scholarly journals A New Method to Identify Short-Text Authors Using Combinations of Machine Learning and Natural Language Processing Techniques

2019 ◽  
Vol 159 ◽  
pp. 428-436
Author(s):  
Biveeken Vijayakumar ◽  
Muhammad Marwan Muhammad Fuad
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
New Method ◽  
Short Text ◽  
Processing Techniques

scholarly journals Generalized approach to sentiment analysis of short text messages in natural language processing

2020 ◽  
pp. 2-14
Author(s):  
Evrenii Polyakov ◽  
Leonid Voskov ◽  
Pavel Abramov ◽  
Sergey Polyakov
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Data Analysis ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Full Range ◽  
Text Messages ◽  
Basic Solution ◽  
Short Text

Introduction: Sentiment analysis is a complex problem whose solution essentially depends on the context, field of study andamount of text data. Analysis of publications shows that the authors often do not use the full range of possible data transformationsand their combinations. Only a part of the transformations is used, limiting the ways to develop high-quality classification models.Purpose: Developing and exploring a generalized approach to building a model, which consists in sequentially passing throughthe stages of exploratory data analysis, obtaining a basic solution, vectorization, preprocessing, hyperparameter optimization, andmodeling. Results: Comparative experiments conducted using a generalized approach for classical machine learning and deeplearning algorithms in order to solve the problem of sentiment analysis of short text messages in natural language processinghave demonstrated that the classification quality grows from one stage to another. For classical algorithms, such an increasein quality was insignificant, but for deep learning, it was 8% on average at each stage. Additional studies have shown that theuse of automatic machine learning which uses classical classification algorithms is comparable in quality to manual modeldevelopment; however, it takes much longer. The use of transfer learning has a small but positive effect on the classificationquality. Practical relevance: The proposed sequential approach can significantly improve the quality of models under developmentin natural language processing problems.

scholarly journals Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 218-239 ◽  
Author(s):  
Ravikumar Patel ◽  
Kalpdrum Passi
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Random Forest ◽  
Natural Language ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
World Cup ◽  
Part Of Speech ◽  
Twitter Data ◽  
Processing Techniques

In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.

scholarly journals Computerized Answer Grading

2021 ◽  
Vol 9 (VI) ◽  
pp. 618-619
Author(s):  
Anurag Langan
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computer Technology ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Grade Student ◽  
Processing Techniques

Grading student answers is a tedious and time-consuming task. A study had found that almost on average around 25% of a teacher's time is spent in scoring the answer sheets of students. This time could be utilized in much better ways if computer technology could be used to score answers. This system will aim to grade student answers using the various Natural Language processing techniques and Machine Learning algorithms available today.

Artificial Intelligence and Investing

2011 ◽  
pp. 237-240 ◽  
Author(s):  
Roy Rada
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Asset Valuation ◽  
Stock Valuation ◽  
Knowledge Based ◽  
Processing Techniques

The techniques of artificial intelligence include knowledgebased, machine learning, and natural language processing techniques. The discipline of investing requires data identification, asset valuation, and risk management. Artificial intelligence techniques apply to many aspects of financial investing, and published work has shown an emphasis on the application of knowledge-based techniques for credit risk assessment and machine learning techniques for stock valuation. However, in the future, knowledge-based, machine learning, and natural language processing techniques will be integrated into systems that simultaneously address data identification, asset valuation, and risk management.

Intelligent compilation of patent summaries using machine learning and natural language processing techniques

2020 ◽  
Vol 43 ◽  
pp. 101027 ◽  
Author(s):  
Amy J.C. Trappey ◽  
Charles V. Trappey ◽  
Jheng-Long Wu ◽  
Jack W.C. Wang
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing Techniques

scholarly journals Answer Script Evaluation using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 849-852
Author(s):  
Dr. K. Suresh
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Methods ◽  
Language Processing ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Text Extraction ◽  
Processing Techniques

The current way of checking answer scripts is hectic for the college. They need to manually check the answers and allocate the marks to the students. Our proposed system uses Machine Learning and Natural Language Processing techniques to beat this. Machine learning algorithms use computational methods to find out directly from data without hopping on predetermined rules. NLP algorithms identify specific entities within the text, explore for key elements during a document, run a contextual search for synonyms and detect misspelled words or similar entries, and more. Our algorithm performs similarity checking and also the number of words associated with the question exactly matched between two documents. It also checks whether the grammar is correctly used or not within the student's answer. Our proposed system performs text extraction and evaluation of marks by applying Machine Learning and Natural Language Processing techniques.

scholarly journals Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 444
Author(s):  
Isuri Anuradha Nanomi Arachchige ◽  
Priyadharshany Sandanapitchai ◽  
Ruvan Weerasinghe
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Systematic Review ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Mental Health Disorder ◽  
Online Support ◽  
Processing Techniques ◽  
Depression Disorder

Depression is a common mental health disorder that affects an individual’s moods, thought processes and behaviours negatively, and disrupts one’s ability to function optimally. In most cases, people with depression try to hide their symptoms and refrain from obtaining professional help due to the stigma related to mental health. The digital footprint we all leave behind, particularly in online support forums, provides a window for clinicians to observe and assess such behaviour in order to make potential mental health diagnoses. Natural language processing (NLP) and Machine learning (ML) techniques are able to bridge the existing gaps in converting language to a machine-understandable format in order to facilitate this. Our objective is to undertake a systematic review of the literature on NLP and ML approaches used for depression identification on Online Support Forums (OSF). A systematic search was performed to identify articles that examined ML and NLP techniques to identify depression disorder from OSF. Articles were selected according to the PRISMA workflow. For the purpose of the review, 29 articles were selected and analysed. From this systematic review, we further analyse which combination of features extracted from NLP and ML techniques are effective and scalable for state-of-the-art Depression Identification. We conclude by addressing some open issues that currently limit real-world implementation of such systems and point to future directions to this end.

scholarly journals Medical Applications using Blockchain and Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 3928-3932
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Blockchain Technology ◽  
Improving Accuracy ◽  
Processing Techniques ◽  
Cryptographic Techniques

Blockchain was particularly used in Cryptocurrency technologies. Prior to 20th century there was no other technologies for determining the health of a person naturally. At the dawn of the 21st Century machine learning played a vital role in determining the health of a person using various algorithms and natural language processing techniques. Now for every machine learning technique to work for it needs data. Data is very important as far as providing information is concerned. Data sharing plays a vital role in improving accuracy of techniques involved. Along the blockchain technology plays a vital role in this aspect. Thus, the merging of these two techniques involve provides highly accurate results in terms of machine learning with privacy and reliability of Blockchain technology. This technique uses natural language processing techniques which focuses basically mainly on healthcare techniques such as cancer detection, prediction of machines used in healthcare etc. Prior to healthcare which is used in blockchain it was used in cryptographic techniques only. Also, this technology can be used to provide medical suggestions to the doctors based on the condition of the patient. The accuracy of this method can be increased more using providing as much data as we can. This combination of Blockchain and machine learning algorithms can be used widely in healthcare, where the data is highly secured and there is no fear of data loss. This paper involves how combining these two technologies can be helpful in healthcare.

scholarly journals Prediction of Workplace Absenteeism Time using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 3489-3493
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Prediction Model ◽  
Language Processing ◽  
Negatively Associated ◽  
Root Cause ◽  
Lost Productivity ◽  
Processing Techniques ◽  
Available Information

Absenteeism in the workplace is a significant cause of lost productivity of the organization and the root cause of the company's performance to many employers. Managing absenteeism is inevitable, but making sudden changes without knowing the cause of the problem is a terrible mistake. This paper aims to develop a reliable workplace absenteeism prediction model using machine learning and natural language processing techniques to aid employers with analyzation of given minimal available information about the employees’ demographics. ‘Distance from residence to work,’ ‘disciplinary failure’ and ‘weight’ was negatively associated with absenteeism time in hours. ‘Age,’ ‘son,’ and ‘height’ were positively associated with absenteeism time in hours

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