Natural Language Processing(Part 17)-Laplacian Smoothing
📚Chapter 3: Sentiment Analysis (Naive Bayes)
Description
Sometimes you try to calculate the probability of a word happening after a word. To do that you might want to count the number of times those two words showed up. One after another, divided by the number of times the first word appeared. Now what if the two words never showed up next to each other in the training corpus. You get a probability of zero, and the probability of an entire sequence might go to zero. Now how can we fix this problem?
In the era of big data and social media, understanding and analyzing sentiments has become a crucial aspect of extracting valuable insights. Sentiment analysis, also known as opinion mining, involves the use of natural language processing and machine learning techniques to determine the sentiment expressed in a piece of text. In this blog post, we delve into the world of sentiment analysis using Naive Bayes, a probabilistic machine learning algorithm, and explore the role of Laplacian smoothing in enhancing its performance.
Sections
Understanding Sentiment Analysis
The Naive Bayes Algorithm
The Challenge of Imbalanced Data
Understanding of Laplacian smoothing
Understanding of Laplacian smoothing with example
Benefits of Laplacian Smoothing in Sentiment Analysis
Conclusion
Section 1- Understanding Sentiment Analysis:
Sentiment analysis aims to determine the sentiment expressed in a given text, whether it’s positive, negative, or neutral. Businesses often leverage sentiment analysis to gain insights into customer opinions, enhance customer experience, and make data-driven decisions. Naive Bayes, a popular algorithm for text classification, has proven effective in sentiment analysis due to its simplicity and efficiency.
Section 2- The Naive Bayes Algorithm:
Naive Bayes is a probabilistic algorithm based on Bayes’ theorem, which calculates the probability of a hypothesis given the observed evidence. In the context of sentiment analysis, the hypothesis represents the sentiment (positive, negative, or neutral), and the evidence is the features extracted from the text.
Naive Bayes assumes that features are conditionally independent, simplifying the calculation of probabilities. Despite its “naive” assumption, Naive Bayes often performs well in practice, especially for text classification tasks like sentiment analysis.
Section 3- The Challenge of Imbalanced Data:
One common challenge in sentiment analysis is dealing with imbalanced datasets, where one sentiment class may dominate the others. This imbalance can lead to biased models that struggle to accurately predict the minority class. This is where Laplacian smoothing, also known as add-one smoothing, comes into play.
Section 4- Understanding of Laplacian smoothing
Laplacian smoothing is a technique used to handle zero probabilities in the training data. In the context of Naive Bayes, it involves adding a small constant (usually 1) to the count of each feature for each class. This prevents the multiplication of probabilities from resulting in zero, ensuring that no feature is entirely disregarded.
Laplacian smoothing helps Naive Bayes generalize better to unseen data and improves the model’s robustness.
The formula for Laplacian smoothing is as follows:
Let’s take a look, let’s now dive into a place in smoothing, a technique you can use to avoid your probabilities being zero.
The expression used to calculate the conditional probability of a word given the class is the frequency of the word in the corpus. Shown here as freak of word I comma class divided by the number of words in the corpus or end class.
Smoothing the probability function means that you will use a slightly different formula from the original. Know that I’ve added a one in the numerator. This little transformation avoids the probability being zero. However, it as a new term to all the frequencies that is not correctly normalized by end class. To account for this, you will add a new term in the denominator V class. That is the number of unique words in your vocabulary to account for that extra term added in the numerator. So now all the probabilities in each column will sum to one. This process is called Laplacian in smoothing.
Section 5- Understanding of Laplacian smoothing with example
Going back to this example, let’s use the formula on it.
The first thing you need to calculate is the number of unique words in your vocabulary. In this example you have eight unique words.
So now let’s calculate the probability for each word. And the positive class for the word I the positive class you get three plus one divided by 13 plus eight which is 0.19.
For the negative class you have three plus one divided by 12 plus eight, which is 0.2 and then so on for the rest of the table.
The number is shown here have been rounded. But using this method to sum of probabilities in your table will still be, one knows that the word because no longer has a probability of zero.
Section 6- Benefits of Laplacian Smoothing in Sentiment Analysis:
- Handling Unseen Features: Laplacian smoothing prevents the model from assigning zero probabilities to features not present in the training data, ensuring that the model can make predictions for previously unseen words.
- Reducing Overfitting: By introducing a small amount of “pseudo-counts,” Laplacian smoothing helps prevent the model from overfitting to the training data, resulting in a more generalizable model.
- Improving Minority Class Prediction: In imbalanced datasets, Laplacian smoothing assists in providing more balanced predictions by preventing the dominance of the majority class and allowing the model to give due consideration to the minority class.
Section 7- Conclusion:
Great, that’s Laplacian smoothing. Now you know why you have to use the Laplacian smoothing. So your probabilities don’t end up being zero. That’s very cool, you learned about Laplacian in smoothing and now understand why it is important to you smoothing. In the next video, you will learn about the log likelihood
Sentiment analysis using Naive Bayes, combined with Laplacian smoothing, is a powerful approach to extracting valuable insights from textual data. This combination addresses the challenges posed by imbalanced datasets and ensures that the model performs well even when faced with previously unseen words. As businesses continue to harness the power of sentiment analysis, understanding and implementing techniques like Laplacian smoothing become essential for building accurate and robust models that can uncover the true sentiments hidden within the vast sea of textual information.
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References
1- Natural Language Processing with Classification and Vector Spaces
