Statistically-constrained shallow text marking:Techniques, evaluation paradigm and results

We present three natural language marking strategies based on fast and reliable shallow parsing techniques, and on widely available lexical resources: lexical substitution, adjective conjunction swaps, and relativiser switching. We test these techniques on a random sample of the British National Corpus. Individual candidate marks are checked for goodness of structural and semantic fit, using both lexical resources, and the web as a corpus. A representative sample of marks is given to 25 human judges to evaluate for acceptability and preservation of meaning. This establishes a correlation between corpus based felicity measures and perceived quality, and makes qualified predictions. Grammatical acceptability correlates with our automatic measure strongly (Pearson's r = 0.795, p = 0.001), allowing us to account for about two thirds of variability in human judgements. A moderate but statistically insignificant (Pearson's r = 0.422, p = 0.356) correlation is found with judgements of meaning preservation, indicating that the contextual window of five content words used for our automatic measure may need to be extended. © 2007 SPIE-IS&T.

Using Brain Data for Sentiment Analysis

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.