Natural Language Processing (NLP): An Introduction

Egger, Roman; Gokce, Enes

doi:10.1007/978-3-030-88389-8_15

Cited by 23 publications

(6 citation statements)

References 33 publications

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“…This ensures quality and a good representation of data, which is extremely important to begin any analysis. In this project, Natural Language Processing (NLP) [6][7][8] is used by importing the Natural Language Toolkit (NLTK) library as the medium to screen and clean the data. Various techniques for processing natural language context are:…”

Section: Pre-processing the Tweetsmentioning

confidence: 99%

Conceding Sentiment Prognosis on Twitter Data

Malhotra,

Sethi

2024

J. Comput. Mech. Manag

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Twitter is the biggest micro-blogging website that gives people a platform to share their opinions about any new happenings around the world. The size of tweets is generally short which makes it very suitable for opinion mining. The key focus of the paper is to analyze the feelings and ideas. In this paper, analysis is done on the classification of tweets on a particular keyword. The tweets related to the given keyword are collected, analyzed, and the result is generated in the form of percentage of positive, neutral and negative sentiments, which gives us a sense of overall sentiment of the keyword. Further, Classification is done using supervised learning algorithms and the best among these will be found by calculating the accuracy of each.

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Section: Pre-processing the Tweetsmentioning

confidence: 99%

Conceding Sentiment Prognosis on Twitter Data

Malhotra,

Sethi

2024

J. Comput. Mech. Manag

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“…Natural Language Processing (NLP) is one of the areas that have been applied in various domains to produce much-needed applications [37,38]. Sentiment analysis is a subbranch of NLP [39] in which aspects are identified.…”

Section: Related Workmentioning

confidence: 99%

A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

Haq,

Daudpota,

Imran

et al. 2023

CMC

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The Internet has become one of the significant sources for sharing information and expressing users' opinions about products and their interests with the associated aspects. It is essential to learn about product reviews; however, to react to such reviews, extracting aspects of the entity to which these reviews belong is equally important. Aspect-based Sentiment Analysis (ABSA) refers to aspects extracted from an opinionated text. The literature proposes different approaches for ABSA; however, most research is focused on supervised approaches, which require labeled datasets with manual sentiment polarity labeling and aspect tagging. This study proposes a semisupervised approach with minimal human supervision to extract aspect terms by detecting the aspect categories. Hence, the study deals with two main sub-tasks in ABSA, named Aspect Category Detection (ACD) and Aspect Term Extraction (ATE). In the first sub-task, aspects categories are extracted using topic modeling and filtered by an oracle further, and it is fed to zero-shot learning as the prompts and the augmented text. The predicted categories are the input to find similar phrases curated with extracting meaningful phrases (e.g., Nouns, Proper Nouns, NER (Named Entity Recognition) entities) to detect the aspect terms. The study sets a baseline accuracy for two main sub-tasks in ABSA on the Multi-Aspect Multi-Sentiment (MAMS) dataset along with SemEval-2014 Task 4 subtask 1 to show that the proposed approach helps detect aspect terms via aspect categories.

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“…In the context of EDM, the data can have diverse forms: when it is in the form of words, sentences or paragraphs, the computer needs to understand it. The Natural Language Processing (NLP) is an established field that makes machines able to make sense of human language (Egger & Gokce, 2022).…”

Section: Overview Of Educational Data Mining and Machine Learningmentioning

confidence: 99%

Categorizing Well-Written Course Learning Outcomes Using Machine Learning

Kacem¹,

Al-Shehri

Qaid

2022

JITE:IIP

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Aim/Purpose: This paper presents a machine learning approach for analyzing Course Learning Outcomes (CLOs). The aim of this study is to find a model that can check whether a CLO is well written or not. Background: The use of machine learning algorithms has been, since many years, a prominent solution to predict learner performance in Outcome Based Education. However, the CLOs definition is still presenting a big handicap for faculties. There is a lack of supported tools and models that permit to predict whether a CLO is well written or not. Consequently, educators need an expert in quality and education to validate the outcomes of their courses. Methodology: A novel method named CLOCML (Course Learning Outcome Classification using Machine Learning) is proposed in this paper to develop predictive models for CLOs paraphrasing. A new dataset entitled CLOC (Course Learning Outcomes Classes) for that purpose has been collected and then undergone a pre-processing phase. We compared the performance of 4 models for predicting a CLO classification. Those models are Support Vector Machine (SVM), Random Forest, Naive Bayes and XGBoost. Contribution: The application of CLOCML may help faculties to make well-defined CLOs and then correct CLOs' measures in order to improve the quality of education addressed to their students. Findings: The best classification model was SVM. It was able to detect the CLO class with an accuracy of 83%. Recommendations for Practitioners: We would recommend both faculties’ members and quality reviewers to make an informed decision about the nature of a given course outcome. Recommendation for Researchers: We would highly endorse that the researchers apply more machine learning models for CLOs of various disciplines and compare between them. We would also recommend that future studies investigate on the importance of the definition of CLOs and its impact on the credibility of Key Performance Indicators (KPIs) values during accreditation process. Impact on Society: The findings of this study confirm the results of several other researchers who use machine learning in outcome-based education. The definition of right CLOs will help the student to get an idea about the performances that will be measured at the end of a course. Moreover, each faculty can take appropriate actions and suggest suitable recommendations after right performance measures in order to improve the quality of his course. Future Research: Future research can be improved by using a larger dataset. It could also be improved with deep learning models to reach more accurate results. Indeed, a strategy for checking CLOs overlaps could be integrated.

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Natural Language Processing (NLP): An Introduction

Cited by 23 publications

References 33 publications

Conceding Sentiment Prognosis on Twitter Data

Conceding Sentiment Prognosis on Twitter Data

A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

Categorizing Well-Written Course Learning Outcomes Using Machine Learning

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