Understanding Aspects in Sentiment Analysis

Oct 13, 2025 by talynt.org 85 views

Shoe Store Recommendations on the Marketplace Based on Aspect-Based Sentiment Analysis on Product Reviews Using the Support Vector Machine Algorithm

Introduction

In today's digital age, online shopping has become a norm, and the marketplace application has made it easier for consumers to purchase products, including shoes, from the comfort of their own homes. However, online shopping also has its own set of challenges that can lead to buyer disappointment. One way to alleviate this issue is by providing buyers with accurate and reliable product reviews from various stores. However, the sheer volume of reviews can be overwhelming, making it difficult for buyers to find the right information.

This study aims to address this issue by conducting an aspect-based sentiment analysis using the Support Vector Machine (SVM) algorithm and TF-IdF (Terquency-Inverse Document Frequency) extraction features on shoe product reviews taken from the Tokopedia application. The study examines various aspects, including prices, quality of goods, shipping, and services, to provide buyers with accurate and reliable recommendations.

Understanding Aspects in Sentiment Analysis

Sentiment analysis based on aspects is a method for analyzing and categorizing consumers' feelings towards various aspects of the product. This approach allows us to understand the buyer's opinion not only in general but also in the context of specific elements of their shopping experience. For instance, a positive review of the quality of shoes can indicate that customers are satisfied with the material used, while negative reviews may reflect dissatisfaction about the shipping process.

Aspect-based sentiment analysis is particularly useful in the context of online shopping, where buyers often have specific needs and preferences. By analyzing reviews based on aspects, buyers can quickly identify the pros and cons of a particular product or store, making it easier for them to make informed decisions.

The Role of Support Vector Machine and TF-IdF

In this study, the Support Vector Machine (SVM) algorithm was used to classify the sentiments of the review. SVM is a highly effective machine learning method in data classification, including text analysis. The algorithm works by identifying the optimal hyperplane that separates the data into different classes, in this case, positive and negative sentiments.

Meanwhile, the TF-IdF method functions to extract the features of text data, measure the importance of words in the context of the document as a whole. The combination of SVM and TF-IdF results in a satisfying level of accuracy, reaching 86.87%. This high accuracy rate ensures that buyers can rely on the recommendations provided by the system.

Advantages for Buyers

With this aspect-based sentiment analysis, buyers can easily find the right shoe store recommendations based on their needs. For example, if a buyer looks for shoes with good quality and fast shipping, they can quickly identify the reviews that match their criteria. This not only saves time but also increases customer satisfaction with more informed decisions.

Moreover, the system provides buyers with a comprehensive understanding of the product and store, including the pros and cons of each aspect. This allows buyers to make more informed decisions and avoid disappointment.

Conclusion

The use of aspect-based sentiment analysis with the Support Vector Machine algorithm and the TF-IDF method is a valuable tool for providing useful recommendations for shoe buyers in the Marketplace. With high accuracy, this method can support buyers in making better decisions, thereby reducing the risk of disappointment in online shopping experiences.

The benefits of this approach extend beyond the buyers, as it also provides sellers with valuable insights into their products and services. By analyzing the sentiments of reviews based on aspects, sellers can identify areas for improvement and make data-driven decisions to enhance their offerings.

Recommendations for Future Research

While this study provides a comprehensive analysis of aspect-based sentiment analysis using the Support Vector Machine algorithm and TF-IdF method, there are several areas for future research. These include:

Expanding the scope of the study: The study focuses on shoe product reviews, but the approach can be applied to other product categories, such as clothing, electronics, and home goods.
Improving the accuracy of the system: While the system achieves a high accuracy rate, there is always room for improvement. Future research can focus on refining the algorithm and incorporating additional features to enhance the accuracy of the system.
Developing a user-friendly interface: The system provides valuable recommendations, but the user interface can be improved to make it more user-friendly and accessible to a wider audience.

By addressing these areas, future research can further enhance the effectiveness of aspect-based sentiment analysis in providing valuable recommendations for buyers and sellers in the Marketplace.

Limitations of the Study

While this study provides a comprehensive analysis of aspect-based sentiment analysis using the Support Vector Machine algorithm and TF-IdF method, there are several limitations to the study. These include:

Limited dataset: The study uses a limited dataset of shoe product reviews from the Tokopedia application. Future research can focus on expanding the scope of the study to include a larger dataset.
Simplistic approach: The study uses a simplistic approach to aspect-based sentiment analysis, which may not capture the complexity of human emotions and opinions. Future research can focus on developing more sophisticated approaches to aspect-based sentiment analysis.

By acknowledging these limitations, future research can build upon the findings of this study and provide a more comprehensive understanding of aspect-based sentiment analysis in the context of online shopping.

Conclusion

In conclusion, this study demonstrates the effectiveness of aspect-based sentiment analysis using the Support Vector Machine algorithm and TF-IdF method in providing valuable recommendations for shoe buyers in the Marketplace. With high accuracy, this method can support buyers in making better decisions, thereby reducing the risk of disappointment in online shopping experiences. The benefits of this approach extend beyond the buyers, as it also provides sellers with valuable insights into their products and services. By addressing the limitations of the study and expanding the scope of the research, future studies can further enhance the effectiveness of aspect-based sentiment analysis in providing valuable recommendations for buyers and sellers in the Marketplace.
Frequently Asked Questions (FAQs) on Shoe Store Recommendations Using Aspect-Based Sentiment Analysis

Q: What is aspect-based sentiment analysis?

A: Aspect-based sentiment analysis is a method for analyzing and categorizing consumers' feelings towards various aspects of a product. In the context of online shopping, this approach allows buyers to understand the opinions of other customers based on specific elements of their shopping experience, such as prices, quality of goods, shipping, and services.

Q: How does the Support Vector Machine (SVM) algorithm work in aspect-based sentiment analysis?

A: The SVM algorithm is a machine learning method that classifies the sentiments of reviews based on the features extracted from the text data. In this study, the SVM algorithm was used to classify the sentiments of reviews as positive or negative, and the TF-IdF method was used to extract the features of the text data.

Q: What is the significance of TF-IdF in aspect-based sentiment analysis?

A: TF-IdF is a method for extracting the features of text data and measuring the importance of words in the context of the document as a whole. In this study, the TF-IdF method was used to extract the features of the text data and measure the importance of words in the context of the review.

Q: How does the system provide recommendations to buyers?

A: The system provides recommendations to buyers by analyzing the sentiments of reviews based on aspects, such as prices, quality of goods, shipping, and services. Buyers can input their specific needs and preferences, and the system will provide them with a list of recommended shoe stores that match their criteria.

Q: What are the benefits of using aspect-based sentiment analysis in online shopping?

A: The benefits of using aspect-based sentiment analysis in online shopping include:

Improved accuracy: Aspect-based sentiment analysis provides more accurate recommendations to buyers, reducing the risk of disappointment in online shopping experiences.
Increased customer satisfaction: By providing buyers with a comprehensive understanding of the product and store, aspect-based sentiment analysis increases customer satisfaction with more informed decisions.
Data-driven decision-making: Aspect-based sentiment analysis provides sellers with valuable insights into their products and services, enabling them to make data-driven decisions to enhance their offerings.

Q: What are the limitations of the study?

A: The limitations of the study include:

Limited dataset: The study uses a limited dataset of shoe product reviews from the Tokopedia application.
Simplistic approach: The study uses a simplistic approach to aspect-based sentiment analysis, which may not capture the complexity of human emotions and opinions.

Q: How can the system be improved?

A: The system can be improved by:

Expanding the scope of the study: The study focuses on shoe product reviews, but the approach can be applied to other product categories, such as clothing, electronics, and home goods.
Improving the accuracy of the system: While the system achieves a high accuracy rate, there is always room for improvement. Future research can focus on refining the algorithm and incorporating additional features to enhance the accuracy of the system.
Developing a user-friendly interface: The system provides valuable recommendations, but the user interface can be improved to make it more user-friendly and accessible to a wider audience.

Q: What are the future research directions?

A: The future research directions include:

Developing a more sophisticated approach to aspect-based sentiment analysis: Future research can focus on developing more sophisticated approaches to aspect-based sentiment analysis, such as incorporating additional features and refining the algorithm.
Expanding the scope of the study: Future research can focus on expanding the scope of the study to include a larger dataset and more product categories.
Developing a user-friendly interface: Future research can focus on developing a user-friendly interface that makes the system more accessible to a wider audience.