Deep Sentiment Extraction using Fuzzy-Rule Based Deep Sentiment Analysis

Jasti, Sireesha; Kumar, G.V.S. Raj

doi:10.14569/ijacsa.2022.0130650

Cited by 2 publications

(4 citation statements)

References 20 publications

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“…Obtained results were contrasted with the ANOVA table (see Table 5) to check for the computed F ratio and the F critical value. The attained F ratio for seven (7) classes is 8.80 with (1,8) degrees of freedom at a significance level of 0.05 which when crossed-verified with the F critical value (the intersection of row 8 and column 1) from the distribution table is 5.32. Similarly, the obtained F ratio for binary classification is 7.58 with (1,8) Thus, since the obtained P-value, 0.017 and 0.024 for seven (7) and binary classifications respectively, is less than the α (significance) value 0.05, then the null hypothesis Ho is rejected.…”

Section: Table 4 -Averaged Accuracy Over Five (5) Iterations For Seve...mentioning

confidence: 63%

“…This is evident as the expression of opinions by humans is on the rise due to the invention of the internet where videos, vlogs, audio, and pictures serve as a medium for such opinion expression. SA typically involves the development of a model which classifies opinion into labeled polarities such as positive, negative, and or neutral classes [1]. These varied polarities necessitate that the raw data availabilities be utilized for mining opinions while also identifying their sentiments, as prior literature focuses on textual data, which may fail to generate accurate results [2].…”

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confidence: 99%

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Fuzzy Layered Convolution Neutral Network for Feature Level Fusion Based On Multimodal Sentiment Classification

Ayodele¹,

Harun²,

Yusoff³

2023

EmAIT

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Multimodal sentiment analysis (MSA) is one of the core research topics of natural language processing (NLP). MSA has become a challenge for scholars and is equally complicated for an appliance to comprehend. One study that supports MSdifficulties is the MSA, which is learning opinions, emotions, and attitudes in an audio-visual format. In order words, using such diverse modalities to obtain opinions and identify emotions is necessary. Such utilization can be achieved via modality datafusion;such as feature fusion. In handling the data fusion of such diverse modalities while obtaining high performance, a typical machine learning algorithm is Deep Learning (DL), particularly the Convolutional Neutral Network (CNN), which has the capacity to handle tasks of great intricacy and difficulty. In this paper, we present a CNN architecture with an integrated layer via fuzzy methodologies for MSA, a task yet to be explored in improving the accuracy performance of CNN for diverse inputs. Experiments conducted on a benchmark multimodal dataset, MOSI, obtaining 37.5% and 81% on seven (7) class and binary classification respectively, reveals an improved accuracy performance compared with the typical CNN, which acquired 28.9% and 78%, respectively.

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Section: Table 4 -Averaged Accuracy Over Five (5) Iterations For Seve...mentioning

confidence: 63%

mentioning

confidence: 99%

Fuzzy Layered Convolution Neutral Network for Feature Level Fusion Based On Multimodal Sentiment Classification

Ayodele¹,

Harun²,

Yusoff³

2023

EmAIT

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

“…Although the reset gate is supervised to learn short-term dependencies and generate the amount of information to forget, the LSTM network is the same 40 ; it is significantly simpler, quicker, and requires fewer gates to calculate. 44 1D Convolutional Neural Network (Conv1D) model. The CNN method generates a sequential model, indicating that layers are arranged sequentially.…”

Section: Deep Learning Methods Used For Sentiment Analysis Of Sacfmentioning

confidence: 99%

Sentiment analysis model for Airline customers’ feedback using deep learning techniques

Samir,

Abd-Elmegid,

Marie

2023

International Journal of Engineering Business Management

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Sentiment analysis (SA) has recently developed an automated approach for assessing sentiment, emotion, and these reviews or opinions to extract relevant and subjective information from text-based data. Analyzing sentiment on social networks, such as Twitter, has become a powerful means of learning about the users’ opinions and better understanding and satisfaction. However, it consumes time and energy to disperse and collect surveys from clients, often inaccurate and inconsistent, and evaluating and improving the accuracy of the methods in sentiment analysis is being hindered by the challenges encountered in Natural Language Processing (NLP). This paper uses NLP, text analysis, biometrics, and computational linguistics to detect and extract replies, moods, or emotions from Skytrax Airline Customers' Feedback SACF data. This research uses deep learning models to analyze various approaches applied to small SACF to solve sentiment analysis problems. We applied word embedding (Glove embedding models) to improve the sentiment classification performance of a series of datasets extensively utilized for feature extractions. Finally, a comparative study has been conducted on the SACF data analysis utilizing deep learning (DL)for evaluating the performance of the different models and input features, which is Recurrent Neural Networks (RNN), long short-term memory (LSTM), Gated Recurrent Unit (GRU), 1D Convolutional Neural Networks (CONV1D), and Bidirectional Encoder Representations from Transformers (BERT) for application to big datasets in 2019. This approach was assessed using each classification technique; the precision, recall, f1-score, and accuracy metrics for sentiment analysis have been identified. And The results show that LSTM outperforms in classification accuracy; the outcome was 91%.

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Deep Sentiment Extraction using Fuzzy-Rule Based Deep Sentiment Analysis

Cited by 2 publications

References 20 publications

Fuzzy Layered Convolution Neutral Network for Feature Level Fusion Based On Multimodal Sentiment Classification

Fuzzy Layered Convolution Neutral Network for Feature Level Fusion Based On Multimodal Sentiment Classification

Sentiment analysis model for Airline customers’ feedback using deep learning techniques

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