Unsupervised Sentiment Analysis

Unsupervised sentiment analysis aims to automatically determine the emotional tone (positive, negative, or neutral) of text without relying on pre-labeled training data. Current research focuses on improving accuracy using techniques like term frequency-inverse document frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and word embedding models such as Doc2Vec, often combined with clustering algorithms like k-means or t-SNE. These methods are applied to diverse data sources, including social media posts and online discussions, to understand public opinion on various topics, demonstrating the value of unsupervised approaches for large-scale sentiment analysis where labeled data is scarce or expensive to obtain.