Unsupervised domain-agnostic identification of product names in social media posts

TL;DR

This paper introduces an unsupervised, domain-agnostic method for identifying product names in social media posts, reducing the need for retraining across different product domains.

Contribution

It presents a novel two-step algorithm combining pretrained models, pattern matching, clustering, and word embeddings for product name recognition without domain-specific supervision.

Findings

Effective in identifying product names across diverse domains

Reduces dependency on labeled training data

Applicable to social media and unstructured text

Abstract

Product name recognition is a significant practical problem, spurred by the greater availability of platforms for discussing products such as social media and product review functionalities of online marketplaces. Customers, product manufacturers and online marketplaces may want to identify product names in unstructured text to extract important insights, such as sentiment, surrounding a product. Much extant research on product name identification has been domain-specific (e.g., identifying mobile phone models) and used supervised or semi-supervised methods. With massive numbers of new products released to the market every year such methods may require retraining on updated labeled data to stay relevant, and may transfer poorly across domains. This research addresses this challenge and develops a domain-agnostic, unsupervised algorithm for identifying product names based on Facebook posts. The algorithm consists of two general steps: (a) candidate product name identification using an off-the-shelf pretrained conditional random fields (CRF) model, part-of-speech tagging and a set of simple patterns; and (b) filtering of candidate names to remove spurious entries using clustering and word embeddings generated from the data.