Abstract:
On social media, people could share information
related to their desire to purchase, sell, or consume products or
services, which serves as a marketplace for C2C e-Commerce.
However, the message post by the social media users will not reach
the potential buyer/seller out of your followers’ circle. Furthermore,
due to the difficulties of interpreting the semantics of social media
posts, extracting product attribution from them is also difficult. To
fix these issues, our research proposes a framework for extracting
product attributes from microblogging messages about product
selling and buying in this paper. First, we use a hybrid approach that
includes Knowledge Base (KB), rule-based, Conditional Random
Field (CRF), and Logistic Regression to extract the semantics of
messages using named entity recognition. The dataset was created
using raw social media messages, product descriptions from ecommerce sites, and KB because there was no product attribute
annotated training dataset. When applied to a real-world dataset,
the proposed approach achieves high accuracy, with classification
and CRF models achieving 95 and 82 percent accuracy, respectively.
