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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This paper presents a continuous-time zoom ADC for audio applications. It combines a 4-bit noise-shaping coarse SAR ADC and a fine delta-sigma modulator with a tail-resistor linearized OTA for improved linearity, energy efficiency, and handling of out-of-band interferers compared to previous designs. In 160 nm CMOS, the prototype chip occupies 0.36 mm 2 , achieves 107.2 dB SNR, 106.6 dB SNDR, and 107.3 dB dynamic range in a 24 kHz bandwidth while consuming 590 µW from a 1.8 V supply. This translates into a Schreier figure-ofmerit (FoMS) of 183.4 dB and a FoMSNDR of 182.7 dB. Keywords-A/D conversion, audio analog to digital converter (ADC), continuous-time delta-sigma ADC, dynamic zoom ADC, low-power circuits, noise-shaping SAR ADC, high linearity operational transconductance amplifier (OTA)

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