Novel OGBEE-based feature selection and feature-level fusion with MLP neural network for social media multimodal sentiment analysis

Bairavel, S.; Krishnamurthy, M.

doi:10.1007/s00500-020-05049-6

Cited by 24 publications

(14 citation statements)

References 38 publications

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“…German, one of the official languages of the United Nations and the eighth most spoken language in the world, is widely spoken in 17 countries in the Eastern European and Central Asian regions, and the total number of people who speak it as a native or second language is about 258 million [2]. As one of the main ways for people to communicate and express their emotions, social media generates a large amount of short texts in German with subjective emotions every day, and it is beneficial to summarize, analyze, and reason about the emotional information contained in them for making business decisions, analyzing political opinions, and predicting social trends in related countries [3]. It is of great value to prevent precise political marketing, build harmonious and stable international relations, promote transnational and interregional economic and trade, and carry out the "Belt and Road" strategy of win-win cooperation.…”

Section: Introductionmentioning

confidence: 99%

Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

2021

Security and Communication Networks

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In this paper, we propose a multilevel feature representation method that combines word-level features, such as German morphology and slang, and sentence-level features, such as special symbols and English-translated sentiment information, and build a deep learning model for German sentiment classification based on the self-attentive mechanism, in order to address the characteristics of German social media texts that are colloquial, irregular, and diverse. Compared with the existing studies, this model not only has the most obvious improvement effect but also has better feature extraction and classification ability for German emotion.

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Section: Introductionmentioning

confidence: 99%

Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

2021

Security and Communication Networks

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“…Table 10 demonstrates the outcomes of the three modalities were intertwined delivering a precision of 89.8%. Definitely, this accuracy is higher in the proposed method when compared with other approaches namely SVM, ANN, OGBEE, 50 and MKELM which obtained 74.09%, 84.51% and 87.8% precision.…”

Section: Resultsmentioning

confidence: 79%

“…Figure 7 describes the comparative analysis of the proposed HBEE optimization algorithms with various other algorithms such as OGBEE, 50 ABC, GHO, DE, and GA with respect to the computational time. The experimental result shows that the proposed HGBEE provides the minimum computational time when compared with all other approaches.…”

Section: Resultsmentioning

confidence: 99%

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

Alqahtani

Pandiaraj²,

Murali³

et al. 2021

Concurrency and Computation

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Presently, large recordings like audio and video are uploaded each day on the public networks particularly Facebook, YouTube, etc. Such postings generate unlimited data via the Internet. Over the next few decades adapting and dealing with data mining to obtain relevant details from social media is considered as a difficult task. To tackle these challenges, a novel text-audio-video consistency-driven multimodal sentiment analysis method is proposed in this paper. This proposed system examines the correlation among the text, audio and video followed by multimodal sentiment analysis.A different set of features are extracted and then the extracted features are chosen optimally by employing a new hybrid grass bee optimization algorithm (HGBEE) thus obtaining a feature set containing an optimal value for better precision and low computational time. Then an utterance level multimodular fusion regarding text, audio, and video features is developed. Finally, the proposed multikernal extreme learning classifier (MKELM) is employed for sentiment classification. Then the proposed system is evaluated by testing with three multimodal datasets in terms of precision, accuracy, recall, and F-measure, respectively. From the simulation, it is clear that the proposed system accomplishes the maximum classification accuracy value of 98.3% with minimum computation time. The implementation of our proposed approach is done under the MATLAB platform. K E Y W O R D Saccuracy and computation time, feature level fusion, grass bee, kernal extreme learning classifier, multimodal sentiment analysis Recently, the public network facilitates gathering information for posting and sharing various types of multimodal features in social media without knowing much detail regarding the network topology and architecture. 1 Individuals are now widely utilizing the internet-based life condition, for example, YouTube, Facebook, Blog, and Microblog so as to express their opinions. 2 Here, sentiment analysis acts as a key for unlocking huge data since

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“…Bairavel et al [ 11 ] suggested a model for multimodal sentiment analysis using feature-level fusion technique and novel oppositional grass bee optimization (OGBEE) algorithm for fusing the extracted features from different modalities and MLP for classification. [ 12 ] compared three different neural network approaches, MLP, RBF and PNN, for Thematic mapping from remotely sensed data.…”

Section: Architecture Algorithm and Characteristics Of Mlpmentioning

confidence: 99%

A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN

Naskath¹,

Sivakamasundari²,

Begum

2022

Wireless Pers Commun

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Deep learning is a wildly popular topic in machine learning and is structured as a series of nonlinear layers that learns various levels of data representations. Deep learning employs numerous layers to represent data abstractions to implement various computer models. Deep learning approaches like generative, discriminative models and model transfer have transformed information processing. This article proposes a comprehensive review of various deep learning algorithms Multi layer perception, Self-organizing map and deep belief networks algorithms. It first briefly introduces historical and recent state-of-the-art reviews with suitable architectures and implementation steps. Moreover, the various applications of those algorithms in various fields such as wireless networks, Adhoc networks, Mobile ad-hoc and vehicular ad-hoc networks, speech recognition engineering, medical applications, natural language processing, material science and remote sensing applications, etc. are classified.

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Novel OGBEE-based feature selection and feature-level fusion with MLP neural network for social media multimodal sentiment analysis

Cited by 24 publications

References 38 publications

Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN

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