Multi-channel CNN to classify nepali covid-19 related tweets using hybrid features

• URL: http://arxiv.org/abs/2203.10286v1
• Date: Sat, 19 Mar 2022 09:55:05 GMT
• Title: Multi-channel CNN to classify nepali covid-19 related tweets using hybrid features
• Authors: Chiranjibi Sitaula, Tej Bahadur Shahi
• Abstract summary: We represent each tweet by combining both syntactic and semantic information, called hybrid features. We design a novel multi-channel convolutional neural network (MCNN), which ensembles the multiple CNNs. We evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets on NepCOV19Tweets dataset.
• Score: 1.713291434132985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Because of the current COVID-19 pandemic with its increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected the developed countries but also developing countries such as Nepal. These complications can be understood from peoples' tweets/comments posted online after their proper analysis and sentiment classification. Nevertheless, owing to the limited number of tokens/words in each tweet, it is always crucial to capture multiple information associated with them for their better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag of words method, whereas the semantic information is generated from the combination of the fastText-based (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel convolutional neural network (MCNN), which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral and negative) on NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification.

Related papers

• Ensemble Transfer Learning for Multilingual Coreference Resolution [60.409789753164944]
  A problem that frequently occurs when working with a non-English language is the scarcity of annotated training data. We design a simple but effective ensemble-based framework that combines various transfer learning techniques. We also propose a low-cost TL method that bootstraps coreference resolution models by utilizing Wikipedia anchor texts.
  arXiv  Detail & Related papers  (2023-01-22T18:22:55Z)
• Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification [6.946336514955953]
  Convolutional Neural Networks (CNN) are more suitable, indeed. fixed kernel sizes make traditional CNN too specific, neither flexible nor conducive to feature learning, thus impacting on the classification accuracy. The proposed solution aims at combining the core idea of 3D and 2D Inception net with the Attention mechanism to boost the HSIC CNN performance in a hybrid scenario. The resulting textitattention-fused hybrid network (AfNet) is based on three attention-fused parallel hybrid sub-nets with different kernels in each block repeatedly using high-level features to enhance the final ground-truth maps.
  arXiv  Detail & Related papers  (2022-01-04T06:30:24Z)
• Keypoint Message Passing for Video-based Person Re-Identification [106.41022426556776]
  Video-based person re-identification (re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras. Existing methods are mostly based on convolutional neural networks (CNNs), whose building blocks either process local neighbor pixels at a time, or, when 3D convolutions are used to model temporal information, suffer from the misalignment problem caused by person movement. In this paper, we propose to overcome the limitations of normal convolutions with a human-oriented graph method. Specifically, features located at person joint keypoints are extracted and connected as a spatial-temporal graph
  arXiv  Detail & Related papers  (2021-11-16T08:01:16Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• The Mind's Eye: Visualizing Class-Agnostic Features of CNNs [92.39082696657874]
  We propose an approach to visually interpret CNN features given a set of images by creating corresponding images that depict the most informative features of a specific layer. Our method uses a dual-objective activation and distance loss, without requiring a generator network nor modifications to the original model.
  arXiv  Detail & Related papers  (2021-01-29T07:46:39Z)
• Convolutional Neural Networks in Multi-Class Classification of Medical Data [0.9137554315375922]
  We introduce an ensemble model that consists of both deep learning (CNN) and shallow learning models (Gradient Boosting) The method achieves Accuracy of 64.93, the highest three-class classification accuracy we achieved in this study.
  arXiv  Detail & Related papers  (2020-12-28T02:04:38Z)
• Fusion of CNNs and statistical indicators to improve image classification [65.51757376525798]
  Convolutional Networks have dominated the field of computer vision for the last ten years. Main strategy to prolong this trend relies on further upscaling networks in size. We hypothesise that adding heterogeneous sources of information may be more cost-effective to a CNN than building a bigger network.
  arXiv  Detail & Related papers  (2020-12-20T23:24:31Z)
• Covid-Transformer: Detecting COVID-19 Trending Topics on Twitter Using Universal Sentence Encoder [7.305019142196582]
  corona-virus disease (also known as COVID-19) has led to a pandemic, impacting more than 200 countries across the globe. With its global impact, COVID-19 has become a major concern of people almost everywhere. We try to analyze the tweets and detect the trending topics and major concerns of people on Twitter.
  arXiv  Detail & Related papers  (2020-09-08T19:00:38Z)
• Combining word embeddings and convolutional neural networks to detect duplicated questions [0.0]
  We propose a simple approach to identifying semantically similar questions by combining the strengths of word embeddings and Convolutional Neural Networks. Our network is trained on the Quora dataset, which contains over 400k question pairs.
  arXiv  Detail & Related papers  (2020-06-08T12:30:25Z)
• A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers [54.996358399108566]
  We investigate the performance of the landmark general CNN classifiers, which presented top-notch results on large scale classification datasets. We compare it against state-of-the-art fine-grained classifiers. We show an extensive evaluation on six datasets to determine whether the fine-grained classifier is able to elevate the baseline in their experiments.
  arXiv  Detail & Related papers  (2020-03-24T23:49:14Z)
• Utilizing Deep Learning to Identify Drug Use on Twitter Data [0.0]
  The classification power of multiple methods was compared including support vector machines (SVM), XGBoost, and convolutional neural network (CNN) based classifiers. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. The synthetically generated set provided increased scores, improving the classification capability and proving the worth of this methodology.
  arXiv  Detail & Related papers  (2020-03-08T07:52:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.