An ensemble approach to stabilize the features for multi-domain sentiment analysis using supervised machine learning

Ghosh, Monalisa; Sanyal, Goutam

doi:10.1186/s40537-018-0152-5

Cited by 45 publications

(21 citation statements)

References 30 publications

(35 reference statements)

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“…Our deep CNN with sentiment analyzed word vectors perform slightly better than that of a contextual bi-directional long-short network [ 74 ]. While the ensemble approach of statistical kernels for movie review analysis [ 76 ] scores a dip lower than the deep neural counterparts. Also, a similar approach with sentiment lexicons [ 20 ] also performs well in parity with the other mentioned works here.…”

Section: Comparative Evaluationmentioning

confidence: 99%

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Das

Kolya²

2021

Evol. Intel.

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Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the full computational potential of deep learning models. Typically, these research works are carried either with a popular open-source data corpus, or self-extracted short phrase texts from Twitter, Reddit, or web-scrapped text data from other resources. Rarely do we see a large amount of data on a current ongoing event is being collected and cultured further. Also, an even more complex task would be to model the data from a currently ongoing event, not only for scaling the sentiment accuracy but also for making a predictive analysis for the same. In this paper, we propose a novel approach for achieving sentiment evaluation accuracy by using a deep neural network on live-streamed tweets on Coronavirus and future case growth prediction. We develop a large tweet corpus exclusively based on the Coronavirus tweets. We split the data into train and test sets, alongside we perform polarity classification and trend analysis. The refined outcome from the trend analysis helps to train the data to provide an incremental learning curvature for our neural network, and we obtain an accuracy of 90.67%. Finally, we provide a statistical-based future prediction for Coronavirus cases growth. Not only our model outperforms several previous state-of-art experiments in overall sentiment accuracy comparison for similar tasks, but it also maintains a throughout performance stability among all the test cases when tested with several popular open-source text corpora.

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Section: Comparative Evaluationmentioning

confidence: 99%

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Das

Kolya²

2021

Evol. Intel.

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“…For the experimental evaluation, a repeated strati ed 5-fold cross validation was used [38]. Furthermore, the performance of the classi ers was also evaluated in terms of the recall, f1-score and precision as additional evaluation criteria [39]. A brief description of these metrics is given below.…”

Section: Validationmentioning

confidence: 99%

Explainable Machine Learning for Knee Osteoarthritis Diagnosis Based on a Novel Fuzzy Feature Selection Methodology

Kokkotis

Ntakolia

Moustakidis³

et al. 2021

Preprint

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Knee Osteoarthritis (ΚΟΑ) is a degenerative joint disease of the knee that results from the progressive loss of cartilage. Due to KOA’s multifactorial nature and the poor understanding of its pathophysiology, there is a need for reliable tools that will reduce diagnostic errors made by clinicians. The existence of public databases has facilitated the advent of advanced analytics in KOA research however the heterogeneity of the available data along with the observed high feature dimensionality make this diagnosis task difficult. The objective of the present study is to provide a robust Feature Selection (FS) methodology that could: (i) handle the multidimensional nature of the available datasets and (ii) alleviate the defectiveness of existing feature selection techniques towards the identification of important risk factors which contribute to KOA diagnosis. For this aim, we used multidisciplinary data obtained from the Osteoarthritis Initiative database for individuals without or with KOA. The proposed fuzzy ensemble feature selection methodology aggregates the results of several FS algorithms (filter, wrapper and embedded ones) based on fuzzy logic. The effectiveness of the proposed methodology was evaluated using an extensive experimental setup that involved multiple competing FS algorithms and several well-known ML models. A 73.55 % classification accuracy was achieved by the best performing model (Random Forest classifier) on a group of twenty-one selected risk factors. Explainability analysis was finally performed to quantify the impact of the selected features on the model’s output thus enhancing our understanding of the rationale behind the decision-making mechanism of the best model.

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“…Before applying the supervised Machine Learning algorithm, data was preprocessed using POS Tagging, Term Frequency Inverse Document Frequency (TF-IDF), Stop Words Removal, Tokenization to obtain the final N-Gram feature set [18]. In [19], the authors used the concept of Ensemble Framework to carry out Sentiment Analysis. Three base classifiers used in work are NB, ME and SVM with various ensemble options: fixed combination, weighted combination and meta-classifier combination.…”

Section: Related Workmentioning

confidence: 99%

Efficient Framework for Sentiment Classification Using Apriori Based Feature Reduction

Jain

Jain²

2018

ICST Transactions on Scalable Information Systems

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This paper proposes a novel feature selection method for Sentiment Classification. UCI ML Dataset is selected having a textual review from three domains (IMDB Movie, AMAZON Product, and YELP restaurant). Text pre-processing and feature selection technique is applied to the dataset. A Novel Feature Selection approach using Association Rule Mining is presented in which Sentence is converted in binary form and Apriori Algorithm is applied to reduce the dataset. Four Machine Learning algorithms: Naïve Bayes, Support Vector Machine, Random Forest & Logistic Regression to implement experiment. The proposed approach shows an accuracy improvement of 4.2%, 4.9% & 5.9% for IMDB, Amazon & Yelp domain datasets, respectively. Compared with the Genetic Algorithm, Principal Component Analysis, Chi-Square, and Relief based feature selection, the proposed method shows an accuracy improvement of 9.8%, 0.4%, 0.6% & 1.9%, respectively.

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An ensemble approach to stabilize the features for multi-domain sentiment analysis using supervised machine learning

Cited by 45 publications

References 30 publications

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Explainable Machine Learning for Knee Osteoarthritis Diagnosis Based on a Novel Fuzzy Feature Selection Methodology

Efficient Framework for Sentiment Classification Using Apriori Based Feature Reduction

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