Sayan Saha

Work place: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India, 632014



Research Interests: Data Structures and Algorithms, Digital Library, Computer Architecture and Organization


Sayan Saha is from New Delhi, India. He is a B. Tech final year Computer Science and Engineering student from Vellore Institute of Technology, Vellore. He has interned with DRDO, India in 2019, Texas Instruments (NSIT) in 2018, and TATA Power in 2020 and is currently working in TeejLab Inc. as a Software and Services Developer. He has won hackathons organized by Google Developers and MLH. He has been also selected as an HPAIR delegate and has been part of the Schneider Go Green Challenge Greater India Team. He is interested in Blockchain, NLP, and Digital Forensics and wants to work in these fields in the future.

Author Articles
Emoji Prediction Using Emerging Machine Learning Classifiers for Text-based Communication

By Sayan Saha Kakelli Anil Kumar

DOI:, Pub. Date: 8 Feb. 2022

We aim to extract emotional components within statements to identify the emotional state of the writer and assigning emoji related to the emotion. Emojis have become a staple part of everyday text-based communication. It is normal and common to construct an entire response with the sole use of emoji. It comes as no surprise, therefore, that effort is being put into the automatic prediction and selection of emoji appropriate for a text message. Major companies like Apple and Google have made immense strides in this, and have already deployed such systems into production (for example, the Google Gboard). The proposed work is focused on the problem of automatic emoji selection for a given text message using machine learning classification algorithms to categorize the tone of a message which is further segregated through n-gram into one of seven distinct categories. Based on the output of the classifier, select one of the more appropriate emoji from a predefined list using natural language processing (NLP) and sentimental analysis techniques. The corpus is extracted from Twitter. The result is a boring text message made lively after being annotated with appropriate text messages

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