Aspect based sentiment analysis for demonetization tweets by optimized recurrent neural network using fire fly-oriented multi-verse optimizer

Datta, Samik; Chakrabarti, Satyajit

doi:10.1007/s12046-021-01608-1

Cited by 20 publications

(11 citation statements)

References 48 publications

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“…Deep learning-based methods have also been applied to the sentiment analysis domain recently to further improve efficiency [ 1 , 4 , 5 , 10 , 12 , 14 , 20 , 25 , 26 , 29 , 30 , 52 ].…”

Section: Related Workmentioning

confidence: 99%

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A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis

Kaur

Sharma

2023

J Big Data

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There is an exponential growth in textual content generation every day in today's world. In-app messaging such as Telegram and WhatsApp, social media websites such as Instagram and Facebook, e-commerce websites like Amazon, Google searches, news publishing websites, and a variety of additional sources are the possible suppliers. Every instant, all these sources produce massive amounts of text data. The interpretation of such data can help business owners analyze the social outlook of their product, brand, or service and take necessary steps. The development of a consumer review summarization model using Natural Language Processing (NLP) techniques and Long short-term memory (LSTM) to present summarized data and help businesses obtain substantial insights into their consumers' behavior and choices is the topic of this research. A hybrid approach for analyzing sentiments is presented in this paper. The process comprises pre-processing, feature extraction, and sentiment classification. Using NLP techniques, the pre-processing stage eliminates the undesirable data from input text reviews. For extracting the features effectively, a hybrid method comprising review-related features and aspect-related features has been introduced for constructing the distinctive hybrid feature vector corresponding to each review. The sentiment classification is performed using the deep learning classifier LSTM. We experimentally evaluated the proposed model using three different research datasets. The model achieves the average precision, average recall, and average F1-score of 94.46%, 91.63%, and 92.81%, respectively.

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“…Deep learning-based methods have also been applied to the sentiment analysis domain recently to further improve efficiency [ 1 , 4 , 5 , 10 , 12 , 14 , 20 , 25 , 26 , 29 , 30 , 52 ].…”

Section: Related Workmentioning

confidence: 99%

“…The aspect-based sentiment analysis for demonetization tweets is performed using the improved deep learning method by Datta and Chakrabarti [ 12 ]. Pre-processing, extraction of aspects, polarity features, and sentiment categorization are all phases of the proposed model.…”

Section: Related Workmentioning

confidence: 99%

A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis

Kaur

Sharma

2023

J Big Data

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“…Other than these works, deep learning has received significant attention for sentiment analysis (Lu et al 2021 ; Datta and Chakrabarti 2021 ), but due to higher training and computational time, we do not consider it as the cost-effective mechanism for SA.…”

Section: Related Workmentioning

confidence: 99%

HAS: Hybrid Analysis of Sentiments for the perspective of customer review summarization

Kaur

Sharma

2022

J Ambient Intell Human Comput

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The reviews posted online by the end-users can help the business owners obtain a fair evaluation of their products/services and take the necessary steps. However, due to the large volume of online reviews being generated from time to time, it becomes challenging for business owners to track each review. The Customer Review Summarization (CRS) model that can present the summarized information and offer businesses with significant acumens to understand the reason behind customers' choices and behavior, would therefore be desirable. We propose the Hybrid Analysis of Sentiments (HAS) for the perspective of effective CRS in this paper. The HAS consists of steps like pre-processing, feature extraction, and review classification. The pre-processing phase removes the unwanted data from the text reviews using Natural Language Processing (NLP) based on different pre-processing functions. For efficient feature extraction, the hybrid mechanism consisting of aspect-related features and review-related features is proposed to build the unique feature vector for each customer review. Review classification is performed using different supervised classifiers like Support Vector Machine (SVM), Naïve Bayes, and Random Forest. The experimental results show that HAS efficiently performed the sentiment analysis and outperformed the existing state-of-the-art techniques with an F1 score of 92.2%.

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“…Finally, the comparative analysis of different machine learning algorithms proves the competent performance of the proposed model. The proposed model FF-MVO-RNN achieves the better results of accuracy 89%, precision 98%, F1-score 94% and recall 89% (2) .…”

Section: Introductionmentioning

confidence: 95%

A Hybrid of Proposed Filtration and Feature Selections to Enhance the Model Performance

Sujatha¹,

Radha²

2021

IJST

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Objectives:Toextract and identify the subjective information of social media user from the unstructured data. To overcome the high dimensionality and sparsity those are the two major challenges in sentiment analysis of text datasets. To increase the model performance by using possibly minimum feature sets in a text classification problem. Methods: We proposed a new filtration method which is applied for the removal of correlated features and zero importance features in addition to the various feature selection methods. The various feature selections such as Mutual Info, Lasso, Recursive Feature Elimination and dimensionality reduction, Principal Component Analysis (PCA) have been used along with the proposed filtration to find the compelling features. This approach was evaluated using three Indian Government Schemes and these tweets were classified using Random Forest classifier. The performance was evaluated using various metrics such as accuracy, precision, recall, f1_score, log loss and roc-auc. Findings: In this research, we proposed a model for selecting relevant and non-correlated feature subsets from the unstructured dataset. From this model, accuracy of 92% with the minimum log loss 0.22 was achieved through the minimum number of feature set. Improvements: This study proves that the performance of the model will be improved by overcoming those two problems (dimensionality and sparsity). Here various feature selection methods have been applied with the proposed filtration in order to minimize the number of features. The computing time and the model performance will be improved as a result of decreasing the features. And this will be more effective in case of large datasets. Even though Random Forest performs well in high dimensional datasets we need some more optimization.

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Aspect based sentiment analysis for demonetization tweets by optimized recurrent neural network using fire fly-oriented multi-verse optimizer

Cited by 20 publications

References 48 publications

A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis

A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis

HAS: Hybrid Analysis of Sentiments for the perspective of customer review summarization

A Hybrid of Proposed Filtration and Feature Selections to Enhance the Model Performance

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