Effective short text classification via the fusion of hybrid features for IoT social data

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.

A Quantum Spatial Graph Convolutional Network for Text Classification

The data generated from non-Euclidean domains and its graphical representation(with complex-relationship object interdependence)applications has observed an exponential growth.The sophistication of graph data has posed consequential obstacles to the existing machine learning algorithms.In this study,we have considered a revamped version of a semi-supervised learning algorithm for graph-structured data to address the issue of expanding deep learning approaches to represent the graph data.Additionally,the quantum information theory has been applied through Graph Neural Networks(GNNs)to generate Riemannian metrics in closed-form of several graph layers.In further,to pre-process the adjacency matrix of graphs,a new formulation is established to incorporate high order proximities.The proposed scheme has shown outstanding improvements to overcome the deficiencies in Graph Convolutional Network(GCN),particularly,the information loss and imprecise information representation with acceptable computational overhead.Moreover,the proposed Quantum Graph Convolutional Network(QGCN)has significantly strengthened the GCN on semi-supervised node classification tasks.In parallel,it expands the generalization process with a significant difference by making small random perturbations
G of the graph during the training process.The evaluation results are provided on three benchmark datasets,including Citeseer,Cora,and PubMed,that distinctly delineate the superiority of the proposed model in terms of computational accuracy against state-of-the-art GCN and three other methods based on the same algorithms in the existing literature.

Deep-BERT:Transfer Learning for Classifying Multilingual Offensive Texts on Social Media

Offensive messages on social media,have recently been frequently used to harass and criticize people.In recent studies,many promising algorithms have been developed to identify offensive texts.Most algorithms analyze text in a unidirectional manner,where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences.In addition,there are many separate models for identifying offensive texts based on monolin-gual and multilingual,but there are a few models that can detect both monolingual and multilingual-based offensive texts.In this study,a detection system has been developed for both monolingual and multilingual offensive texts by combining deep convolutional neural network and bidirectional encoder representations from transformers(Deep-BERT)to identify offensive posts on social media that are used to harass others.This paper explores a variety of ways to deal with multilin-gualism,including collaborative multilingual and translation-based approaches.Then,the Deep-BERT is tested on the Bengali and English datasets,including the different bidirectional encoder representations from transformers(BERT)pre-trained word-embedding techniques,and found that the proposed Deep-BERT’s efficacy outperformed all existing offensive text classification algorithms reaching an accuracy of 91.83%.The proposed model is a state-of-the-art model that can classify both monolingual-based and multilingual-based offensive texts.

A Short Text Classification Model Based on Chinese Part-of-Speech Information and Mutual Learning

Short text classification is one of the common tasks in natural language processing.Short text contains less information,and there is still much room for improvement in the performance of short text classification models.This paper proposes a new short text classification model ML-BERT based on the idea of mutual learning.ML-BERT includes a BERT that only uses word vector informa-tion and a BERT that fuses word information and part-of-speech information and introduces transmissionflag to control the information transfer between the two BERTs to simulate the mutual learning process between the two models.Experi-mental results show that the ML-BERT model obtains a MAF1 score of 93.79%on the THUCNews dataset.Compared with the representative models Text-CNN,Text-RNN and BERT,the MAF1 score improves by 8.11%,6.69%and 1.69%,respectively.

An Intelligent Deep Neural Sentiment Classification Network

A Deep Neural Sentiment Classification Network(DNSCN)is devel-oped in this work to classify the Twitter data unambiguously.It attempts to extract the negative and positive sentiments in the Twitter database.The main goal of the system is tofind the sentiment behavior of tweets with minimum ambiguity.A well-defined neural network extracts deep features from the tweets automatically.Before extracting features deeper and deeper,the text in each tweet is represented by Bag-of-Words(BoW)and Word Embeddings(WE)models.The effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2(TSA2),Twit-ter-Sanders-Apple3(TSA3),and Twitter-DataSet(TDS).TSA2 and TDS consist of positive and negative tweets,whereas TSA3 has neutral tweets also.Thus,the proposed DNSCN acts as a binary classifier for TSA2 and TDS databases and a multiclass classifier for TSA3.The performances of DNSCN architecture are evaluated by F1 score,precision,and recall rates using 5-fold and 10-fold cross-validation.Results show that the DNSCN-WE model provides more accuracy than the DNSCN-BoW model for representing the tweets in the feature encoding.The F1 score of the DNSCN-BW based system on the TSA2 database is 0.98(binary classification)and 0.97(three-class classification)for the TSA3 database.This system provides better a F1 score of 0.99 for the TDS database.

Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool.

基于模糊聚类和Naive Bayes方法的文本分类器

本文提出一种文本分类的新方法 ,该方法将模糊聚类与基于NaiveBayes的EM分类算法相结合 ,从而大大提高了EM分类算法的准确性 ,并解决了使用字符匹配引起的不完整性和不准确性问题。该方法首先给出每个类的一些关键词 ,并把这些关键词作为聚类中心进行聚类 。