Sriparna Saha
Biographic Data
| ID | 4974997 |
|---|---|
| NAME | Sriparna Saha |
| GIVEN NAMES | Sriparna |
| FAMILY NAME | Saha |
| SIGNATURE | SAHA S |
| AFFILIATIONS | Indian Institute of Technology Patna |
| ORCID | 0000-0001-5458-9381 |
| VERIFIED | Yes |
| TOTAL WORKS | 22 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 22 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Variance in Teachers’ Implementation of Culturally Responsive Digital Curriculum Tools in Aotearoa New Zealand
Let’s Decipher the Origin: Towards Multimodal Aspect-Based Explainable Complaints in Finance
Financial grievances on social media increasingly feature multimodal content, combining text and images to express complex concerns. Previous text-only approaches often struggle to capture mixed emotional phenomena within posts, such as praise for credit card services alongside dissatisfaction with branch wait times. Prior research has relied on binary classifications, overlooking root aspect-specific complaints (e.g., categorizing card services …
Fine-Grained Visual Aspects in Genre Prediction
In this study, we investigate the role of visual content in accurately predicting movie genres. By extracting keyframes from Hindi, Bengali, Malayalam, and Telugu language-based Indian movie trailers in the Flickscore dataset, we analyse visual elements using visual language model (VLM) and Large Language Models (LLMs). Our approach focuses on the FAMOS aspects (focus, action, mood, object, setting) to understand the movie’s theme, summary, and g…
Multimodal Movie Recommendation With Multitasking Architecture and Learning User–Movie Representation: An Empirical Study
With the increasing availability of multimodal movie data, there is a growing interest in leveraging these data to improve movie recommendations. In the recent era, due to the increase in the number of users and movies on OTT platforms such as Amazon Prime, its services, including personalized movie recommendations, become challenging. This article proposes a novel approach $M^{2}RM^{2}UL$ , which stands for multimodal movie recommendation with m…
Toward Multimodal Complaint Severity Detection From Social Media
The prevalence of complaints submitted online and the sheer volume of information made available by social media platforms highlight the need for automated complaint analysis tools. In linguistic studies, complaints have been classified according to how much of personal risk the complainant is willing to take. This is crucial information for understanding the motivations of complainants and how people come up with reasonable means of reparation. …
Explainable Cyberbullying Detection in Hinglish: A Generative Approach
The escalating prevalence of online cyberbullying and trolling across various social media platforms has becomes a pressing concern. Extensive research demonstrates the detrimental impact of cyberbullying on the mental well-being of its victims. Given the sheer volume of online content, manual identification of cyberbullying instances proves unfeasible, necessitating the development of automated cyberbullying detection methods. This challenge has…
Online Research Topic Modeling and Recommendation Utilizing Multiview Autoencoder-Based Approach
Recent years have witnessed tremendous growth in the publication of research articles as well as in the rise of new research topics. Articles get published in a streaming manner and therefore retrieving and recommending trending topics continuously by updating the trend of topics with time will be beneficial for young researchers. The proposed topic recommendation system is a clustering-based approach that utilizes an autoencoder framework for th…
HateThaiSent: Sentiment-Aided Hate Speech Detection in Thai Language
Social media platforms are a double-edged sword: on the one hand, they enable the dissemination of information; but on the other hand, they also provide an avenue for spreading online abuse and harassment, such as hate speech. While significant research efforts are being devoted to detecting online hate speech in the English language, little attention has been paid to the Thai language. In this study, we created a benchmark dataset, called HateTh…
Extracting the Full Story: A Multimodal Approach and Dataset to Crisis Summarization in Tweets
In our digitally connected world, the influx of microblog data poses a formidable challenge in extracting relevant information amid a continuous stream of updates. This challenge intensifies during crises, where the demand for timely and relevant information is crucial. Current summarization techniques often struggle with the intricacies of microblog data in such situations. To address this, our research explores crisis-related microblogs, recogn…
Online Summarization of Microblog Data: An Aid in Handling Disaster Situations
During any natural disaster or unfortunate accident, both civilians and responders need information on an urgent basis. In such events, microblogging sites particularly Twitter plays an important role in providing real-time information. The raw form of microblog tweets is prodigiously informative but massive in size. The end-users and data analysts have to go through millions of tweets before extraction of any information. To ease the process and…
MTBullyGNN: A Graph Neural Network-Based Multitask Framework for Cyberbullying Detection
Cyberbullying is a malady of social media, and its automatic detection is critically important considering its virulence, velocity of spreading, and the scale of the havoc it can wreak. However, the problem is challenging due to its disguised behavior, noise in the content, and, in recent times, introduction of code-mixing. In this work, we propose MTBullyGNN a novel graph neural network (GNN)-based multitask (MT) framework that solves sentiment-…
Mental Health Disorder Identification From Motivational Conversations
Mental health disorder continues to be a grievous concern plaguing humans worldwide. The scarcity of mental health professionals (MHPs) has driven novel efforts lately to combat mental illness by developing automated systems capable of assisting MHPs. However, lack of high-quality conversational data due to privacy concerns remains a bottleneck toward its study and automation. Also, distinguishing and identifying various mental disorders is a cha…
Multiview Deep Online Clustering: An Application to Online Research Topic Modeling and Recommendations
In today’s scenario, a large number of scientific articles on various domains are being published everyday, resulting in a rapid change in the trends of research topics. Retrieving the trending topics, evaluating the trends and extracting the scope of topics could be beneficial to young researchers, which can be recommended for future scope. Publication of articles is a continuous process, and so is the evolution of topics as well as the scope. T…
Emoji, Sentiment and Emotion Aided Cyberbullying Detection in Hinglish
The advent of the Internet is a boon to society. However, many of its banes cannot be undermined, cyberbullying being one of them. The emotional state and sentiment of a person have a significant influence on the intended content. The current work is the first attempt in investigating the role of sentiment and emotion information for identifying cyberbullying in the Indian scenario. From Twitter, a benchmark Hind–English code-mixed corpus called …
Investigations in Emotion Aware Multimodal Gender Prediction Systems From Social Media Data
Gender plays a crucial role in improving the performance quality of personalized systems. Privacy and anonymity allow users to hide their details. Based on the intuition that post contents of male and female users differ, we can predict the gender of the social media account holder via their corresponding posts. These posts can be multimodal (text + image) in nature. We investigate various emotion-assisted multimodal gender prediction models in t…
An Attention-Based Multimodal Siamese Architecture for Tweet-User Verification
With the advent of internet technologies, it has created different ways of writing anonymously, which has lead to criminal and malicious activities over social media platforms. Thus, the automatic authentication checking of the available contents is the need of the hour. Social media sites, such as Facebook, Twitter, and so on, are used heavily by the users for sharing of information about their day-to-day activities. The identity of the suspect …
On Multimodal Microblog Summarization
Microblog summarization systems are gaining importance during natural disasters. A lot of tweets are posted along with multimedia content during the occurrence of any natural disaster event. Extracting relevant information/summary from these tweets is important for the smooth functioning of the rescue operation. Moreover, because of the limited size of the tweets, in many cases, tweets are associated with images. The current work is the first of …
A Multitask Multimodal Ensemble Model for Sentiment- and Emotion-Aided Tweet Act Classification
Speech act classification determining the communicative intent of an utterance has been studied widely over the years as an independent task. This holds true for discussion in any for a, including social media platforms such as Twitter. However, the tweeter’s emotional state has a huge impact on its pragmatic content because communication is fundamentally characterized and mediated by direct emotions. Sentiment as a human behavior often has a str…
Authorship Attribution of Microtext Using Capsule Networks
Authorship attribution (AA) is an important task, as it identifies the author of a written text from a set of suspect authors. Different methodologies of anonymous writing have been discovered with the rising usage of social media. This anonymous writing leads to an increase in malicious and suspicious activities, and anonymity makes it difficult to find the suspect. AA helps to find the writer of a suspect text from a set of suspects. Different …
A Multimodal Author Profiling System for Tweets
The rising usage of social media has motivated to invent different methodologies of anonymous writing, which leads to an increase in malicious and suspicious activities. This anonymity has created difficulty in finding the suspect. Author profiling deals with the characterization of an author through some key attributes such as gender, age, language, dialect region variety, personality, and so on. Identifying the gender of the author of a suspect…
Decoding emotional changes of android-gamers using a fused Type-2 fuzzy deep neural network
Bert-Caps: A Transformer-Based Capsule Network for Tweet Act Classification
Identification of speech acts provides essential cues in understanding the pragmatics of a user utterance. It typically helps in comprehending the communicative intention of a speaker. This holds true for conversations or discussions on any fora, including social media platforms, such as Twitter. This article presents a novel tweet act classifier (speech act for Twitter) for assessing the content and intent of tweets, thereby exploring the valuab…
No prominent works on this page.
Bert-Caps: A Transformer-Based Capsule Network for Tweet Act Classification
Identification of speech acts provides essential cues in understanding the pragmatics of a user utterance. It typically helps in comprehending the communicative intention of a speaker. This holds true for conversations or discussions on any fora, including social media platforms, such as Twitter. This article presents a novel tweet act classifier (speech act for Twitter) for assessing the content and intent of tweets, thereby exploring the valuab…
A Multimodal Author Profiling System for Tweets
The rising usage of social media has motivated to invent different methodologies of anonymous writing, which leads to an increase in malicious and suspicious activities. This anonymity has created difficulty in finding the suspect. Author profiling deals with the characterization of an author through some key attributes such as gender, age, language, dialect region variety, personality, and so on. Identifying the gender of the author of a suspect…
Decoding emotional changes of android-gamers using a fused Type-2 fuzzy deep neural network
On Multimodal Microblog Summarization
Microblog summarization systems are gaining importance during natural disasters. A lot of tweets are posted along with multimedia content during the occurrence of any natural disaster event. Extracting relevant information/summary from these tweets is important for the smooth functioning of the rescue operation. Moreover, because of the limited size of the tweets, in many cases, tweets are associated with images. The current work is the first of …
A Multitask Multimodal Ensemble Model for Sentiment- and Emotion-Aided Tweet Act Classification
Speech act classification determining the communicative intent of an utterance has been studied widely over the years as an independent task. This holds true for discussion in any for a, including social media platforms such as Twitter. However, the tweeter’s emotional state has a huge impact on its pragmatic content because communication is fundamentally characterized and mediated by direct emotions. Sentiment as a human behavior often has a str…
Authorship Attribution of Microtext Using Capsule Networks
Authorship attribution (AA) is an important task, as it identifies the author of a written text from a set of suspect authors. Different methodologies of anonymous writing have been discovered with the rising usage of social media. This anonymous writing leads to an increase in malicious and suspicious activities, and anonymity makes it difficult to find the suspect. AA helps to find the writer of a suspect text from a set of suspects. Different …
Mental Health Disorder Identification From Motivational Conversations
Mental health disorder continues to be a grievous concern plaguing humans worldwide. The scarcity of mental health professionals (MHPs) has driven novel efforts lately to combat mental illness by developing automated systems capable of assisting MHPs. However, lack of high-quality conversational data due to privacy concerns remains a bottleneck toward its study and automation. Also, distinguishing and identifying various mental disorders is a cha…
Multiview Deep Online Clustering: An Application to Online Research Topic Modeling and Recommendations
In today’s scenario, a large number of scientific articles on various domains are being published everyday, resulting in a rapid change in the trends of research topics. Retrieving the trending topics, evaluating the trends and extracting the scope of topics could be beneficial to young researchers, which can be recommended for future scope. Publication of articles is a continuous process, and so is the evolution of topics as well as the scope. T…
Emoji, Sentiment and Emotion Aided Cyberbullying Detection in Hinglish
The advent of the Internet is a boon to society. However, many of its banes cannot be undermined, cyberbullying being one of them. The emotional state and sentiment of a person have a significant influence on the intended content. The current work is the first attempt in investigating the role of sentiment and emotion information for identifying cyberbullying in the Indian scenario. From Twitter, a benchmark Hind–English code-mixed corpus called …
Investigations in Emotion Aware Multimodal Gender Prediction Systems From Social Media Data
Gender plays a crucial role in improving the performance quality of personalized systems. Privacy and anonymity allow users to hide their details. Based on the intuition that post contents of male and female users differ, we can predict the gender of the social media account holder via their corresponding posts. These posts can be multimodal (text + image) in nature. We investigate various emotion-assisted multimodal gender prediction models in t…
An Attention-Based Multimodal Siamese Architecture for Tweet-User Verification
With the advent of internet technologies, it has created different ways of writing anonymously, which has lead to criminal and malicious activities over social media platforms. Thus, the automatic authentication checking of the available contents is the need of the hour. Social media sites, such as Facebook, Twitter, and so on, are used heavily by the users for sharing of information about their day-to-day activities. The identity of the suspect …
Toward Multimodal Complaint Severity Detection From Social Media
The prevalence of complaints submitted online and the sheer volume of information made available by social media platforms highlight the need for automated complaint analysis tools. In linguistic studies, complaints have been classified according to how much of personal risk the complainant is willing to take. This is crucial information for understanding the motivations of complainants and how people come up with reasonable means of reparation. …
Explainable Cyberbullying Detection in Hinglish: A Generative Approach
The escalating prevalence of online cyberbullying and trolling across various social media platforms has becomes a pressing concern. Extensive research demonstrates the detrimental impact of cyberbullying on the mental well-being of its victims. Given the sheer volume of online content, manual identification of cyberbullying instances proves unfeasible, necessitating the development of automated cyberbullying detection methods. This challenge has…
Online Research Topic Modeling and Recommendation Utilizing Multiview Autoencoder-Based Approach
Recent years have witnessed tremendous growth in the publication of research articles as well as in the rise of new research topics. Articles get published in a streaming manner and therefore retrieving and recommending trending topics continuously by updating the trend of topics with time will be beneficial for young researchers. The proposed topic recommendation system is a clustering-based approach that utilizes an autoencoder framework for th…
HateThaiSent: Sentiment-Aided Hate Speech Detection in Thai Language
Social media platforms are a double-edged sword: on the one hand, they enable the dissemination of information; but on the other hand, they also provide an avenue for spreading online abuse and harassment, such as hate speech. While significant research efforts are being devoted to detecting online hate speech in the English language, little attention has been paid to the Thai language. In this study, we created a benchmark dataset, called HateTh…
Extracting the Full Story: A Multimodal Approach and Dataset to Crisis Summarization in Tweets
In our digitally connected world, the influx of microblog data poses a formidable challenge in extracting relevant information amid a continuous stream of updates. This challenge intensifies during crises, where the demand for timely and relevant information is crucial. Current summarization techniques often struggle with the intricacies of microblog data in such situations. To address this, our research explores crisis-related microblogs, recogn…
Online Summarization of Microblog Data: An Aid in Handling Disaster Situations
During any natural disaster or unfortunate accident, both civilians and responders need information on an urgent basis. In such events, microblogging sites particularly Twitter plays an important role in providing real-time information. The raw form of microblog tweets is prodigiously informative but massive in size. The end-users and data analysts have to go through millions of tweets before extraction of any information. To ease the process and…
MTBullyGNN: A Graph Neural Network-Based Multitask Framework for Cyberbullying Detection
Cyberbullying is a malady of social media, and its automatic detection is critically important considering its virulence, velocity of spreading, and the scale of the havoc it can wreak. However, the problem is challenging due to its disguised behavior, noise in the content, and, in recent times, introduction of code-mixing. In this work, we propose MTBullyGNN a novel graph neural network (GNN)-based multitask (MT) framework that solves sentiment-…
Multimodal Movie Recommendation With Multitasking Architecture and Learning User–Movie Representation: An Empirical Study
With the increasing availability of multimodal movie data, there is a growing interest in leveraging these data to improve movie recommendations. In the recent era, due to the increase in the number of users and movies on OTT platforms such as Amazon Prime, its services, including personalized movie recommendations, become challenging. This article proposes a novel approach $M^{2}RM^{2}UL$ , which stands for multimodal movie recommendation with m…
Variance in Teachers’ Implementation of Culturally Responsive Digital Curriculum Tools in Aotearoa New Zealand
Let’s Decipher the Origin: Towards Multimodal Aspect-Based Explainable Complaints in Finance
Financial grievances on social media increasingly feature multimodal content, combining text and images to express complex concerns. Previous text-only approaches often struggle to capture mixed emotional phenomena within posts, such as praise for credit card services alongside dissatisfaction with branch wait times. Prior research has relied on binary classifications, overlooking root aspect-specific complaints (e.g., categorizing card services …
Fine-Grained Visual Aspects in Genre Prediction
In this study, we investigate the role of visual content in accurately predicting movie genres. By extracting keyframes from Hindi, Bengali, Malayalam, and Telugu language-based Indian movie trailers in the Flickscore dataset, we analyse visual elements using visual language model (VLM) and Large Language Models (LLMs). Our approach focuses on the FAMOS aspects (focus, action, mood, object, setting) to understand the movie’s theme, summary, and g…
Computer Science (20 works) · Artificial Intelligence (17 works) · Machine learning (13 works) · Social media (13 works) · World Wide Web (12 works) · Natural language processing (11 works) · Hate Speech and Cyberbullying Detection (8 works) · Information retrieval (8 works) · Psychology (8 works) · Data science (7 works)