A domain transferable lexicon set for Twitter sentiment analysis using a supervised machine learning approach

This research hierarchically reduce the feature set to a small set of seven “meta features” to reduce sparsity and shows that TSA based on these features can produce highly accurate results as measured by recall, precision, and F1 metrics.

Abstract

The Twitter messaging service has become a platform for customers and news consumers to express sentiments. Accurately capturing these sentiments has been challenging for researchers. The traditional approaches to Twitter Sentiment Analysis (TSA) include dictionary-based and use supervised machine learning tools for sentiment classification. This research follows the supervised machine learning approach. A major challenge for the machine learning approach is feature selection, which is often domain dependent. We address this specific challenge and present a novel approach to identify a lexicon set unique to TSA. We show that this Twitter Specific Lexicon Set (TSLS) is small, and most importantly, is domain transferable. This identification process generates a collection of vectorized tweets for input to machine learning tools. In traditional approaches, this vectorization often results in a highly sparse input matrix which produces low accuracy measures. In this research, we hierarchically reduce the feature set to a small set of seven “meta features” to reduce sparsity. We show that TSA based on these features can produce highly accurate results using a dynamic architecture for neural networks (DAN2) and SVM (machine learning tools) as measured by recall, precision, and F1 metrics (the harmonic average of precision and recall). Our results show that a Twitter Generic Feature Set (TGFS) derived from two datasets (@JustinBieber and @Starbucks) is domain transferable and when combined with only a few Twitter Domain Specific Features (TDSF) (less than 3%), can produce excellent sentiment classification values. We evaluate the effectiveness and transferability of the TGFS across three new and distinct domains (@GovChristie, @SouthwestAir, and @VerizonWireless).