Using Brain Data for Sentiment Analysis

Abstract 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. 1 Introduction and related work Sentiment analysis-automatically recognizing the emotions conveyed by a text, and in particular distinguishing positive from negative valence-has become one of the most popular research areas in computational linguistics The focus of the preliminary investigation discussed in this paper was primarily practical: to address one of the issues that have to be faced in order to achieve the ultimate goal. The problem is that the cost of collecting valence information through fMRI or MEG would be prohibitive at present. On the other hand, EEG is a very inexpensive and widespread technology. The structure of the paper is as follows. First of all we describe the paradigm in general terms. Next we discuss how we used a linguistically controlled data set of word stimuli to elicit EEG data about valence and to train a within-subjects valence classifier which was then used to assign valence to words in the test set. Finally, we discuss preliminary experiments using this valence for sentiment analysis. 2 Methodology A number of issues need to be tackled in order to use brain data to determine the valence of words. The first problem, already mentioned, is that fMRI as used by Cato et al is very expensive (the costs are in the order of €500 per hour) and requires substantial medical infrastructure. As already mentioned, our solution to this problem was to use EEG, which costs substantially less and is becoming a standard facility also in Computer Science and Psychology labs. But even using EEG, it is not possible to get the valence of each word directly from subjects. Generally at least 5-6 presentations of a stimulus (word) to each subject are needed to get a stable representation of the signal for that stimulus and that subject. At a few seconds per stimulus, at most 80 stimuli can be presented to a subject in one hour-the duration of time after which the subject's attention generally is lost. This makes it time-consuming to measure brain activity for even the relatively small number of words in a standard corpus. Creating an EEG-based sentiment dictionary would require multiple sessions for multiple participants. In these experiments we used a test subset of the corpus created for the Sentiment Analysis at SemEval-2007 81 Using Brain Data for Sentiment Analysis tators. Annotation was performed using a web-based interface that displayed one headline at a time, together with a slide bar for valence assignment. The interval for the valence annotations was set from -100 to 100, where 0 represents a neutral headline, -100 represents a highly negative headline and 100 corresponds to a highly positive headline. We selected only positive or negative sentences, not neutral ones. The inter-annotator agreement for the sentiment polarity is 0.78 In order to address the problem mentioned above we proceeded as follows. First of all we specified a training dataset consisting of 36 stimuli-12 positive, 12 negative, and 12 neutral-from behavioral norms 82 Last but not least, there is the problem of achieving a good performance on determining predicted valence. The performance of EEG at lexical information 3 Using machine learning to decode and predict the valence of English words from EEG data In this Section we discuss how we used EEG to decode the emotional valence of English words. EEG experiment and data preprocessing Materials. Previous wor