Cascading Classifiers for Twitter Sentiment Analysis with Emotion Lexicons
Many different attempts have been made to determine sentiment polarity in tweets, using emotion lexicons and different NLP techniques with machine learning. In this paper we focus on using emotion lexicons and machine learning only, avoiding the use of additional NLP techniques. We present a scheme that is able to outperform other systems that use both natural language processing and distributional semantics. Our proposal consists on using a cascading classifier on lexicon features to improve accuracy. We evaluate our results with the TASS 2015 corpus, reaching an accuracy only 0.07 below the top ranked system for task 1, 3 levels, whole test corpus. The cascading method we implemented consisted on using the results of a first stage classification with Multinomial Naive Bayes as additional columns for a second stage classification using a Naive Bayes Tree classifier with feature selection. We tested with at least 30 different classifiers and this combination yielded the best results.
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