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Aspect-based opinion mining (ABOM) has emerged as a crucial task in natural language processing (NLP) aimed at extracting fine-grained opinions about specific aspects of products, services or entities from textual data. With the exponential growth of user-generated content on the Internet, such as product reviews, social media posts and forum discussions, understanding the opinions expressed towards different aspects has become increasingly important for businesses, researchers and consumers alike. Traditional sentiment analysis approaches often treat the entire document or sentence as a unit, neglecting the fact that opinions are often expressed towards specific aspects or features. However, ABOM goes beyond overall sentiment analysis by identifying the aspects being discussed and extracting opinions related to each aspect individually. This granular analysis provides deeper insights into the strengths and weaknesses of products or services, enabling businesses to make informed decisions and enhancing user experiences. In natural language processing, aspect-based opinion mining is an essential task that seeks to analyse opinions stated in text documents about particular qualities or aspects of entities, products or services. Organisations and customers may make better decisions with the aid of the customer-based summary produced from the identified aspect words. The main goal of this work is to estimate the polarity of aspect terms in a given textual collection. Sentiment polarity estimation is needed for a large number of samples or reviews in the dataset. In this paper, the approach of deep memory network is utilised. When assessing the sentiment polarity of an aspect, the deep memory network technique explicitly takes into account the significance of each context word. Deep memory networks are able to store and make use of historical context, which facilitates a more sophisticated comprehension of the connections among various aspects and opinions in the reviews. They also enhance the recognition and categorisation of emotions linked to particular features. Experiments are conducted on laptop and restaurant datasets and the performance of the model is then evaluated with different classifiers.

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An Approach to Aspect-Based Opinion Mining Using Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Shilpi Gupta,
  • Niraj Singhal,
  • Pradeep Kumar

摘要

Aspect-based opinion mining (ABOM) has emerged as a crucial task in natural language processing (NLP) aimed at extracting fine-grained opinions about specific aspects of products, services or entities from textual data. With the exponential growth of user-generated content on the Internet, such as product reviews, social media posts and forum discussions, understanding the opinions expressed towards different aspects has become increasingly important for businesses, researchers and consumers alike. Traditional sentiment analysis approaches often treat the entire document or sentence as a unit, neglecting the fact that opinions are often expressed towards specific aspects or features. However, ABOM goes beyond overall sentiment analysis by identifying the aspects being discussed and extracting opinions related to each aspect individually. This granular analysis provides deeper insights into the strengths and weaknesses of products or services, enabling businesses to make informed decisions and enhancing user experiences. In natural language processing, aspect-based opinion mining is an essential task that seeks to analyse opinions stated in text documents about particular qualities or aspects of entities, products or services. Organisations and customers may make better decisions with the aid of the customer-based summary produced from the identified aspect words. The main goal of this work is to estimate the polarity of aspect terms in a given textual collection. Sentiment polarity estimation is needed for a large number of samples or reviews in the dataset. In this paper, the approach of deep memory network is utilised. When assessing the sentiment polarity of an aspect, the deep memory network technique explicitly takes into account the significance of each context word. Deep memory networks are able to store and make use of historical context, which facilitates a more sophisticated comprehension of the connections among various aspects and opinions in the reviews. They also enhance the recognition and categorisation of emotions linked to particular features. Experiments are conducted on laptop and restaurant datasets and the performance of the model is then evaluated with different classifiers.