Aspect Based Multi Classification for Text Mining Using Neural Attention Model

Title

Aspect Based Multi Classification for Text Mining Using Neural Attention Model

Creator

Nagendra, N

Contributor

J, Chandra

Description

Aspect-based text classification is crucial for multi-classification in e- commerce, including diverse sectors like food, online shopping, and restaurants. Traditional research often focuses on a few classes and domains, such as restaurants or electronics, and overlooks the need to categorize sentences based on domain- specific contexts. However, e-commerce involves numerous domains that require more sophisticated classification methods. E-commerce platforms generate vast amounts of textual data, including comments, product descriptions, and customer reviews, which contain valuable information about various aspects of products or services. Since customers often research product reviews from multiple sources before purchasing, these reviews become essential user-generated content for e-commerce businesses. To address this gap, the Aspect-Based Neural Attention Model (ABNAM) was developed. ABNAM enhances classification's accuracy and comprehensiveness by considering each domain's unique characteristics. This leads to better categorization and provides more relevant insights for businesses operating across various e- commerce sectors. Experimental real-world data results demonstrate that ABNAM identifies more meaningful and coherent features. It significantly outperforms other methods by achieving higher accuracy, better recall and precision, and more robust performance across different datasets. The current research introduces an efficient and innovative sentence classification model using ABNAM. Unlike traditional automated text classification models, which struggle to categorize data into sixteen classes, ABNAM excels by leveraging technologies such as TF-IDF, N-Gram, Convolutional Neural Networks (CNN), Linear Support Vector Machines (SVM), Random Forest, and Nae Bayes. Among these methods, ABNAM achieved the highest accuracy at 97%, successfully classifying sentences into one of the sixteen categories. The research positions ABNAM as a novel and highly effective classification model, particularly in achieving high-class categorizations.

Source

Author's Submission

Date

2024-01-01

Publisher

Christ(Deemed to be University)

Subject

Data Science

Rights

Open Access

Relation

61000393

Format

PDF

Language

English

Type

PhD

Identifier

http://hdl.handle.net/10603/598702