arXiv subjects

Raj Kumar Gupta

Publications and source records attributed to Raj Kumar Gupta.

6 recordsLinked to original sources

arXiv2023

How People Respond to the COVID-19 Pandemic on Twitter: A Comparative Analysis of Emotional Expressions from US and India

The COVID-19 pandemic has claimed millions of lives worldwide and elicited heightened emotions. This study examines the expression of various emotions pertaining to COVID-19 in the United States and India as manifested in over 54 million tweets, covering the fifteen-month period from February 2020 through April 2021, a period which includes the beginnings of the huge and disastrous increase in COVID-19 cases that started to ravage India in March 2021. Employing pre-trained emotion analysis and topic modeling algorithms, four distinct types of emotions (fear, anger, happiness, and sadness) and their time- and location-associated variations were examined. Results revealed significant country differences and temporal changes in the relative proportions of fear, anger, and happiness, with fear declining and anger and happiness fluctuating in 2020 until new situations over the first four months of 2021 reversed the trends. Detected differences are discussed briefly in terms of the latent topics revealed and through the lens of appraisal theories of emotions, and the implications of the findings are discussed.

cs.CL↗

arXiv2022

COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes

This paper describes a large global dataset on people's discourse and responses to the COVID-19 pandemic over the Twitter platform. From 28 January 2020 to 1 June 2022, we collected and processed over 252 million Twitter posts from more than 29 million unique users using four keywords: "corona", "wuhan", "nCov" and "covid". Leveraging probabilistic topic modelling and pre-trained machine learning-based emotion recognition algorithms, we labelled each tweet with seventeen attributes, including a) ten binary attributes indicating the tweet's relevance (1) or irrelevance (0) to the top ten detected topics, b) five quantitative emotion attributes indicating the degree of intensity of the valence or sentiment (from 0: extremely negative to 1: extremely positive) and the degree of intensity of fear, anger, sadness and happiness emotions (from 0: not at all to 1: extremely intense), and c) two categorical attributes indicating the sentiment (very negative, negative, neutral or mixed, positive, very positive) and the dominant emotion (fear, anger, sadness, happiness, no specific emotion) the tweet is mainly expressing. We discuss the technical validity and report the descriptive statistics of these attributes, their temporal distribution, and geographic representation. The paper concludes with a discussion of the dataset's usage in communication, psychology, public health, economics, and epidemiology.

cs.CL↗

arXiv2021

Exploring the contextual factors affecting multimodal emotion recognition in videos

Emotional expressions form a key part of user behavior on today's digital platforms. While multimodal emotion recognition techniques are gaining research attention, there is a lack of deeper understanding on how visual and non-visual features can be used to better recognize emotions in certain contexts, but not others. This study analyzes the interplay between the effects of multimodal emotion features derived from facial expressions, tone and text in conjunction with two key contextual factors: i) gender of the speaker, and ii) duration of the emotional episode. Using a large public dataset of 2,176 manually annotated YouTube videos, we found that while multimodal features consistently outperformed bimodal and unimodal features, their performance varied significantly across different emotions, gender and duration contexts. Multimodal features performed particularly better for male speakers in recognizing most emotions. Furthermore, multimodal features performed particularly better for shorter than for longer videos in recognizing neutral and happiness, but not sadness and anger. These findings offer new insights towards the development of more context-aware emotion recognition and empathetic systems.

cs.MM↗

arXiv2021

Surface plasmon resonance for in-plane birefringence measurement of anisotropic thin organic film

The measurement of in-plane birefringence ($Δ{n}$) of ultrathin film is challenging due to a significant deviation of physical properties of materials in ultrathin regime as compared to that in bulk state. Surface plasmon resonance (SPR) phenomenon can be employed to measure change in refractive index of ultrathin film at a very high resolution. This article discusses simulation of SPR phenomenon in Kretschmann configuration for the measurement of $Δ{n}$ in organic thin film exhibiting nematic-like ordering on the two dimensional gold surface. The distribution of plasmonic field on the gold surface was found to be anisotropic. This suggested that the coupling plasmonic field with that of organic thin film exhibiting nematic-like ordering on the gold surface will be non-isotropic. Therefore, a non-zero difference in resonance angle (RA) was obtained from SPR measurement performed along the optic-axis (OA) and orthogonal to OA of the in-plane nematic ordering ($Δθ$). A calibration surface showing the variation of ($Δθ$) as a function of $Δ{n}$ and thickness of thin organic film consisting of shape anisotropic tilted molecules exhibiting nematic-like ordering on gold surface was obtained. This calibration surface was employed for the measurement of $Δ{n}$ of single layer of Langmuir-Blodgett films of cadmium stearate (CdSA) and 4'-octyl-4-biphenylcarbonitrile (8CB) deposited on SPR chips. The thickness of the LB films was estimated using X-ray reflectivity measurement and $Δθ$ was measured using a home built SPR instrument. The $Δ{n}$ values were found to be 0.012 and 0.022 for ultrathin films of CdSA and 8CB molecules, respectively.

cond-mat.soft↗

arXiv2020

SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda using Sentence-Level Emotional Salience Features

This paper describes a system developed for detecting propaganda techniques from news articles. We focus on examining how emotional salience features extracted from a news segment can help to characterize and predict the presence of propaganda techniques. Correlation analyses surfaced interesting patterns that, for instance, the "loaded language" and "slogan" techniques are negatively associated with valence and joy intensity but are positively associated with anger, fear and sadness intensity. In contrast, "flag waving" and "appeal to fear-prejudice" have the exact opposite pattern. Through predictive experiments, results further indicate that whereas BERT-only features obtained F1-score of 0.548, emotion intensity features and BERT hybrid features were able to obtain F1-score of 0.570, when a simple feedforward network was used as the classifier in both settings. On gold test data, our system obtained micro-averaged F1-score of 0.558 on overall detection efficacy over fourteen propaganda techniques. It performed relatively well in detecting "loaded language" (F1 = 0.772), "name calling and labeling" (F1 = 0.673), "doubt" (F1 = 0.604) and "flag waving" (F1 = 0.543).

cs.CL↗

arXiv2017

A learning-based approach for automatic image and video colorization

In this paper, we present a color transfer algorithm to colorize a broad range of gray images without any user intervention. The algorithm uses a machine learning-based approach to automatically colorize grayscale images. The algorithm uses the superpixel representation of the reference color images to learn the relationship between different image features and their corresponding color values. We use this learned information to predict the color value of each grayscale image superpixel. As compared to processing individual image pixels, our use of superpixels helps us to achieve a much higher degree of spatial consistency as well as speeds up the colorization process. The predicted color values of the gray-scale image superpixels are used to provide a 'micro-scribble' at the centroid of the superpixels. These color scribbles are refined by using a voting based approach. To generate the final colorization result, we use an optimization-based approach to smoothly spread the color scribble across all pixels within a superpixel. Experimental results on a broad range of images and the comparison with existing state-of-the-art colorization methods demonstrate the greater effectiveness of the proposed algorithm.

cs.GR↗