Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts
Alexandrea K. Ramnarine
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.