Boosting Text Classification Efficiency with N gram Features in Naive Bayes Models

Enhancing the Efficiency of a Text Classification System Using N-gram Features

Abstract:

The efficiency and accuracy of text classification systems are crucial for various applications, including sentiment analysis, topic categorization, and spam detection. This research improve such systems by employing n-gram features in conjunction with the Nve Bayes algorithm. N-grams are contiguous sequences of elements words or characters from a given sample sequence of data or text.

The Nve Bayes classifier is chosen due to its simplicity and effectiveness, particularly when dealing with high-dimensional datasets like those encountered in text classification tasks. By incorporating n-gram features into the model, we significantly enhance its performance by leveraging patterns within textual data that are not captured by single-word features alone.

:

A dataset was collected from diverse online sources to encompass a broad range of text types and sentiments, providing a robust foundation for our experiments. involves extracting n-grams unigrams, bigrams, trigrams, etc. from the input texts, which are then used as features in the Nve Bayes classifier.

Implementation:

We first preprocess the raw data to ensure consistency in text representation, including tokenization, removal of stop words, and stemming or lemmatization. Following this, n-grams are based on specific parameters n=1,2,3. Each document is then represented as a vector where each dimension corresponds to an n-gram present in the vocabulary.

The Nve Bayes algorithm utilizes these features for classification. It calculates the probability of each class given the feature set and assigns documents to classes accordingly. This step involves estimating the prior probabilities of each class and conditional probabilities of each feature n-grams within each class.

Evaluation:

To assess the system's performance, we use common metrics such as accuracy, precision, recall, F1-score, and confusion matrices. The dataset is typically split into trning and testing sets for this purpose. By varying n-values during experimentation, we identify the optimal balance between model complexity and performance.

Results:

s demonstrate that using N-gram features significantly boosts classification accuracy compared to traditional unigram-based approaches. This enhancement is particularly noticeable in scenarios with a high degree of text diversity and complexity.

:

Our study underscores the effectiveness of incorporating n-gram features into Nve Bayes-based text classification systems. By enhancing feature representation, these methods can achieve superior performance metrics while mntning computational efficiency. The findings contribute valuable insights for practitioners looking to optimize their text analysis tools.

Keywords: Text Classification, N-Gram Features, Nve Bayes Classifier
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