SENTIMENT ANALYSIS OF ELECTRIC CARS USING RECURRENT NEURAL NETWORK METHOD IN INDONESIAN TWEETS

Sentiment analysis is computational research of the opinions of many people who are textually expressed against a particular topic. Twitter is the most popular communication tool among Internet users today to express their opinions. Deep Learning is a solution to allow computers to learn from experience and understand the world in terms of the hierarchy concept. Deep Learning objectives replace manual assignments with learning. The development of deep learning has a set of algorithms that focus on learning data representation. The recurrent Neural Network is one of the machine learning methods included in Deep learning because the data is processed through multi-players. RNN is also an algorithm that can recall the input with internal memory, therefore it is suitable for machine learning problems involving sequential data. The study aims to test models that have been created from tweets that are positive, negative, and neutral sentiment to determine the accuracy of the models. The models have been created using the Recurrent Neural Network when applied to tweet classifications to mark the individual classes of Indonesian-language tweet data sentiment. From the experiments conducted, results on the built system showed that the best test results in the tweet data with the RNN method using Confusion Matrix are with Precision 0.618, Recall 0.507 and Accuracy 0.722 on the data amounted to 3000 data and comparative data training and data testing of ratio data 80:20

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A Framework for Sentiment Analysis of Telugu Tweets

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1602.089620 ◽

2020 ◽

Vol 9 (6) ◽

pp. 523-525

Keyword(s):

Neural Network ◽

Machine Learning ◽

Social Media ◽

Deep Learning ◽

Sentiment Analysis ◽

Recurrent Neural Network ◽

English Language ◽

Research Work ◽

Learning Approaches ◽

Text Data

Now a day Social Media like Facebook, twitter and Instagram is major Sources for people to share their emotions based on the current situations in society. By knowing the interesting patterns in it, a government/appropriate person for that situation can take good and useful decisions. Sentiment analysis is a method where people can extract the useful information from the text like the emotions (happy, sad, and neutral) of people. Much research work was been underdoing in the area of sentiment analysis. Among that work the Machine learning and Deep learning approaches plays a maximum role. Existing works on sentiment analysis is going in the English language. In this paper, proposed a novel framework that specifically designed to do sentiment analysis of the text data, that available in the telugu language. The proposed framework was integrated with the word embedding model Word2Vec, language translator and deep learning approaches like Recurrent Neural Network and Navie base algorithms to collect and analyse the sentiment in tweeter data that present in telugu language. The results shows effective in terms of accuracy, precision and specificity.

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PARROT is a flexible recurrent neural network framework for analysis of large protein datasets

eLife ◽

10.7554/elife.70576 ◽

2021 ◽

Vol 10 ◽

Author(s):

Daniel Griffith ◽

Alex S Holehouse

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

High Throughput ◽

Recurrent Neural Network ◽

Transcriptional Activation ◽

Network Architecture ◽

Learning Approaches ◽

Large Protein ◽

Protein Datasets

The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine learning approaches are increasingly being utilized due to their ability to infer complex nonlinear patterns from high-dimensional data. Despite their effectiveness, machine learning (and in particular deep learning) approaches are not always accessible or easy to implement for those with limited computational expertise. Here we present PARROT, a general framework for training and applying deep learning-based predictors on large protein datasets. Using an internal recurrent neural network architecture, PARROT is capable of tackling both classification and regression tasks while only requiring raw protein sequences as input. We showcase the potential uses of PARROT on three diverse machine learning tasks: predicting phosphorylation sites, predicting transcriptional activation function of peptides generated by high-throughput reporter assays, and predicting the fibrillization propensity of amyloid beta with data generated by deep mutational scanning. Through these examples, we demonstrate that PARROT is easy to use, performs comparably to state-of-the-art computational tools, and is applicable for a wide array of biological problems.

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Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

Sensors ◽

10.3390/s20061576 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1576 ◽

Cited By ~ 13

Author(s):

Li Zhu ◽

Lianghao Huang ◽

Linyu Fan ◽

Jinsong Huang ◽

Faming Huang ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Environmental Factors ◽

Recurrent Neural Network ◽

Landslide Susceptibility ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Predictive Rate

Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.

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Deep Learning Approaches for Textual Sentiment Analysis

Handbook of Research on Emerging Trends and Applications of Machine Learning - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-9643-1.ch009 ◽

2020 ◽

pp. 171-182 ◽

Cited By ~ 1

Author(s):

Tamanna Sharma ◽

Anu Bajaj ◽

Om Prakash Sangwan

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Natural Language Processing ◽

Natural Language ◽

Sentiment Analysis ◽

Language Processing ◽

Machine Learning Techniques ◽

Computational Technique ◽

Learning Techniques

Sentiment analysis is computational measurement of attitude, opinions, and emotions (like positive/negative) with the help of text mining and natural language processing of words and phrases. Incorporation of machine learning techniques with natural language processing helps in analysing and predicting the sentiments in more precise manner. But sometimes, machine learning techniques are incapable in predicting sentiments due to unavailability of labelled data. To overcome this problem, an advanced computational technique called deep learning comes into play. This chapter highlights latest studies regarding use of deep learning techniques like convolutional neural network, recurrent neural network, etc. in sentiment analysis.

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Analysis of deep learning models for network anomaly detection in Internet of Things

Information and Control Systems ◽

10.31799/1684-8853-2021-1-28-37 ◽

2021 ◽

pp. 28-37

Author(s):

Diana Gaifilina ◽

Igor Kotenko

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Internet Of Things ◽

Anomaly Detection ◽

Recurrent Neural Network ◽

Network Traffic ◽

Learning Models ◽

Network Anomaly Detection ◽

Machine Learning Models

Introduction: The article discusses the problem of choosing deep learning models for detecting anomalies in Internet of Things (IoT) network traffic. This problem is associated with the necessity to analyze a large number of security events in order to identify the abnormal behavior of smart devices. A powerful technology for analyzing such data is machine learning and, in particular, deep learning. Purpose: Development of recommendations for the selection of deep learning models for anomaly detection in IoT network traffic. Results: The main results of the research are comparative analysis of deep learning models, and recommendations on the use of deep learning models for anomaly detection in IoT network traffic. Multilayer perceptron, convolutional neural network, recurrent neural network, long short-term memory, gated recurrent units, and combined convolutional-recurrent neural network were considered the basic deep learning models. Additionally, the authors analyzed the following traditional machine learning models: naive Bayesian classifier, support vector machines, logistic regression, k-nearest neighbors, boosting, and random forest. The following metrics were used as indicators of anomaly detection efficiency: accuracy, precision, recall, and F-measure, as well as the time spent on training the model. The constructed models demonstrated a higher accuracy rate for anomaly detection in large heterogeneous traffic typical for IoT, as compared to conventional machine learning methods. The authors found that with an increase in the number of neural network layers, the completeness of detecting anomalous connections rises. This has a positive effect on the recognition of unknown anomalies, but increases the number of false positives. In some cases, preparing traditional machine learning models takes less time. This is due to the fact that the application of deep learning methods requires more resources and computing power. Practical relevance: The results obtained can be used to build systems for network anomaly detection in Internet of Things traffic.

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Klasifikasi Teks Hadis Bukhari Terjemahan Indonesia Menggunakan Recurrent Convolutional Neural Network (CRNN)

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021853750 ◽

2021 ◽

Vol 8 (5) ◽

pp. 907

Author(s):

Muhammad Yuslan Abu Bakar ◽

Adiwijaya Adiwijaya

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Recurrent Neural Network ◽

Recurrent Neural Networks ◽

Spatial Information ◽

Contextual Information ◽

Computing Time ◽

Sequential Data ◽

Text Data

Hadis merupakan sumber hukum dan pedoman kedua bagi umat Islam setelah Al-Qur’an dan banyak sekali hadis yang telah diriwayatkan oleh para ahli hadis selama ini. Penelitian ini membangun sebuah sistem yang dapat melakukan klasifikasi teks hadis Bukhari terjemahan berbahasa Indonesia. Topik ini diangkat untuk memenuhi kebutuhan umat Islam dalam mengetahui apa saja informasi mengenai anjuran dan larangan yang terdapat dalam suatu hadis. Klasifikasi teks memiliki tantangannya tersendiri terkait dengan jumlah fitur yang sangat banyak (dimensi sangat besar) sehingga waktu komputasi menjadi besar dan mengakibatkan sulitnya mendapatkan hasil yang optimal. Pada penelitian ini, digunakan salah satu metode hibrid dalam dunia deep learning dengan menggabungkan Convolutional Neural Network dan Recurrent Neural Network, yaitu Convolutional Recurrent Neural Network (CRNN). Convolutional Neural Network dipilih sebagai metode seleksi dan reduksi data dikarenakan dapat menangkap informasi spasial yang saling berhubungan dan berkorelasi. Sementara Recurrent Neural Network digunakan sebagai metode klasifikasi dengan mengusung kemampuan utamanya yaitu dapat menangkap informasi kontekstual yang sangat panjang khususnya pada data sekuens seperti data teks dengan mengandalkan ‘memori’ yang dimilikinya. Hasil penelitian menyajikan beberapa hasil klasifikasi menggunakan deep learning, dimana hasil akurasi terbaik diberikan oleh Convolutional Recurrent Neural Network (CRNN), yakni sebesar 80.79%. Abstract Hadith is a source of law and guidance for Muslims after the Qur'an and many hadith have been narrated by hadith experts so far. This research builds a system that can classify Bukhari hadith in Indonesian translations. This topic was raised to meet the needs of Muslims in knowing what information about the suggestions and prohibitions that exist in a hadith. Text classification has its own challenges related to several features whose dimensions are very large so that it increases computing time and causes difficulties in getting optimal results. This research uses a hybrid method in deep learning by combining a Convolutional Neural Network and a Recurrent Neural Network, namely Convolutional Recurrent Neural Network (CRNN). Convolutional Neural Network was chosen as a method of selecting and reducing data that can be determined as spatial information that is interrelated and correlated. While Recurrent Neural Networks are used as a classification method by carrying out capabilities that can be used as very long contextual information specifically on sequential data such as text data by relying on the ‘memory’ it has. This research presents several classification results using deep learning, where the best accuracy results are given by the Convolutional Recurrent Neural Network (CRNN), which is equal to 80.79%.

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Micro Clustering Methodology for Document Objects using Deep Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b3716.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 1546-1552

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Recurrent Neural Network ◽

Large Data ◽

Heterogeneous Data ◽

Machine Learning Algorithms ◽

Volume Data ◽

Data Set ◽

Clustering And Classification

Large data clustering and classification is a very challenging task in data mining. Various machine learning and deep learning systems have been proposed by many researchers on a different dataset. Data volume, data size and structure of data may affect the time complexity of the system. This paper described a new document object classification approach using deep learning (DL) and proposed a recurrent neural network (RNN) for classification with a micro-clustering approach.TF-IDF and a density-based approach are used to store the best features. The plane work used supervised learning method and it extracts features set called as BK of the desired classes. once the training part completed then proceeds to figure out the particular test instances with the help of the planned classification algorithm. Recurrent Neural Network categorized the particular test object according to their weights. The system can able to work on heterogeneous data set and generate the micro-clusters according to classified results. The system also carried out experimental analysis with classical machine learning algorithms. The proposed algorithm shows higher accuracy than the existing density-based approach on different data sets.

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Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning

Frontiers in Communications and Networks ◽

10.3389/frcmn.2021.656786 ◽

2021 ◽

Vol 2 ◽

Author(s):

Danshi Wang ◽

Min Zhang

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Optical Communication ◽

Optical Communications ◽

Channel Modeling ◽

Modeling Method ◽

Learning Performance ◽

Sequential Data

Techniques from artificial intelligence have been widely applied in optical communication and networks, evolving from early machine learning (ML) to the recent deep learning (DL). This paper focuses on state-of-the-art DL algorithms and aims to highlight the contributions of DL to optical communications. Considering the characteristics of different DL algorithms and data types, we review multiple DL-enabled solutions to optical communication. First, a convolutional neural network (CNN) is used for image recognition and a recurrent neural network (RNN) is applied for sequential data analysis. A variety of functions can be achieved by the corresponding DL algorithms through processing the different image data and sequential data collected from optical communication. A data-driven channel modeling method is also proposed to replace the conventional block-based modeling method and improve the end-to-end learning performance. Additionally, a generative adversarial network (GAN) is introduced for data augmentation to expand the training dataset from rare experimental data. Finally, deep reinforcement learning (DRL) is applied to perform self-configuration and adaptive allocation for optical networks.

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Perbandingan Model Deep Learning untuk Klasifikasi Sentiment Analysis dengan Teknik Natural Languange Processing

Jurnal Teknologi dan Manajemen Informatika ◽

10.26905/jtmi.v7i2.6506 ◽

2021 ◽

Vol 7 (2) ◽

pp. 113-121

Author(s):

Firman Pradana Rachman

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Sentiment Analysis ◽

Recurrent Neural Networks ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Setiap orang mempunyai pendapat atau opini terhadap suatu produk, tokoh masyarakat, atau pun sebuah kebijakan pemerintah yang tersebar di media sosial. Pengolahan data opini itu di sebut dengan sentiment analysis. Dalam pengolahan data opini yang besar tersebut tidak hanya cukup menggunakan machine learning, namun bisa juga menggunakan deep learning yang di kombinasikan dengan teknik NLP (Natural Languange Processing). Penelitian ini membandingkan beberapa model deep learning seperti CNN (Convolutional Neural Network), RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory) dan beberapa variannya untuk mengolah data sentiment analysis dari review produk amazon dan yelp.

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Machine Learning Versus Deep Learning Performances on the Sentiment Analysis of Product Reviews

International Journal of Machine Learning and Computing ◽

10.18178/ijmlc.2021.11.2.1021 ◽

2021 ◽

Vol 11 (2) ◽

pp. 103-109

Author(s):

Pumrapee Poomka ◽

◽

Nittaya Kerdprasop ◽

Kittisak Kerdprasop

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Computational Models ◽

Shopping Mall ◽

Machine Learning Techniques ◽

Support Vector ◽

Product Reviews ◽

Review Section

At this current digital era, business platforms have been drastically shifted toward online stores on internet. With the internet-based platform, customers can order goods easily using their smart phones and get delivery at their place without going to the shopping mall. However, the drawback of this business platform is that customers do not really know about the quality of the products they ordered. Therefore, such platform service often provides the review section to let previous customers leave a review about the received product. The reviews are a good source to analyze customer's satisfaction. Business owners can assess review trend as either positive or negative based on a feedback score that customers had given, but it takes too much time for human to analyze this data. In this research, we develop computational models using machine learning techniques to classify product reviews as positive or negative based on the sentiment analysis. In our experiments, we use the book review data from amazon.com to develop the models. For a machine learning based strategy, the data had been transformed with the bag of word technique before developing models using logistic regression, naïve bayes, support vector machine, and neural network algorithms. For a deep learning strategy, the word embedding is a technique that we used to transform data before applying the long short-term memory and gated recurrent unit techniques. On comparing performance of machine learning against deep learning models, we compare results from the two methods with both the preprocessed dataset and the non-preprocessed dataset. The result is that the bag of words with neural network outperforms other techniques on both non-preprocess and preprocess datasets.

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