based on AI that can monitor my moods but first I will collect information about myself, my moods and after collecting at least 6 months of my moods and my writings I will be able to formulate as a kind of recognition when my emotions are “out of control” I mean those states or feelings of emptiness. I think that sometimes not all of us have access to treatments and therapies so I would like to develop in a free way this project that I have just started today. I have already started the code to register events of my moods. I will share with you the updates :D 
  
  
  ```
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.metrics import accuracy_score, classification_report
  import nltk
  from nltk.corpus import stopwords
  import string
  import matplotlib.pyplot as plt
  from datetime import datetime
  
  nltk.download('stopwords')
  
  data = {
      'text': [
          "Hoy me siento bien, aunque un poco cansado", 
          "Me siento triste y solo", 
          "Esto es frustrante, todo sale mal", 
          "Estoy nervioso por lo que va a pasar",
          "No puedo con este estrés", 
          "Todo está saliendo bien, me siento optimista", 
          "Siento miedo de lo que pueda suceder", 
          "Hoy fue un día horrible"
      ],
      'emotion': [
          'felicidad', 
          'tristeza', 
          'enojo', 
          'ansiedad', 
          'ansiedad', 
          'felicidad', 
          'miedo', 
          'tristeza'
      ]
  }
  
  df = pd.DataFrame(data)
  
  # Función para limpiar el texto
  def clean_text(text):
  ```
  Yes, I speak Spanish :P too 
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