We study the automatic detection of suggestion expressing text among the opinionated text. The examples of such suggestions in online reviews would be, customer suggestions about improvement in a commercial entity, and advice to the fellow customers. We present a qualitative and quantitative analysis of suggestions present in the text samples obtained from social media platforms. Suggestion mining from social media is an emerging research area, and thus problem definition and datasets are still evolving; this work also contributes towards the same. The problem has been formulated as a sentence classification task, and we compare the results of some popular supervised learning approaches in this direction. We also evaluate different kinds of features with these classifiers. The experiments indicate that deep learning based approaches tend to be promising for this task.

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Biosimulation researchers use a variety of models, tools and languages for capturing and processing different aspects of biological processes. However, current modeling methods do not capture the underlying semantics of the biosimulation models sufficiently to support building, reusing, composing and merging complex biosimulation models originating from diverse experiments. In this paper, we propose an ontology based and multi-layered biosimulation model to facilitate researchers to share, integrate and collaborate their knowledge bases at Web scale. In particular, we investigate the semantic biosimulation model under the context of the multi-scale finite element (FE) modelling of the inner-ear. The proposed ontologybased biosimulation model will provide a homogenized and standardized access to the shared, semantically integrated and harmonized datasets for clinical data (histological data, micro-CT images of the cochlea, pathological data) and inner ear FE simulation models. The work presented in this paper is analyzed and designed as part of the SIFEM EU project.

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