Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model

Mahto, Dashrath; Yadav, Subhash Chandra; Lalotra, Gotam Singh

doi:10.1155/2022/1068554

Cited by 11 publications

(3 citation statements)

References 45 publications

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“…A Semi-supervised deep embedded clustering model was designed by Dutta et al [25] to extract personality features from Kaggle personality dataset by learning cluster assignments and feature representations simultaneously. Recently, pre-trained models such as GloVe [13] and BERT [26] are significantly used in personality prediction task [27][28][29]. El-Demerdash et al [30] achieve competitive accuracy results for personality classification tasks on MyPersonality and Essays datasets by proposing an architecture that use transfer learning of some pre-trained models such as ELMo [31], ULMFiT [32] and BERT [26].…”

Section: B) Related Workmentioning

confidence: 99%

TranSentGAT: A Sentiment-Based Lexical Psycholinguistic Graph Attention Network for Personality Prediction

Bajestani,

Khalilzadeh,

Azarnoosh

et al. 2024

IEEE Access

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Extended use of social media, lead to personality detection from online content shared by users.While it has numerous applications in different areas such as recommendation systems, most of existing studies focus on superficial, statistical, and explicit user contents, ignoring the knowledge hidden in semantic features. In this study, we proposed a method to explore psycholinguistic knowledge hidden in deep levels of users data for the task of personality prediction. We proposed a method utilizing fine-tuned domain-specific BERT model to extract features from a sentence, and enriched the outputs by leveraging emotional information to highlight the important words. Furthermore, by conducting a double-way-attention mechanism we reflected the information from highlighted words into the whole knowledge extracted from inputs. Then, created a graph by considering extracted embeddings from last step as node features and developing a dynamic and task-realted learning approach to specify edges to connect different pairs of nodes based on a neural network, and leveraged graph attention network to predict personality traits. Finally, experimental results confirmed the effectiveness of our proposed model with 80.63% of accuracy, compared to other stateof-the-art studies for the essays dataset. Also, several ablations are conducted to illustrate and verify the impact of different sections and parameteres of the proposed architecture.

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Section: B) Related Workmentioning

confidence: 99%

TranSentGAT: A Sentiment-Based Lexical Psycholinguistic Graph Attention Network for Personality Prediction

Bajestani,

Khalilzadeh,

Azarnoosh

et al. 2024

IEEE Access

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“…19 The user's mood can be calculated using a tweet's positive and negative terms. 20 sentiment score ¼…”

Section: Sentiment Analysismentioning

confidence: 99%

“…The user’s mood can be calculated using a tweet's positive and negative terms. 20 P and N represent the number of positive and negative words in a tweet, while S is the sentiment score:…”

Section: Introductionmentioning

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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Analyzing Deep Neural Network Algorithms for Recognition of Emotions Using Textual Data

Kumar,

Babulal,

Mahto

et al. 2023

Lecture Notes in Networks and Systems

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Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model

Cited by 11 publications

References 45 publications

TranSentGAT: A Sentiment-Based Lexical Psycholinguistic Graph Attention Network for Personality Prediction

TranSentGAT: A Sentiment-Based Lexical Psycholinguistic Graph Attention Network for Personality Prediction

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

Analyzing Deep Neural Network Algorithms for Recognition of Emotions Using Textual Data

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