iSA: a fast, scalable and accurate algorithm for sentiment analysis of social media content
Information Sciences (Journal IF: 4.038)
Co-authors: Luigi Curini & Stefano M. Iacus
Replication material: www.sciencedirect.com
What is worth remembering:
- The new algorithm iSA for sentiment/opinion analysis is presented
- iSA is fast, scalable, accurate and language independent
- iSA is stable if the number of classes/opinions is large and allows for cross-tabulation
- iSA works in the case of random and non-random sampling
We present iSA (integrated Sentiment Analysis), a novel algorithm designed for social networks and Web 2.0 sphere (Twitter, blogs, etc.) opinion analysis, i.e. developed for the digital environments characterized by abundance of noise compared to the amount of information. Instead of performing an individual classification and then aggregate the predicted values, iSA directly estimates the aggregated distribution of opinions. Based on supervised hand-coding rather than NLP techniques or ontological dictionaries, iSA is a language-agnostic algorithm (based on human coders’ abilities). iSA exploits a dimensionality reduction approach which makes it scalable, fast, memory efficient, stable and statistically accurate. The cross-tabulation of opinions is possible with iSA thanks to its stability. Through empirical analysis 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 known as ReadMe.
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