Natural Language Processing(Part 20)-Training Naïve Bayes
📚Chapter 3: Sentiment Analysis (Naive Bayes)
This tutorial, I’ll show you how to train the Naive Bayes classifier. In this context, we train in something different than in logistic regression or deep learning. There is no gradient descent. We’re just counting frequencies of words in the corpus. You will now be creating step-by-step, a Naive Bayes model for sentiment analysis using a corpus of tweets that you’ve already collected. That’s awesome.
Sections
Get annotate a Dataset
Preprocess
Computer Frequency
Get Lambda
Computer Logprior
Summary
Section-1- Get annotate a Dataset
The first step for any supervised machine learning project is to gather the data to train and test your model. For sentiment analysis of tweets, this step involves getting a corpus of tweets and dividing it into two groups; positive and negative tweets.
Section 2- Preprocess
The next step is fundamental to your model success. The preprocessing step, as described in the previous module, consists of five smaller steps. One, lowercase the texts, 2, remove punctuation, URLs, and handles, 3, remove stop words, 4, stemming or reducing words to their common stem, and 5, finally, tokenizing or splitting your document into single words or tokens. For the assignments in this week, it will be relatively straightforward to implement this processing pipeline. In the real-world, you might find the gathering and processing of texts takes up a big chunk of your project’s hours.
Section 3- Computer Frequency
Once you have a clean corpus of processed tweets, you will start by computing the vocabulary foreach word in class, like you did in the previous week. This process will produce a table like the one shown here. You can compute the sum of words and class in each corpus in the same step. From this table of frequencies, you get the conditional probability or probability of word given class by using the Laplacian smoothing formula. See how the number of unique words in V class is equal to 6. You only account for the words in the table, not the total number of words in the original corpus. This produces a table of conditional probabilities for each word and each class. This table only contains values greater than 0.
Section 4- Get Lambda
For the forth step, you’ll get the Lambda score for each word, which is the log of the ratio of your conditional
probabilities.
Section 5- Computer Logprior
The fifth step is the estimation of the log prior. To do this, you’ll need to count the number of positive and negative tweets. Then the log prior is the log of the ratio of the number of positive tweets over the number of negative tweets. In the upcoming assignments, you’ll be working with a balanced datasets. The log prior is equal to 0. For unbalanced datasets, this term will become important.
Summary
In summary, training a Naive Bayes model can be divided into six logical steps. You get to annotate a dataset with positive and negative tweets. Typically, it’s better if your tweets match the same context that you want to use in the final model. Then you process the raw texts to get a corpus of clean standardized tokens. You compute the dictionary frequencies for each word in class, then compute the conditional probabilities of each word using the Laplacian smoothing formula, you compute the lambda factor for each word, and finally, you estimate the log prior of the model or how likely it is to see a positive tweet in your accounts. That was quite a lot. Now, you have seen how to build a probability table needed to apply Naive Bayes. The next exciting thing we’ll do is to classify
your sentences.
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References
1- Natural Language Processing with Classification and Vector Spaces
