scholarly journals Sentiment Analysis of Review Data Using Blockchain and LSTM to Improve Regulation for a Sustainable Market

2021 ◽  
Vol 17 (1) ◽  
pp. 1-19
Author(s):  
Zhihua Zhao ◽  
Zhihao Hao ◽  
Guancheng Wang ◽  
Dianhui Mao ◽  
Bob Zhang ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Data Storage ◽  
Short Term Memory ◽  
Text Data ◽  
Specific Product ◽  
Short Text ◽  
Research Areas ◽  
Regulation Strategies ◽  
Lstm Network ◽  
Regulatory Decisions

E-commerce has developed greatly in recent years, as such, its regulations have become one of the most important research areas in order to implement a sustainable market. The analysis of a large amount of reviews data generated in the shopping process can be used to facilitate regulation: since the review data is short text and it is easy to extract the features through deep learning methods. Through these features, the sentiment analysis of the review data can be carried out to obtain the users’ emotional tendency for a specific product. Regulators can formulate reasonable regulation strategies based on the analysis results. However, the data has many issues such as poor reliability and easy tampering at present, which greatly affects the outcome and can lead regulators to make some unreasonable regulatory decisions according to these results. Blockchain provides the possibility of solving these problems due to its trustfulness, transparency and unmodifiable features. Based on these, the blockchain can be applied for data storage, and the Long short-term memory (LSTM) network can be employed to mine reviews data for emotional tendencies analysis. In order to improve the accuracy of the results, we designed a method to make LSTM better understand text data such as reviews containing idioms. In order to prove the effectiveness of the proposed method, different experiments were used for verification, with all results showing that the proposed method can achieve a good outcome in the sentiment analysis leading to regulators making better decisions.

scholarly journals Aspect-level sentiment analysis merged with knowledge graph and graph convolutional neural network

2021 ◽  
Vol 2083 (4) ◽  
pp. 042044
Author(s):  
Zuhua Dai ◽  
Yuanyuan Liu ◽  
Shilong Di ◽  
Qi Fan
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Structural Information ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Text Data ◽  
Short Text ◽  
Fine Grained ◽  
Syntactic Information ◽  
Text Information

Abstract Aspect level sentiment analysis belongs to fine-grained sentiment analysis, w hich has caused extensive research in academic circles in recent years. For this task, th e recurrent neural network (RNN) model is usually used for feature extraction, but the model cannot effectively obtain the structural information of the text. Recent studies h ave begun to use the graph convolutional network (GCN) to model the syntactic depen dency tree of the text to solve this problem. For short text data, the text information is not enough to accurately determine the emotional polarity of the aspect words, and the knowledge graph is not effectively used as external knowledge that can enrich the sem antic information. In order to solve the above problems, this paper proposes a graph co nvolutional neural network (GCN) model that can process syntactic information, know ledge graphs and text semantic information. The model works on the “syntax-knowled ge” graph to extract syntactic information and common sense information at the same t ime. Compared with the latest model, the model in this paper can effectively improve t he accuracy of aspect-level sentiment classification on two datasets.

scholarly journals Quantum-Inspired Interactive Networks for Conversational Sentiment Analysis

2019 ◽  
Author(s):  
Yazhou Zhang ◽  
Qiuchi Li ◽  
Dawei Song ◽  
Peng Zhang ◽  
Panpan Wang
Keyword(s):  
Sentiment Analysis ◽  
Short Term Memory ◽  
Measurement Theory ◽  
Affective State ◽  
Interaction Dynamics ◽  
Influence Model ◽  
Interactive Networks ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Weak Influence

Conversational sentiment analysis is an emerging, yet challenging Artificial Intelligence (AI) subtask. It aims to discover the affective state of each participant in a conversation. There exists a wealth of interaction information that affects the sentiment of speakers. However, the existing sentiment analysis approaches are insufficient in dealing with this task due to ignoring the interactions and dependency relationships between utterances. In this paper, we aim to address this issue by modeling intrautterance and inter-utterance interaction dynamics. We propose an approach called quantum-inspired interactive networks (QIN), which leverages the mathematical formalism of quantum theory (QT) and the long short term memory (LSTM) network, to learn such interaction dynamics. Specifically, a density matrix based convolutional neural network (DM-CNN) is proposed to capture the interactions within each utterance (i.e., the correlations between words), and a strong-weak influence model inspired by quantum measurement theory is developed to learn the interactions between adjacent utterances (i.e., how one speaker influences another). Extensive experiments are conducted on the MELD and IEMOCAP datasets. The experimental results demonstrate the effectiveness of the QIN model.

scholarly journals TKCA: a timely keystroke-based continuous user authentication with short keystroke sequence in uncontrolled settings

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Lulu Yang ◽  
Chen Li ◽  
Ruibang You ◽  
Bibo Tu ◽  
Linghui Li
Keyword(s):  
Short Term Memory ◽  
State Of The Art ◽  
User Authentication ◽  
Natural Setting ◽  
Short Text ◽  
Current State ◽  
Continuous User ◽  
Series Of Experiments ◽  
Lstm Network ◽  
The Relationship

AbstractKeystroke-based behavioral biometrics have been proven effective for continuous user authentication. Current state-of-the-art algorithms have achieved outstanding results in long text or short text collected by doing some tasks. It remains a considerable challenge to authenticate users continuously and accurately with short keystroke inputs collected in uncontrolled settings. In this work, we propose a Timely Keystroke-based method for Continuous user Authentication, named TKCA. It integrates the key name and two kinds of timing features through an embedding mechanism. And it captures the relationship between context keystrokes by the Bidirectional Long Short-Term Memory (Bi-LSTM) network. We conduct a series of experiments to validate it on a public dataset - the Clarkson II dataset collected in a completely uncontrolled and natural setting. Experiment results show that the proposed TKCA achieves state-of-the-art performance with 8.28% of EER when using only 30 keystrokes and 2.78% of EER when using 190 keystrokes.

scholarly journals A multi domains short message sentiment classification using hybrid neural network architecture

2021 ◽  
Vol 10 (4) ◽  
pp. 2181-2191
Author(s):  
Devi Munandar ◽  
Andri Fachrur Rozie ◽  
Andria Arisal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Network Architecture ◽  
Short Term Memory ◽  
Limited Resource ◽  
Short Message ◽  
Short Text ◽  
Domain Specific ◽  
Bahasa Indonesia

Sentiment analysis of short texts is challenging because of its limited context of information. It becomes more challenging to be done on limited resource language like Bahasa Indonesia. However, with various deep learning techniques, it can give pretty good accuracy. This paper explores several deep learning methods, such as multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and builds combinations of those three architectures. The combinations of those three architectures are intended to get the best of those architecture models. The MLP accommodates the use of the previous model to obtain classification output. The CNN layer extracts the word feature vector from text sequences. Subsequently, the LSTM repetitively selects or discards feature sequences based on their context. Those advantages are useful for different domain datasets. The experiments on sentiment analysis of short text in Bahasa Indonesia show that hybrid models can obtain better performance, and the same architecture can be directly used in another domain-specific dataset.

scholarly journals A CNN-BiLSTM Model for Document-Level Sentiment Analysis

2019 ◽  
Vol 1 (3) ◽  
pp. 832-847 ◽  
Author(s):  
Maryem Rhanoui ◽  
Mounia Mikram ◽  
Siham Yousfi ◽  
Soukaina Barzali
Keyword(s):  
Sentiment Analysis ◽  
Short Term Memory ◽  
High Concentration ◽  
Short Term ◽  
Term Memory ◽  
Short Text ◽  
Opinion Analysis ◽  
Large Size ◽  
Long Short Term Memory ◽  
Document Level

Document-level sentiment analysis is a challenging task given the large size of the text, which leads to an abundance of words and opinions, at times contradictory, in the same document. This analysis is particularly useful in analyzing press articles and blog posts about a particular product or company, and it requires a high concentration, especially when the topic being discussed is sensitive. Nevertheless, most existing models and techniques are designed to process short text from social networks and collaborative platforms. In this paper, we propose a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) models, with Doc2vec embedding, suitable for opinion analysis in long texts. The CNN-BiLSTM model is compared with CNN, LSTM, BiLSTM and CNN-LSTM models with Word2vec/Doc2vec embeddings. The Doc2vec with CNN-BiLSTM model was applied on French newspapers articles and outperformed the other models with 90.66% accuracy.

scholarly journals Sentiment Analysis of Novel Review Using Long Short-Term Memory Method

2019 ◽  
Vol 13 (3) ◽  
pp. 209
Author(s):  
Muh Amin Nurrohmat ◽  
Azhari SN
Keyword(s):  
Sentiment Analysis ◽  
Short Term Memory ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Short Term ◽  
Bayes Method ◽  
Text Data ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Naive Bayes Method

The rapid development of the internet and social media and a large amount of text data has become an important research subject in obtaining information from the text data. In recent years, there has been an increase in research on sentiment analysis in the review text to determine the polarity of opinion on social media. However, there are still few studies that apply the deep learning method, namely Long Short-Term Memory for sentiment analysis in Indonesian texts.This study aims to classify Indonesian novel novels based on positive, neutral and negative sentiments using the Long Short-Term Memory (LSTM) method. The dataset used is a review of Indonesian language novels taken from the goodreads.com site. In the testing process, the LSTM method will be compared with the Naïve Bayes method based on the calculation of the values of accuracy, precision, recall, f-measure.Based on the test results show that the Long Short-Term Memory method has better accuracy results than the Naïve Bayes method with an accuracy value of 72.85%, 73% precision, 72% recall, and 72% f-measure compared to the results of the Naïve Bayes method accuracy with accuracy value of 67.88%, precision 69%, recall 68%, and f-measure 68%.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

A Review on Sentiment Classification: Natural Language Understanding

2019 ◽  
Vol 13 (1) ◽  
pp. 20-27 ◽  
Author(s):  
Srishty Jindal ◽  
Kamlesh Sharma
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Natural Language Understanding ◽  
Business Analytics ◽  
Language Understanding ◽  
Text Data ◽  
Data Set ◽  
Market Positioning ◽  
Illegal Activities

Background: With the tremendous increase in the use of social networking sites for sharing the emotions, views, preferences etc. a huge volume of data and text is available on the internet, there comes the need for understanding the text and analysing the data to determine the exact intent behind the same for a greater good. This process of understanding the text and data involves loads of analytical methods, several phases and multiple techniques. Efficient use of these techniques is important for an effective and relevant understanding of the text/data. This analysis can in turn be very helpful in ecommerce for targeting audience, social media monitoring for anticipating the foul elements from society and take proactive actions to avoid unethical and illegal activities, business analytics, market positioning etc. Method: The goal is to understand the basic steps involved in analysing the text data which can be helpful in determining sentiments behind them. This review provides detailed description of steps involved in sentiment analysis with the recent research done. Patents related to sentiment analysis and classification are reviewed to throw some light in the work done related to the field. Results: Sentiment analysis determines the polarity behind the text data/review. This analysis helps in increasing the business revenue, e-health, or determining the behaviour of a person. Conclusion: This study helps in understanding the basic steps involved in natural language understanding. At each step there are multiple techniques that can be applied on data. Different classifiers provide variable accuracy depending upon the data set and classification technique used.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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