Aspect-Based Sentiment Analysis of Tweets Using Independent Component Analysis (ICA) and Probabilistic Latent Semantic Analysis (pLSA)

Abstract

Twitter is an ocean of diverse topics while sentiment classifier is always limited to a specific domain or topic. Twitter lacks data labeling and a mechanism to acquire sentiment labels. Sentiment words extracted from the twitter are generalized. It is important to find the correct sentiment from a tweet; otherwise, it may generate different sentiment than the desired. Sentiment analysis work done up to now has limitation, i.e., it is based on predefined lexicons. Sentiment of a word based on this lexicon is not generalized. State of art of our work suggests a solution that will make sentiment analysis based on the sentence sentiments and not just only based on predefined lexicons. In this paper, we propose a framework to analyze sentiment from the sentence, by applying independent component analysis (ICA) in coordination with probabilistic latent semantic analysis. We view pLSA as word categorization technique based on some topics, wherein a given corpus is split among different topics. We further utilize these topics for tagging sentiment with the help of ICA, in this way, we are able to assign more accurate sentiment of a sentence than the existing approaches. The proposed work is efficient as its outcome is more precise and accurate than the lexicons-based sentiment analysis. With adequate unsupervised machine learning training, accurate outcomes with a normal precision rate of 77.98% are accomplished.