Using Sentiment Representation Learning to Enhance Gender Classification for User Profiling

TL;DR

This paper introduces a sentiment representation learning approach using microblog posts to improve gender classification accuracy, achieving a 5.53% increase over baseline methods.

Contribution

The study proposes a novel Sentiment Representation Learning based MLP model that leverages sentiment features for enhanced gender prediction in user profiling.

Findings

Sentiment features significantly improve gender classification accuracy.

The proposed model outperforms baseline methods by 5.53%.

Sentiment context is valuable for demographic user profiling.

Abstract

User profiling means exploiting the technology of machine learning to predict attributes of users, such as demographic attributes, hobby attributes, preference attributes, etc. It's a powerful data support of precision marketing. Existing methods mainly study network behavior, personal preferences, post texts to build user profile. Through our data analysis of micro-blog, we find that females show more positive and have richer emotions than males in online social platform. This difference is very conducive to the distinction between genders. Therefore, we argue that sentiment context is important as well for user profiling.This paper focuses on exploiting microblog user posts to predict one of the demographic labels: gender. We propose a Sentiment Representation Learning based Multi-Layer Perceptron(SRL-MLP) model to classify gender. First we build a sentiment polarity classifier in advance by training Long Short-Term Memory(LSTM) model on e-commerce review corpus. Next we transfer sentiment representation to a basic MLP network. Last we conduct experiments on gender classification by sentiment representation. Experimental results show that our approach can improve gender classification accuracy by 5.53\%, from 84.20\% to 89.73\%.