Improving Text Classification Accuracy via Enhanced Preprocessing and Feature Selection

Article ## Enhancing the Quality of Text Classification Through Preprocessing and Feature Selection

Abstract:

This paper investigates methodologies med at improving text classification accuracy, focusing on pre and feature selection approaches. The core goal is to enhance ' performance by refining textual input data before it is fed into a classifier.

1. Introduction

In recent years, text classification has emerged as a crucial component in the field of Processing NLP. These tasks involve categorizing documents or text segments into predefined classes based on their content. However, achieving high accuracy often necessitates careful preprocessing and feature selection processes to remove noise and irrelevant information, while retning essential context.

2. Problem Formulation

Text classification faces several challenges that impact its performance, including noisy data, ambiguity in language interpretation, large input sizes, and the curse of dimensionality. These issues can significantly reduce a model's ability to generalize effectively across different datasets and scenarios.

3. Pre

To address these challenges, we explore various preprocessing strategies med at cleaning and structuring textual data for optimal performance:

• Tokenization: Breaking down text into individual words or count allows us to handle each unit separately.

• Stop word removal: Eliminating common words like 'the', 'is' that do not carry significant meaning helps reduce noise in the dataset.

• StemmingLemmatization: Converting words to their base form simplifies feature extraction and prevents redundancy.

4. Feature Selection

Effective feature selection is vital for text classification as it allows us to identify the most relevant features while eliminating irrelevant ones:

• Term Frequency-Inverse Document Frequency TF-IDF: Measures the importance of each term in a document by considering its frequency within the document and rarity across multiple documents.

• Word Embeddings: Techniques like Word2Vec or GloVe capture semantic relationships between words, providing richer representations for features.

5. Experimental Setup

We conduct experiments using standard text classification datasets to evaluate the performance improvements after applying our pre and feature selection methods:

• Datasets: Sentiment Analysis IMDb Reviews, Topic Classification Reuters Corpus Volume 1 2.

6. Results and Discussions

Our results demonstrate that proper preprocessing and feature selection significantly enhance the accuracy of in text classification tasks. Notably, incorporating TF-IDF with word embeddings yields the best performance across various experiments.

In , this study underscores the critical role of effective pre and feature selection strategies in enhancing the quality of text classification outcomes. By carefully preparing textual data for , we can achieve higher accuracy and robustness, making them better suited to real-world applications.

Keywords: Text Classification, Pre, Feature Selection, Processing
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