ISA: A FAST, SCALABLE AND ACCURATE ALGORITHM FOR SUPERVISED OPINION ANALYSIS

摘要

We present iSA (integrated Sentiment Analysis), a novel algorithm designed for social networks and Web 2.0 sphere (Twitter, blogs, etc.) opinion analysis. Instead of working on individual classification and then aggregating the estimates, iSA estimates directly the aggregated distribution of opinions. Not being based on NLP techniques or ontological dictionaries but on supervised hand-coding, iSA is a language agnostic algorithm (up to human coders' ability). iSA exploits a dimensionality reduction approach which makes it scalable, fast, memory efficient, stable and statistically accurate. Cross-tabulation of opinions is possible with iSA thanks to its stability. It will be shown when iSA outperforms machine learning techniques of individual classification (e.g. SVM, Random Forests, etc.) as well as the only other alternative for aggregated sentiment analysis like ReadMe.