arXiv subjects

Omar Sharif

Publications and source records attributed to Omar Sharif.

23 records · Page 2Linked to original sources

arXiv2021

NLP-CUET@DravidianLangTech-EACL2021: Investigating Visual and Textual Features to Identify Trolls from Multimodal Social Media Memes

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 identify trolls from multimodal social media memes. This work presents a computational model that we have developed as part of our participation in the task. Training data comes in two forms: an image with embedded Tamil code-mixed text and an associated caption given in English. We investigated the visual and textual features using CNN, VGG16, Inception, Multilingual-BERT, XLM-Roberta, XLNet models. Multimodal features are extracted by combining image (CNN, ResNet50, Inception) and text (Long short term memory network) features via early fusion approach. Results indicate that the textual approach with XLNet achieved the highest weighted $f_1$-score of $0.58$, which enabled our model to secure $3^{rd}$ rank in this task.

cs.CV↗

arXiv2021

Combating Hostility: Covid-19 Fake News and Hostile Post Detection in Social Media

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 task-B is a multi-label multi-class classification task with five hostile classes (i.e. defame, fake, hate, offense, non-hostile). Various techniques are used to perform the classification task, including SVM, CNN, BiLSTM, and CNN+BiLSTM with tf-idf and Word2Vec embedding techniques. Results indicate that SVM with tf-idf features achieved the highest 94.39% weighted $f_1$ score on the test set in task-A. Label powerset SVM with n-gram features obtained the maximum coarse-grained and fine-grained $f_1$ score of 86.03% and 50.98% on the task-B test set respectively.

cs.CL↗

arXiv2020

TechTexC: Classification of Technical Texts using Convolution and Bidirectional Long Short Term Memory Network

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 classify a text of computer science domain into fine-grained sub-domains. A classification system (called 'TechTexC') is developed to perform the classification task using three techniques: convolution neural network (CNN), bidirectional long short term memory (BiLSTM) network, and combined CNN with BiLSTM. Results show that CNN with BiLSTM model outperforms the other techniques concerning task-1 of sub-tasks (a, b, c and g) and task-2a. This combined model obtained f1 scores of 82.63 (sub-task a), 81.95 (sub-task b), 82.39 (sub-task c), 84.37 (sub-task g), and 67.44 (task-2a) on the development dataset. Moreover, in the case of test set, the combined CNN with BiLSTM approach achieved that higher accuracy for the subtasks 1a (70.76%), 1b (79.97%), 1c (65.45%), 1g (49.23%) and 2a (70.14%).

cs.CL↗

arXiv2020

SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews

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 write up, and reviews. Sentiment analysis deals with the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude toward a particular topic is positive, negative, or neutral. The impact of customer review is significant to perceive the customer attitude towards a restaurant. Thus, the automatic detection of sentiment from reviews is advantageous for the restaurant owners, or service providers and customers to make their decisions or services more satisfactory. This paper proposes, a deep learning-based technique (i.e., BiLSTM) to classify the reviews provided by the clients of the restaurant into positive and negative polarities. A corpus consists of 8435 reviews is constructed to evaluate the proposed technique. In addition, a comparative analysis of the proposed technique with other machine learning algorithms presented. The results of the evaluation on test dataset show that BiLSTM technique produced in the highest accuracy of 91.35%.

cs.CL↗

arXiv2020

Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naive Bayes

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 affect the buying decision of a customer in most cases. This work introduces a machine learning-based technique to determine sentiment polarities (either positive or negative category) from Bengali book reviews. To assess the effectiveness of the proposed technique, a corpus with 2000 reviews on Bengali books is developed. A comparative analysis with various approaches (such as logistic regression, naive Bayes, SVM, and SGD) also performed by taking into consideration of the unigram, bigram, and trigram features, respectively. Experimental result reveals that the multinomial Naive Bayes with unigram feature outperforms the other techniques with 84% accuracy on the test set.

cs.CL↗