Abstract
Online product reviews are exploring on e-commerce platforms, and mining aspect-level product information contained in those reviews has great economic benefit. The aspect category classification task is a basic task for aspect-level sentiment analysis which has become a hot research topic in the natural language processing (NLP) field during the last decades. In various e-commerce platforms, there emerge various user-generated question-answering (QA) reviews which generally contain much aspect-related information of products. Although some researchers have devoted their efforts on the aspect category classification for traditional product reviews, the existing deep learning-based approaches cannot be well applied to represent the QA-style reviews. Thus, we propose a 4-dimension (4D) textual representation model based on QA interaction-level and hyperinteraction-level by modeling with different levels of the text representation, i.e., word-level, sentence-level, QA interaction-level, and hyperinteraction-level. In our experiments, the empirical studies on datasets from three domains demonstrate that our proposals perform better than traditional sentence-level representation approaches, especially in the Digit domain.
Highlights
Nowadays, Internet finance has evolved rapidly, and e-commerce platform occupies the central position in its development by providing virtual payment means
Since the word-level and sentencelevel dimension representation have been widely studied in the natural language processing (NLP) area, we use approaches based on the two dimensions as our baseline methods
(2) Clearly, among all 2D approaches, when the question and answer texts are both employed in our task, Q + A(2D) approaches perform best. is means that we can utilize auxiliary information contained in answer texts to further improve the performance of the aspect category classification task on QA-style reviews
Summary
Internet finance has evolved rapidly, and e-commerce platform occupies the central position in its development by providing virtual payment means. In the virtual trading environment created by the e-commerce platform, both parties can realize communication on time by online product reviews. The content of those reviews can affect the business of the e-commerce platform, spreading to the stability of the whole Internet finance. On these grounds, it is very important to mine valuable information contained in those reviews, which can help consumers make purchase decisions and help organizations know customer satisfaction and make adjustment strategies. It is difficult for us to manually collect and manipulate these texts. us, sentiment analysis comes into being
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