Using Brain Data for Sentiment Analysis

Yuqiao Gu, Fabio Celli, Josef Steinberger, Andrew James Anderson, Massimo Poesio, Carlo Strapparava, Brian Murphy

Research output: Contribution to journalArticlepeer-review


We present the results of exploratory experiments using lexical valence extracted from brain using electroencephalography (EEG) for sentiment analysis. We selected 78 English words (36 for training and 42 for testing), presented as stimuli to 3 English native speakers. EEG signals were recorded from the subjects while they performed a mental imaging task for each word stimulus. Wavelet decomposition was employed to extract EEG features from the time-frequency domain. The extracted features were used as inputs to a sparse multinomial logistic regression (SMLR) classifier for valence classification, after univariate ANOVA feature selection. After mapping EEG signals to sentiment valences, we exploited the lexical polarity extracted from brain data for the prediction of the valence of 12 sentences taken from the SemEval-2007 shared task, and compared it against existing lexical resources.
Original languageEnglish
Pages (from-to)79-94
Number of pages16
JournalJournal for Language Technology and Computational Linguistics
Issue number1
Publication statusPublished - 2014


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