Deep Learning Based Sentiment Classification in Social Network Services Datasets

Wint, Zar Zar; Manabe, Yuki; Aritsugi, Masayoshi

doi:10.1109/bcd2018.2018.00022

Cited by 18 publications

(9 citation statements)

References 9 publications

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“…An innovative deep learning methodology with hybrid CNNs and Bi-LSTM features was demonstrated in Wint et al, 2018, that coupled the power of CNNs with Bi-LSTM. The authors achieved unique vectors of features provided as input to the LSTM layer using both separate pre-trained vectors of words.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Deep ConvNet Sentiment Classification Model with Word Embedding and BiLSTM Technique

Ranjan

Daniel

2023

ADCAIJ

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Sentiment Classification is a key area of natural language processing research that is frequently utilized in several industries. The goal of sentiment analysis is to figure out if a product or service received a negative or positive response. Sentiment analysis is widely utilized in several commercial fields to enhance the quality of services (QoS) for goods or services by gaining a better knowledge of consumer feedback. Deep learning provides cutting-edge achievements in a variety of complex fields. The goal of the study is to propose an improved approach for evaluating and categorising sentiments into different groups. This study proposes a novel hybridised model that combines the benefits of deep learning technologies Dual LSTM (Long Short Term Memory) and CNN (Convolution Neural Network) with the word embedding technique. The performance of three distinct word embedding approaches is compared in order to choose the optimal embedding for the proposed model's implementation. In addition, attention-based BiLSTM is used in a multi-convolutional approach. Standard measures were used to verify the validity of the suggested model's performance. The results show that the proposed model has a significantly enhanced accuracy of 96.56%, which is significantly better than existing models.

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Section: Related Workmentioning

confidence: 99%

An Optimized Deep ConvNet Sentiment Classification Model with Word Embedding and BiLSTM Technique

Ranjan

Daniel

2023

ADCAIJ

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“…Sentiment analysis has long been one of the most popular deep learning research areas. In [35], a novel deep learning architecture with mixed CNNs and BiLSTM characteristics (H2CBi) is proposed, combining the power of CNNs and BiLSTM. Authors used two distinct pre-trained word embeddings to produce different input images, which were then input into the LSTM.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review on Airlines Industries based on Sentiment Analysis and Topic Modeling

Ha,

Naseem,

Keya

et al. 2023

Preprint

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One of the most important things that managers need is feedback from customers about the quality of the products and services their firms offer. Getting feedback from customers is now typically done via online channels including social media, instant messaging, and review websites. However, due to the massive volume, a powerful tool is required for processing the data received from various sources. Sentiment analysis is a form of NLP that can instantly determine if a user is feeling positive, negative, or neutral towards a given topic. The researcher uses topic modeling to iteratively pick a subset of topics that best describes the whole. Many studies, some using topic modeling, have been conducted on the topic of the airline business over the past few years, with the majority of them focusing on sentiment analysis in a particular area or region. This article provides a comprehensive study of the airline industry in terms of sentiment analysis and topic modeling using machine learning and deep learning techniques so that customers may learn more about the most reputable and convenient airlines in the world. In this review paper, we enlisted overall 84 articles in different years and provide a statistical analysis of 60 articles based on sentiment analysis and topic modeling focusing on different types of machine learning and deep learning. The most important tasks involved in sentiment analysis of airline industries are discussed, and it is determined that sentiment analysis may be performed on various languages. In addition, a summary of popular datasets, essential characteristics of those datasets, machine learning and deep learning models applied to those datasets, accuracy acquired from those datasets, and a comparison of different machine and deep learning models are included in the survey. The fundamental objective of this study is to draw attention to the efficacy of machine and deep learning architectures in solving issues of various airline industries involving sentiment analysis and topic modeling.

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“…As a result, the researcher integrates the advantages of convolutions for obtaining native characteristics with an LSTM layer for determining sentiment long term dependency. A novel deep learning architecture with hybridized CNN and BiLSTM (H2CBi) set of features has been presented in (Wint et al, 2018) which combines the power of CNNs and BiLSTM. The researchers obtained distinct set of vectors of feature supplied as input for LSTM layer using two separate pretrained vectors of words.…”

Section: Related Workmentioning

confidence: 99%

Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques

Ranjan

Daniel

2022

ADCAIJ

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The fast growth of Internet and social media has resulted in a significant quantity of texts based review that is posted on the platforms like social media. In the age of social media, analyzing the emotional context of comments using machine learning technology helps in understanding of QoS for any product or service. Analysis and classification of user’s review helps in improving the QoS (Quality of Services). Machine Learning techniques have evolved as a great tool for performing sentiment analysis of user’s. In contrast to traditional classification models. Bidirectional Long Short-Term Memory (BiLSTM) has obtained substantial outcomes and Convolution Neural Network (CNN) has shown promising outcomes in sentiment classification. CNN can successfully retrieve local information by utilizing convolutions and pooling layers. BiLSTM employs dual LSTM orientations for increasing the background knowledge accessible to deep learning based models. The hybrid model proposed here is to utilize the advantages of these two deep learning based models. Tweets of users for reviews of Indian Railway Services have been used as data source for analysis and classification. Keras Embedding technique is used as input source to the proposed hybrid model. The proposed model receives inputs and generates features with lower dimensions which generate a classification result. The performance of proposed hybrid model was compared using Keras and Word2Vec and observed effective improvement in the response of the proposed model with an accuracy of 95.19%.

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Deep Learning Based Sentiment Classification in Social Network Services Datasets

Cited by 18 publications

References 9 publications

An Optimized Deep ConvNet Sentiment Classification Model with Word Embedding and BiLSTM Technique

An Optimized Deep ConvNet Sentiment Classification Model with Word Embedding and BiLSTM Technique

A Systematic Review on Airlines Industries based on Sentiment Analysis and Topic Modeling

Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques

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