An Automated Learning Model of Conventional Neural Network Based Sentiment Analysis on Twitter Data

Neelakandan, S.; Paulraj, D.

doi:10.1166/jctn.2020.8876

Cited by 29 publications

(6 citation statements)

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“…In Reference [24], a hybrid SA framework based on hadoop distributed file system MapReduce and the Gradient Boosted Decision Tree classification method is applied to classify sentiments in the tweets. An automated neural network‐based SA model is proposed in Reference [25] to explore Twitter data. Also, a deep learning neural classification for polarity classification is proposed in Reference [26], based on a Sentiment Treebank; while in Reference [27] a word embedding method has been exploited for deep convolution neural networks.…”

Section: Related Workmentioning

confidence: 99%

Improving the emotion‐based classification by exploiting the fuzzy entropy in FCM clustering

Cardone

Martino

Senatore

2021

Int J of Intelligent Sys

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Emotion detection in the natural language text has drawn the attention of several scientific communities as well as commercial/marketing companies: analyzing human feelings expressed in the opinions and feedback of web users helps understand general moods and support market strategies for product advertising and market predictions. This paper proposes a framework for emotion‐based classification from social streams, such as Twitter, according to Plutchik's wheel of emotions. An entropy‐based weighted version of the fuzzy c‐means (FCM) clustering algorithm, called EwFCM, to classify the data collected from streams has been proposed, improved by a fuzzy entropy method for the FCM center cluster initialization. Experimental results show that the proposed framework provides high accuracy in the classification of tweets according to Plutchik's primary emotions; moreover, the framework also allows the detection of secondary emotions, which, as defined by Plutchik, are the combination of the primary emotions. Finally, a comparative analysis with a similar fuzzy clustering‐based approach for emotion classification shows that EwFCM converges more quickly with better performance in terms of accuracy, precision, and runtime. Finally, a straightforward mapping between the computed clusters and the emotion‐based classes allows the assessment of the classification quality, reporting coherent and consistent results.

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Section: Related Workmentioning

confidence: 99%

Improving the emotion‐based classification by exploiting the fuzzy entropy in FCM clustering

Cardone

Martino

Senatore

2021

Int J of Intelligent Sys

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“…When the websites are classified effectively, their URL is queued and extracted by the frontier model. In few FWC methods [6,7], the classification model depends on the document similarity measures for filtering related and non-related web pages. But such methods do not consider the expressiveness of web page contents, i.e., they do not explore their semantic contents or utilize the data in the filter procedure [8].…”

Section: Introductionmentioning

confidence: 99%

An Automated Word Embedding with Parameter Tuned Model for Web Crawling

Neelakandan¹,

Aggarwal²,

Bhukya³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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In recent years, web crawling has gained a significant attention due to the drastic advancements in the World Wide Web. Web Search Engines have the issue of retrieving massive quantity of web documents. One among the web crawlers is the focused crawler, that intends to selectively gather web pages from the Internet. But the efficiency of the focused crawling can easily be affected by the environment of web pages. In this view, this paper presents an Automated Word Embedding with Parameter Tuned Deep Learning (AWE-PTDL) model for focused web crawling. The proposed model involves different processes namely pre-processing, Incremental Skip-gram Model with Negative Sampling (ISGNS) based word embedding, bidirectional long short-term memory-based classification and bird swarm optimization based hyperparameter tuning. The SGNS training desires to go over the complete training data to pre-compute the noise distribution before performing Stochastic Gradient Descent (SGD) and the ISGNS technique is derived for the word embedding process. Besides, the cosine similarity is computed from the word embedding matrix to generate a feature vector which is fed as input into the Bidirectional Long Short-Term Memory (BiLSTM) for the prediction of website relevance. Finally, the Birds Swarm Optimization-Bidirectional Long Short-Term Memory (BSO-BiLSTM) based classification model is used to classify the webpages and the BSO algorithm is employed to determine the hyperparameters of the BiLSTM model so that the overall crawling performance can be considerably enhanced. For validating the enhanced outcome of the presented model, a comprehensive set of simulations are carried out and the results are examined in terms of different measures. The Automated Word Embedding with Parameter Tuned Deep Learning (AWE-PTDL) technique has attained a higher harvest rate of 85% when compared with the other techniques. The experimental results highlight the enhanced web crawling performance of the proposed model over the recent state of art web crawlers.

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“…Recent advancements in smart systems have stimulated scientists for employing distributed IDS in association with several Machine Learning (ML) methods like reinforcement learning (RL), deep learning (DL), and Artificial Neural networks (ANN). The regular ANN method has certain constraints in handling the difficulty of IDS [8][9][10]. Enhancing techniques by resolving these limitations is a demand for understanding the possibilities of IDS in real-time.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System for Big Data Analytics in IoT Environment

Anuradha¹,

Mani²,

Shanthi³

et al. 2022

Computer Systems Science and Engineering

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In the digital area, Internet of Things (IoT) and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic. Though IoT networks are popular and widely employed in real world applications, security in IoT networks remains a challenging problem. Conventional intrusion detection systems (IDS) cannot be employed in IoT networks owing to the limitations in resources and complexity. Therefore, this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning (IMFSDL) based classification model, called IMFSDL-IDS for IoT networks. The proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages: data transformation and data normalization. To manage big data, Hadoop ecosystem is employed. Besides, the IMFSDL-IDS model includes a hill climbing with moth flame optimization (HCMFO) for feature subset selection to reduce the complexity and increase the overall detection efficiency. Moreover, the beetle antenna search (BAS) with variational autoencoder (VAE), called BAS-VAE technique is applied for the detection of intrusions in the feature reduced data. The BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification performance. To validate the intrusion detection performance of the IMFSDL-IDS system, a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct aspects. The resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25% and 97.39% on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.

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An Automated Learning Model of Conventional Neural Network Based Sentiment Analysis on Twitter Data

Cited by 29 publications

References 0 publications

Improving the emotion‐based classification by exploiting the fuzzy entropy in FCM clustering

Improving the emotion‐based classification by exploiting the fuzzy entropy in FCM clustering

An Automated Word Embedding with Parameter Tuned Model for Web Crawling

Intrusion Detection System for Big Data Analytics in IoT Environment

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