Add new column in dataframe based on multiple column conditions

You can use np.select()

conditions = [df['Positive']>=0.80, df['Negative']>=0.80, ((df['Positive']>=0.50) & (df['Positive']<0.80)),
              ((df['Negative']>=0.50) & (df['Negative']<0.80)), df['Neutral']>=0.5]
values = ['Very Positive', 'Very Negative', 'Positive', 'Negative', 'Neutral']
df['Sentiment'] = np.select(conditions, values, default=np.nan)

OUTPUT

                                               Text  Negative  Neutral  Positive      Sentiment
0                          I lost my phone. I am sad      0.80     0.15      0.05  Very Negative
1                                   How is your day?      0.10     0.80      0.10        Neutral
2                     Let's go out for dinner today.      0.06     0.55      0.39        Neutral
3  I am super pissed at my friend for cancelling ...      0.73     0.11      0.16       Negative
4                     I am so happy  I want to dance      0.00     0.10      0.90  Very Positive
5      I am not sure if I should laugh or just smile      0.08     0.24      0.68       Positive

You can create a function that map from the three values into sentiment then use the apply method to apply the function for each row in the tables. It should generate one column (series). Then, append the series to the main table.

It should looks something like this

def your_fn(values):
  pos = values["Positive"]
  neu = values["Neutral"]
  neg = values["Negative"]

  # 1. If the value of negative or positive is most dominating and >= 0.8 (80%) then mark it as very negative or very positive.
  if (pos >= .8): 
    return "Very positive"
  if (neg >= .8):
    return "Very negative"
  
  # 2. If the value of negative or positive is most dominating but it is >= 0.5 but less than 0.8 then just negative or positive.
  if (pos >= .5): 
    return "Positive"
  if (neg >= .5):
    return "Negative"

  # 3. If the value of neutral is >= 0.5 then Neutral. There is no such thing as Very Neutral.
  if (neu >= .5):
    return "Neutral"
  
  return "-"

df['Sentiment'] = df.apply(your_fn, axis=1)