Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data

Behera, Ranjan Kumar; Jena, Monalisa; Rath, Santanu Kumar; Misra, Sanjay

doi:10.1016/j.ipm.2020.102435

Cited by 195 publications

(92 citation statements)

References 14 publications

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“…Behera et al. [17] proposed a hybrid model combining convolutional neural network (CNN) and long short term memory (LSTM) called Co-LSTM, which is highly adaptable with big social data.…”

Section: Literature Reviewmentioning

confidence: 99%

TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets

Satu

Khan

Mahmud

et al. 2021

Knowledge-Based Systems

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“…Behera et al. [17] proposed a hybrid model combining convolutional neural network (CNN) and long short term memory (LSTM) called Co-LSTM, which is highly adaptable with big social data.…”

Section: Literature Reviewmentioning

confidence: 99%

TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets

Satu

Khan

Mahmud

et al. 2021

Knowledge-Based Systems

View full text Add to dashboard Cite

“…Behera et al [ 15 ] proposed a Co-LSTM model combining CNN and LSTM; there were four datasets, IMDB [ 49 ], Airline Reviews [ 50 ], Self-Driving Car [ 51 ], and US Presidential Election [ 49 ]. The results were 83.13%, 94.96%, 86.43%, and 90.45%, respectively.…”

Section: Previous Workmentioning

confidence: 99%

“…On the other hand, Onan et al [ 20 , 21 ] utilized ensemble machine learning models. Paredes-Valverde et al [ 11 ] also applied CNN, Behera et al [ 15 ] used Co-CNN, and Li et al [ 16 ] used CNN-LSTM and CNN-BiLSTM. Finally, Onan et al [ 6 , 23 , 24 ] applied RNN, LSTM, and Bi-LSTM.…”

Section: Previous Workmentioning

confidence: 99%

Improving Arabic Sentiment Analysis Using CNN‐Based Architectures and Text Preprocessing

Mustafa

Sutcliffe

Sun

et al. 2021

Computational Intelligence and Neuroscience

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Sentiment analysis is an essential process which is important to many natural language applications. In this paper, we apply two models for Arabic sentiment analysis to the ASTD and ATDFS datasets, in both 2-class and multiclass forms. Model MC1 is a 2-layer CNN with global average pooling, followed by a dense layer. MC2 is a 2-layer CNN with max pooling, followed by a BiGRU and a dense layer. On the difficult ASTD 4-class task, we achieve 73.17%, compared to 65.58% reported by Attia et al., 2018. For the easier 2-class task, we achieve 90.06% with MC1 compared to 85.58% reported by Kwaik et al., 2019. We carry out experiments on various data splits, to match those used by other researchers. We also pay close attention to Arabic preprocessing and include novel steps not reported in other works. In an ablation study, we investigate the effect of two steps in particular, the processing of emoticons and the use of a custom stoplist. On the 4-class task, these can make a difference of up to 4.27% and 5.48%, respectively. On the 2-class task, the maximum improvements are 2.95% and 3.87%.

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“…The latter does not come with a file system other than Hadoop, so you need to combine it with a different file system based on the cloud [53]. Hadoop spends time running computationally complex machine learning algorithms [54,55], making Apache Spark 100 times faster than ever before. Also, trends and patterns which make it easy to diagnose and treat patients are been revealed by big data.…”

Section: Big Data In the Healthcare Systemmentioning

confidence: 99%

Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

Awotunde

Jimoh

Oladipo

et al. 2021

Communications in Computer and Information Science

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Malaria has been identified to be one of the most common diseases with a great public health problem globally and it is caused by mosquitos' parasites. This prevails in developing nations where healthcare facilities are not enough for the patients. The technological advancement in medicine has resulted in the collection of huge volumes of data from various sources in different formats. A reliable and early parasite-based diagnosis, identification of symptoms, disease monitoring, and prescription are crucial to decreasing malaria occurrence in Nigeria. Hence, the use of deep and machine learning models is essentials to reduce the effect of malaria-endemic and for better predictive models. Therefore, this paper proposes a framework to predict malaria-endemic in Nigeria. To predict the malaria-endemic well, both environmental and clinical data were used using Kwara State as a case study. The study used a deep learning algorithm as a classifier for the proposed system. Three locations were selected from Irepodun Local Government Areas of Kwara State with 34 months periodic pattern. Each location reacted differently based on environmental factors. The findings indicate that both factors are significant in malaria prediction and transmission. The LSTM algorithm provides an efficient method for detecting situations of widespread malaria.

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Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data

Cited by 195 publications

References 14 publications

TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets

TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets

Improving Arabic Sentiment Analysis Using CNN‐Based Architectures and Text Preprocessing

Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

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