Omar Sharif

Chittagong University of Engineering & Technology | CUET · Department of Computer Science and Engineering (CSE)

Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents is growing enormously in recent years through instant messaging, social networking posts, blogs, online portals, and other digital platforms. Unfortunately, the misapplication of technologies has boosted with this rap...

In the past few years, the meme has become a new way of communication on the Internet. As memes are the images with embedded text, it can quickly spread hate, offence and violence. Classifying memes are very challenging because of their multimodal nature and region-specific interpretation. A shared task is organized to develop models that can ident...

The pervasiveness of aggressive content in social media has become a serious concern for government organizations and tech companies because of its pernicious societal effects. In recent years, social media has been repeatedly used as a tool to incite communal aggression, spread distorted propaganda, damage social harmony and demean the identity of...

This paper illustrates a detail description of the system and its results that developed as a part of the participation at CONSTRAINT shared task in AAAI-2021. The shared task comprises two tasks: a) COVID19 fake news detection in English b) Hostile post detection in Hindi. Task-A is a binary classification problem with fake and real class, while t...

Prior works formulate the extraction of event-specific arguments as a span extraction problem, where event arguments are explicit -- i.e. assumed to be contiguous spans of text in a document. In this study, we revisit this definition of Event Extraction (EE) by introducing two key argument types that cannot be modeled by existing EE frameworks. Fir...

Background: One of the key FDA-approved medications for Opioid Use Disorder (OUD) is buprenorphine. Despite its popularity, individuals often report various information needs regarding buprenorphine treatment on social media platforms like Reddit. However, the key challenge is to characterize these needs. In this study, we propose a theme-based fra...

Social media platforms are vital resources for sharing self-reported health experiences, offering rich data on various health topics. Despite advancements in Natural Language Processing (NLP) enabling large-scale social media data analysis, a gap remains in applying keyphrase extraction to health-related content. Keyphrase extraction is used to ide...

Social media sites have become a popular platform for individuals to seek and share health information. Despite the progress in natural language processing for social media mining, a gap remains in analyzing health-related texts on social discourse in the context of events. Event-driven analysis can offer insights into different facets of healthcar...

In social media platforms like Youtube, clickbait content has become a standardized method of driving public attention to video content for the creator’s benefit. With the gradual standardization of clickbait on Youtube, an increasing trend is observed for the probability of deceptive intentions within the content. Smarter clickbait and masqueradin...

Social media sites have become a popular platform for individuals to seek and share health information. Despite the progress in natural language processing for social media mining, a gap remains in analyzing health-related texts on social discourse in the context of events. Event-driven analysis can offer insights into different facets of healthcar...

[Accepted for ICWSM-2024] Social media platforms are vital resources for sharing self-reported health experiences, offering rich data on various health topics. Despite advancements in Natural Language Processing (NLP) enabling large-scale social media data analysis, a gap remains in applying keyphrase extraction to health-related content. Keyphrase...

An emerging trend on social media platforms is their use as safe spaces for peer support. Particularly in healthcare, where many medical conditions contain harsh stigmas, social media has become a stigma-free way to engage in dialogues regarding symptoms, treatments, and personal experiences. Many existing works have employed NLP algorithms to faci...

Advances in social media platforms led to the widespread adoption of memes, making them a powerful communication tool on the internet. Memes' visual aspect gives them a remarkable ability to influence users' opinions. However, individuals misemploy this popularity to foment animosity. The spread of these hostile memes can have a detrimental effect...

With the proliferation of internet usage, a massive growth of consumer-generated content on social media has been witnessed in recent years that provide people's opinions on diverse issues. Through social media, users can convey their emotions and thoughts in distinctive forms such as text, image, audio, video, and emoji, which leads to the advance...

In recent years, memes have become a common medium of promulgating o_ensive views by the content polluters in social media. Due to their multimodal nature, memes can easily evade the content regulators’ eyes. The proliferation of these undesired or harmful memes can cause a detrimental impact on social harmony. Therefore, restraining offensive meme...

With the substantial rise of internet usage, social media has become a powerful communication medium to convey information, opinions , and feelings on various issues. Recently, memes have become a popular way of sharing information on social media. Usually, memes is visuals with text incorporated into them and quickly disseminate hatred and offensi...

Emotion classification in text has growing interest among NLP experts due to the enormous availability of people’s emotions and its emergence on various Web 2.0 applications/services. Emotion classification in the Bengali texts is also gradually being considered as an important task for sports, e-commerce, entertainments, and security applications....

Recently, word sense disambiguation has gained increased attention by NLP practitioner due to its various potential applications in language technology. This paper proposes a Naïve Bayes classifier for resolving lexical ambiguities of Bangla words with the help of a Bangla sense annotated corpus. At the initial stage, a Bangla sense annotated corpu...

In recent years, the widespread use of the Internet has resulted in a revolutionary way for people to share their feelings or sentiment on blogs, social media, e-commerce sites, and online platforms. Most of the feelings expressed on the online platforms are in textual forms (such as status, tweets, comments, and reviews). These textual expressions...

Categorizing emotion refers to extracting the individuals’ behaviour from texts and assigning textual units into an emotion from predefined emotional connotations. Identification and categorization of emotion content have mostly been made for English, French, Chinese, Arabic, and other high-resource languages. However, very few studies have investi...

News categorization is the task of automatically assigning the news articles or headlines to a particular class. The proliferation of social media and various web 2.0 platforms usage has resulted in substantial textual online content. The majority of this textual data is unstructured, which is extremely hard and time-consuming to organize, manipula...

The proliferation of the Internet and social media usage creates enormous textual data (specifically, news content) on the web. The most proportion of contents primarily are unstructured. Extracting meaningful insights from unstructured content is nearly impossible or extremely hard, and time-consuming by human labor. Thus, automatic text classific...

Textual semantic similarity is a crucial constituent in many NLP tasks such as information retrieval, machine translation, information retrieval and textual forgery detection. It is a complicated task for rule-based techniques to address semantic similarity measures in low-resource languages due to the complex morphological structure and scarcity o...

It is a very challenging task to recognize unconstrained Bengali handwritten text due to its cursive nature. This paper introduces an offline technique of recognizing handwritten Bengali sentences based on BiLSTM architecture and connectionist temporal classification (CTC) output layer. The traditional approach of detecting handwritten sentence rec...

The amount of textual data generation has increased enormously due to the effortless access of the Internet and the evolution of various web 2.0 applications. These textual data productions resulted because of, the people express their opinions, emotions or sentiment regarding any service or product through tweets, posts, and reviews. Sentiment ana...

Although research on emotion classification has significantly progressed in high-resource languages, it is still infancy for resource-constrained languages like Bengali. However, unavailability of necessary language processing tools and deficiency of benchmark corpora makes the emotion classification task in Bengali more challenging and complicated...

Recently, social media has gained substantial attention as people can share opinions, expressions, emotions and carry out meaningful interactions through it spontaneously. Unfortunately, with this rapid advancement, social media misuse has also been proliferated, which leads to an increase in aggressive, offensive and abusive activities. Most of th...

In recent years, several systems have been developed to regulate the spread of negativity and eliminate aggressive, offensive or abusive contents from the online platforms. Nevertheless, a limited number of researches carried out to identify positive, encouraging and supportive contents. In this work, our goal is to identify whether a social media...

The increasing accessibility of the internet facilitated social media usage and encouraged individuals to express their opinions liberally. Nevertheless, it also creates a place for content polluters to disseminate offensive posts or contents. Most of such offensive posts are written in a cross-lingual manner and can easily evade the online surveil...

Part of Speech (POS) tagging is recognized as a significant research problem in the field of Natural Language Processing (NLP). It has considerable importance in several NLP technologies. However, developing an efficient POS tagger is a challenging task for resource-scarce languages like Bengali. This paper presents an empirical investigation of va...

Recently, emotion detection in language has increased attention to NLP researchers due to the massive availability of people’s expressions, opinions, and emotions through comments on the Web 2.0 platforms. It is a very challenging task to develop an automatic sentiment analysis system in Bengali due to the scarcity of resources and the unavailabili...

This paper illustrates the details description of technical text classification system and its results that developed as a part of participation in the shared task TechDofication 2020. The shared task consists of two sub-tasks: (i) first task identify the coarse-grained technical domain of given text in a specified language and (ii) the second task...

The amount of textual data generation has increased enormously due to the effortless access of the Internet and the evolution of various web 2.0 applications. These textual data productions resulted because of the people express their opinion, emotion or sentiment about any product or service in the form of tweets, Facebook post or status, blog wri...

Recently, sentiment polarity detection has increased attention to NLP researchers due to the massive availability of customer's opinions or reviews in the online platform. Due to the continued expansion of e-commerce sites, the rate of purchase of various products, including books, are growing enormously among the people. Reader's opinions/reviews...

Suspicious Bangla text detection is a text classification problem of determining Bangla texts into suspicious and non suspicious categories. In this paper, we have proposed a machine learning based system that can classify Bangla texts into suspicious and non-suspicious. For this purpose, a corpus is developed and logistic regression algorithm is u...