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In this age of natural language processing, most of the sentiment analysis tasks are done by polarization, for example, 0 for negative or 1 for positive of the given context/text. In some work, the tasks are done using fine-grained polarization, such as very negative or very positive. The proposed system of this paper includes the categorization of the paragraphs using its nature. All the paragraphs are self-narrated, and the number of words in those self-narrated paragraphs contains 50–4200 words. The paragraphs are categorized using three categorizations: “work stress,” “bullying,” and “sexual harassment” in both real and cyber worlds. Artificial neural network paragraph vectors, a distributed bag-of-words and distributed memory, are used to get the embedding of each paragraph and later for classification by data mining techniques. The accuracy of each algorithm lies between 70 and 94%. The best model gives a 77.46% F1 score in the test set.

This work reports the potentiality of the motor imagery movement classification from prefrontal hemodynamics for the brain–computer interface (BCI) applications. Although movement-related activation correlates with the central lobe, this area of a paralyzed patient is often found obsolete. Therefore, to design a BCI system for paralyzed persons, the central lobe hemodynamics cannot be considered. To overcome this problem, this work proposed an alternative approach. This research work experimentally investigates the potentiality of classifying the motor planning (imagery) activities from the prefrontal hemodynamics. The functional changes of prefrontal hemodynamics for imagery hand movements are measured by functional near-infrared spectroscopy (fNIRS) from several subjects. The fNIRS signals of imagery hand movements are classified by a k-nearest neighbor and artificial neural network algorithms. The classification accuracies were checked with the subject dependent and independent approach. Our results demonstrate that the prefrontal hemodynamics could serve as the potential biomarker for the effective BCI system.