On Natural Language Processing Pipeline: Applications of Sentiment Analysis for Educational Purposes Using Python’s NLTK, TextBlob, and Streamlit Libraries

Fouad Boulkroun

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Chapter 2 On Natural Language Processing Pipeline

Applications of Sentiment Analysis for Educational Purposes Using Python’s NLTK, TextBlob, and Streamlit Libraries

In: Artificial Intelligence

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Fouad Boulkroun

Fouad Boulkroun
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Type:: Chapter

Pages:: 38–84

DOI:: https://doi.org/10.1163/9789004754065_002

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Abstract

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that deals with understanding and deriving insights from large amounts of human language such as text and speech. It is the intersection of computer science, linguistics, and machine learning, with the one aim of making computers understand and generate human language in natural language. Sentiment analysis (SA) is a field of research subsumed under the umbrella of NLP. It is the process of using computer technologies and algorithms to systematically identify and classify affective information in a given text. Such information bears upon opinions, emotions, and/or attitudes towards a service, a product, a topic, or the like. SA has gained currency as a research area with a range of applications in a number of fields; sentiment information might prove useful for educational institutions, companies and consumers, government agencies, as well as political groups. The aim of the present chapter is to explain NLP tasks with applications of SA on university students’ attitudinal comments on the quality of their blended learning experiences as examples. It sets off by accounting, in the first place, for the different NLP tasks and the NLP pipeline, touching upon the preprocessing and cleaning techniques that are a pre-requisite for SA. It then delves into quantitatively identifying the sentiments of the inputted texts, using computational linguistic approaches; in particular, of the two major methods used for SA, the lexicon-based (or unsupervised machine-learning) method is made use of here. More specifically, the chapter attempts to determine the polarity (i.e., positivity/negativity) and subjectivity (or objectivity) thereof, identify the potential sentiments and their strengths therein, and identify the potential trends and insights emerging therefrom. A line chart and a word cloud are plotted to represent most frequently appearing words in the comments. All along, the analysis will have recourse to Python’s libraries, namely Natural Language Toolkit (NLTK), TextBlob, and Streamlit. Notwithstanding the eventual results, this chapter is also meant to serve as a tutorial for conducting NLP and SA in Python by sharing the code of the most important steps. Of note, the reader will also get introduced to the way the Python code is converted into a web-based application that can be shared with other users to automate the analysis process from beginning to end. The chapter ends with a discussion of its limitations, together with suggestions of how to overcome them. Its Implications will inform educational and research decisions by automatically understanding students’ responses at scale.

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Artificial Intelligence

An Algorithmic Renaissance in Education, Linguistics, and Arts

Series: Contemporary Approaches to Research in Learning Innovations, Volume: 15

E-Book ISBN:: 9789004754065

Publisher:: Brill

Print Publication Date:: 23 Dec 2025

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- Education

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Chapter 2 On Natural Language Processing Pipeline

Applications of Sentiment Analysis for Educational Purposes Using Python’s NLTK, TextBlob, and Streamlit Libraries

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Artificial Intelligence

An Algorithmic Renaissance in Education, Linguistics, and Arts

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