Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts

TL;DR

This study demonstrates that unsupervised NLP techniques can effectively analyze social media posts about plastic surgery, achieving nearly 90% accuracy in sentiment prediction without labeled data.

Contribution

It introduces an unsupervised approach using simple deep learning models and clustering techniques to classify social media posts by sentiment, outperforming basic supervised methods.

Findings

Unsupervised models achieved almost 90% sentiment prediction accuracy.

Unsupervised learning outperformed rudimentary supervised classification.

Techniques applied include TF-IDF, t-SNE, k-means, and LDA.

Abstract

The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases based on the sheer volume and velocity of textual data. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. Using a word ranking method, term frequency-inverse document frequency (TF-IDF), to create features across documents, it is possible to perform unsupervised analytics, machine learning (ML) that can group the documents without a human manually labeling the data. For large datasets with thousands of features, t-distributed stochastic neighbor embedding (t-SNE), k-means clustering and Latent Dirichlet allocation (LDA) are employed to learn top words and generate topics for a Reddit and Twitter combined corpus. Using extremely simple deep learning models, this study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery based on a tweet or subreddit post with almost 90% accuracy. Furthermore, the model is capable of achieving higher accuracy on the unsupervised sentiment task than on a rudimentary supervised document classification task. Therefore, unsupervised learning may be considered a viable option in labeling social media documents for NLP tasks.