According to the World Health Organization, depression is the leading cause of disability worldwide. Globally, more than 300 million people of all ages suffer from the disorder. And the incidence of the disorder is increasing everywhere. Depression is a complex condition, involving many systems of the body, including the immune system, either as cause or effect. It disrupts sleep and it interferes with appetite; in some cases, it causes weight loss; in others, it contributes to weight gain. Depression is also often accompanied by anxiety. Research indicates that not only do the two conditions co-occur but that they overlap in vulnerability patterns.

Because of its complexity, a full understanding of depression has been elusive. Researchers have some evidence that depression susceptibility is related to diet, both directly—through inadequate consumption of nutrients such as omega-3 fats—and indirectly, through the variety of bacteria that populate the gut. But depression involves mood and thoughts as well as the body, and it causes pain for both those living with the disorder and those who care about them.

Even in the most severe cases, depression is highly treatable. The condition is often cyclical, and early treatment may prevent or forestall recurrent episodes. Many studies show that the most effective treatment is cognitive behavioral therapy, which addresses problematic thought patterns, with or without the use of antidepressant drugs. In addition, evidence is quickly accumulating that regular mindfulness meditation, on its own or combined with cognitive therapy, can stop depression before it starts by diminishing reactivity to distressing experiences, effectively enabling disengagement of attention from the repetitive negative thoughts that often set the downward spiral of mood in motion.

Naive Bayes is a machine learning model that is used for large volumes of data, even if you are working with data that has millions of data records the recommended approach is Naive Bayes. It gives very good results when it comes to NLP tasks such as sentimental analysis. It is a fast and uncomplicated classification algorithm.

It is a theorem that works on conditional probability. Conditional probability is the probability that something will happen, given that something else has already occurred. The conditional probability can give us the probability of an event using its prior knowledge.

Where,
P(A): The probability of hypothesis H being true. This is known as the prior probability.
P(B): The probability of the evidence.
P(A|B): The probability of the evidence given that hypothesis is true.
P(B|A): The probability of the hypothesis given that the evidence is true.

• It is a kind of classifier that works on Bayes theorem.
• Prediction of membership probabilities is made for every class such as the probability of data points associated to a particular class.
• The class having maximum probability is appraised as the most suitable class.
• This is also referred as Maximum A Posteriori (MAP).
• The MAP for a hypothesis is:
  • 𝑀𝐴𝑃 (𝐻) = max 𝑃((𝐻|𝐸))
  • 𝑀𝐴𝑃 (𝐻) = max 𝑃((𝐻|𝐸) ∗ (𝑃(𝐻)) /𝑃(𝐸))
  • 𝑀𝐴𝑃 (𝐻) = max(𝑃(𝐸|𝐻) ∗ 𝑃(𝐻))
  • 𝑃 (𝐸) is evidence probability, and it is used to normalize the result. The result will not be affected by removing 𝑃(𝐸).
• NB classifiers conclude that all the variables or features are not related to each other.
• The Existence or absence of a variable does not impact the existence or absence of any other variable.
• Example:
  • Fruit may be observed to be an apple if it is red, round, and about 4″ in diameter.
  • In this case also even if all the features are interrelated to each other, and NB classifier will observe all of these independently contributing to the probability that the fruit is an apple.
• We experiment with the hypothesis in real datasets, given multiple features.
• So, computation becomes complex.

1. Gaussian Naïve Bayes: When characteristic values are continuous in nature then an assumption is made that the values linked with each class are dispersed according to Gaussian that is Normal Distribution.

2. Multinomial Naïve Bayes: Multinomial Naive Bayes is favored to use on data that is multinomial distributed. It is widely used in text classification in NLP. Each event in text classification constitutes the presence of a word in a document.

3. Bernoulli Naïve Bayes: When data is dispensed according to the multivariate Bernoulli distributions then Bernoulli Naive Bayes is used. That means there exist multiple features but each one is assumed to contain a binary value. So, it requires features to be binary-valued.

Aim for this project is to build a machine learning model that can classify a text to two category: depressed or not depressed using bayes formula by calculating the sentiment of the frequency words in the training data.

For this project we need a dataset which contain a lot of depression and neutral text, apprently so the model has a good accuracy we need a lot of text (eight hundred to more than a million is a good range). Because it's difficult to extract that much of tweets using Twitter API so we decided to use the Sentiment140 dataset from Kaggle. The dataset for this project contains 1,600,000 tweets with label of 0 for negative sentiment and 4 for positive sentiment.

• Vidushi Gandhi

References: psychologytoday.com, analyticssteps.com