MACHINE LEARNING TEXT ANALYZER – TEXT CLASSIFICATION USING SUPERVISED AND UN-SUPERVISED ALGORITHMS

MACHINE LEARNING TEXT ANALYZER – TEXT CLASSIFICATION USING SUPERVISED AND UN-SUPERVISED ALGORITHMS

Abstract:
Machine learning techniques have revolutionized the field of natural language processing, enabling the development of advanced text analysis systems. Text classification, a fundamental task in natural language processing, involves assigning predefined categories or labels to textual data based on its content. This paper presents a Machine Learning Text Analyzer that utilizes both supervised and unsupervised algorithms for text classification.

The supervised approach leverages labeled training data to build a predictive model capable of categorizing unseen text. Various supervised algorithms, such as Naive Bayes, Support Vector Machines (SVM), and Random Forest, are employed to train the classification model. These algorithms learn from the labeled data’s features and patterns, enabling accurate classification of new text samples.

In contrast, the unsupervised approach does not require labeled data. It employs clustering algorithms, such as K-means and hierarchical clustering, to group similar texts based on their intrinsic properties. The unsupervised approach discovers hidden patterns and structures within the text data, allowing for exploratory analysis and identification of topics or themes.

The proposed Machine Learning Text Analyzer combines the strengths of both supervised and unsupervised approaches. It first employs unsupervised clustering algorithms to gain insights into the data and discover potential classes or categories. Subsequently, the supervised algorithms are applied to build a robust classification model using the labeled data. This hybrid approach benefits from the unsupervised analysis by providing initial knowledge and structure, which enhances the performance of the subsequent supervised classification.

The effectiveness of the Machine Learning Text Analyzer is evaluated using real-world text datasets. Experimental results demonstrate its ability to accurately classify text documents into relevant categories, achieving high precision and recall scores. Moreover, the system exhibits flexibility and adaptability, as it can be trained on diverse domains and can handle large-scale text datasets.

The Machine Learning Text Analyzer presented in this paper opens up possibilities for various applications, such as sentiment analysis, document categorization, spam detection, and content recommendation. By combining supervised and unsupervised algorithms, the system provides an efficient and effective solution for text classification tasks, enabling users to extract valuable insights from textual data with improved accuracy and efficiency.

MACHINE LEARNING TEXT ANALYZER – TEXT CLASSIFICATION USING SUPERVISED AND UN-SUPERVISED ALGORITHMS. GET MORE  COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS

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