Text classification algorithms for mining unstructured data: a SWOT analysis

Kumar, Akshi; Dabas, Vikrant; Hooda, Parul

doi:10.1007/s41870-017-0072-1

Cited by 56 publications

(34 citation statements)

References 15 publications

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“…Different kinds of steps are followed in this phase. Unnecessary texts are removed so that the text is being cleaned [15] . Lemmatization and punctuation are used to further refine the results.…”

Section: Related Workmentioning

confidence: 99%

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Prediction of COVID-19 growth and trend using machine learning approach

Gothai¹,

Thamilselvan²,

Rajalaxmi³

et al. 2023

Materials Today: Proceedings

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The Covid-19 Corona Virus, also known as SARS-CoV-2, has wreaked havoc around the world, and the condition is only getting worse.It is a pandemic disease spreading from person-to-person every day. Therefore, it is important to keep track the number of patients being affected. The current system gives the computerized data in a collective way which is very difficult to analyze and predict the growth of disease in a particular area and in the world. Machine learning algorithms can be used to successfully map the disease and its progression to solve this problem. Machine Learning, a branch of computer science, is critical in correctly distinguishing patients with the condition by analyzing their chest X-ray photographs. Supervised Machine learning models with associated algorithms (like LR, SVR and Time series algorithms) to analyze data for regression and classification helps in training the model to predict the number of total number of global confirmed cases who will be prone to the disease in the upcoming days. In this proposed work, the overall dataset of the world is being collected, preprocessed and the number of confirmed cases up to a particular date are extracted which is given as the training set to the model. The model is being trained by supervised machine learning algorithms to predict the growth of cases in the upcoming days. The experimental setup with the above mentioned algorithms shows that Time series Holt's model outperforms Linear Regression and Support Vector Regression algorithms.

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Section: Related Workmentioning

confidence: 99%

“…Various features are extracted from the preprocessed clinical reports and are converted into probabilistic values as per the semantics [15] . We use NumPy and Pandas library forextracting relevant features.…”

Section: Related Workmentioning

confidence: 99%

Prediction of COVID-19 growth and trend using machine learning approach

Gothai¹,

Thamilselvan²,

Rajalaxmi³

et al. 2023

Materials Today: Proceedings

View full text Add to dashboard Cite

show abstract

“…misinformation, disinformation and propaganda. Kumar et al, Sarwar et al [ 9 , 10 ] analysed textual data for predicting various diseseases. They showed that text classification showed better results in detecting the disease as well as any type of fraud from the text.…”

Section: Related Workmentioning

confidence: 99%

Identifying propaganda from online social networks during COVID-19 using machine learning techniques

Khanday

Khan

Rabani

2020

Int. j. inf. tecnol.

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COVID-19, affected the entire world because of its non-availability of vaccine. Due to social distancing online social networks are massively used in pandemic times. Information is being shared enormously without knowing the authenticity of the source. Propaganda is one of the type of information that is shared deliberately for gaining political and religious influence. It is the systematic and deliberate way of shaping opinion and influencing thoughts of a person for achieving the desired intention of a propagandist. Various propagandistic messages are being shared during COVID-19 about the deadly virus. We extracted data from twitter using its application program interface (API), Annotation is being performed manually. Hybrid feature engineering is performed for choosing the most relevant features.The binary classification of tweets is being performed with the help of machine learning algorithms. Decision tree gives better results among all other algorithms. For better results feature engineering may be improved and deep learning can be used for classification task.

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“…To extract the structured text for analytics from the unstructured bug reports/reviews, pre-processing is done [17]. 6 Google Web Scraper: https://chrome.google.com/webstore/detail /webscraper.…”

Section: B Pre-processingmentioning

confidence: 99%

A Particle Swarm Optimized Learning Model of Fault Classification in Web-Apps

et al. 2019

Self Cite

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The term web-app defines the current dynamic pragmatics of the website, where the user has control. Finding faults in such dynamic content is challenging, as to whether the fault is exposed or not depends on its execution path. Moreover, the complexity and uniqueness of each web application make fault assessment an extremely laborious and expensive task. Also, artificial fault injection models are run in controlled and simulated environments, which may not be representative of the real-world fault data. Classifying faults can intelligently enhance the quality of the web-apps by the assessment of probable faults. In this paper, an empirical study is conducted to classify faults in bug reports of three open-source web-apps (qaManager, bitWeaver, and WebCalendar) and reviews of two play store web-apps (Dineout: Reserve a Table and Wynk Music). Five supervised learning algorithms (naïve Bayesian, decision tree, support vector machines, K-nearest neighbor, and multi-layer perceptron) have been first evaluated based on the conventional term frequency-inverse document frequency (tf-idf) feature extraction method, and subsequently, a feature selection method to improve classifier performance is proposed using particle swarm optimization (a nature-inspired, meta-heuristic algorithm). This paper is a preliminary exploratory study to build an automated tool, which can optimally categorize faults. The empirical analysis validates that the particle swarm optimization for feature selection in fault classification task outperforms the tf-idf filter-based classifiers with an average accuracy gain of about 11% and nearly 26% average feature reduction. The highest accuracy of 93.35% is shown by the decision tree after feature selection.

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Text classification algorithms for mining unstructured data: a SWOT analysis

Cited by 56 publications

References 15 publications

Prediction of COVID-19 growth and trend using machine learning approach

Prediction of COVID-19 growth and trend using machine learning approach

Identifying propaganda from online social networks during COVID-19 using machine learning techniques

A Particle Swarm Optimized Learning Model of Fault Classification in Web-Apps

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