Mayukh Mukhopadhyay

Mayukh Mukhopadhyay, Sangeeta Sahney

Toxic contents in online product review are a common phenomenon. A content is perceived to be toxic when it is rude, disrespectful, or unreasonable and make individuals leave the discussion. Machine learning algorithms helps the sell side community to identify such toxic patterns and eventually moderate such inputs. Yet, the extant literature provides fewer information about the sentiment of a prospective consumer on the perception of a product after being exposed to such toxic review content. In this study, we collect a balanced data set of review comments from 18 different players segregated into three different sectors from google play-store. Then we calculate the sentence-level sentiment and toxicity score of individual review content. Finally, we use structural equation modelling to quantitatively study the influence of toxic content on overall product sentiment. We observe that comment toxicity negatively influences overall product sentiment but do not exhibit a mediating effect over reviewer score to influence sector-wise relative rating.

Mayukh Mukhopadhyay, Kaushik Ghosh

Non Fungible Token (NFT) Industry has been witnessing multi-million dollar trade in recent times. With rapid innovation of the NFT market environment by technology, innovation, and decentralization, it is becoming hard to distinguish between genuine NFT from fads and scams. This article discuss the NFT market microstructure, with a focus on price formation, market structure, transparency, and applications to other financial areas. Market manipulation in NFT market with the context of wash-sale patterns has also been surveyed. The article concludes by providing pointers on due-diligence activity that can be adopted by investors to mitigate NFT trading risk.

Mayukh Mukhopadhyay, Om Ranjan

Audio effect implementation on random musical signal is a basic application of digital signal processors. In this paper, the compatibility features of MATLAB R2008a with Code Composer Studio 3.3 has been exploited to develop Simulink models which when emulated on TMS320C6713 DSK generate real time audio effects. Each design has been done by two different asynchronous scheduling techniques: (i) Idle task Scheduling and (ii) DSP/BIOS task Scheduling. A basic COCOMO analysis has been done for the generated code to justify the industrial viability of this design approach. KEYWORDS: Musical signal processing, Real time Audio effects, Echo, Stress Generation, Reverberation, Reverberated Chorus, Real Time Scheduling.