https://doi.org/10.37815/rte.v36ne1.1206

Original articles

Links Related Generation to Transcribed Texts for YouTube Videos

Generación de Vínculos Relacionados a Textos Transcritos para Videos de YouTube

Pablo Martínez León1 https://orcid.org/0000-0002-9269-346X, Marcos Orellana1 https://orcid.org/0000-

0002-3671-9362

1Universidad de Cuenca,
Laboratorio
de Investigación y Desarrollo en Informática

(LIDI), Cuenca, Ecuador

[email-masked], [email-masked]

.

Esta obra está bajo una licencia internacional

Creative Commons Atribución-NoComercial 4.0

Sent: 2024/07/12 Accepted:
2024/10/02 Published: 2024/10/15

Abstract

The way people
learn has undergone a significant transformation thanks to the advancement of technology. Various digital tools complement daily and academic activities, facilitating access to updated and diverse information. The Internet, in particular, has positioned itself
as the primary source of information, offering a large amount of
textual and audiovisual content, with short content in the form of videos
being the most popular. However,
the learning process
inevitably involves acquiring new concepts and terms. When encountering unfamiliar vocabulary in videos,
people often search
for additional information to understand the content better.
Therefore, this research seeks
to develop a tool capable
of analyzing video
transcripts using Natural
Language Processing techniques to identify key terms and relate them to
other relevant information sources, thus facilitating learning. When evaluating the relevance of terms to the textual
content of videos on various topics using an Artificial Intelligence
model, a relevance greater than 75% was evidenced for all terms.
This confirms the efficacy of this approach
for analyzing and understanding the textual content of
transcribed videos.

Keywords: Natural language
processing, Text mining,
Text classification, Web Mashup.

Resumen

La manera en que las personas aprenden ha experimentado una transformación significativa gracias al avance de la tecnología. Actualmente, una amplia variedad de herramientas digitales complementa las actividades cotidianas y académicas, facilitando el acceso a información
actualizada y diversa.
Internet, en particular, se ha posicionado como la principal fuente de información, ofreciendo una gran cantidad de contenidos textuales y audiovisuales, siendo los contenidos breves en forma de videos, los
más populares. Sin embargo,
el proceso de aprendizaje inevitablemente implica
la adquisición de nuevos
conceptos y términos.
Al encontrarse con vocabulario
desconocido en los videos,
las personas suelen buscar información adicional para
comprender mejor el contenido. Por lo tanto, esta investigación busca desarrollar una herramienta capaz de analizar las transcripciones de videos utilizando técnicas de Procesamiento de Lenguaje Natural para identificar términos clave
y relacionarlos con otras fuentes de información relevante, facilitando así el aprendizaje. Al evaluar la relevancia de los términos con el contenido textual en videos de distintas
temáticas utilizando
un modelo de Inteligencia Artificial, se evidencia
una relevancia mayor al 75% en todos los términos, lo que confirma la eficacia de este enfoque para analizar y comprender el contenido textual de los videos transcritos.

Palabras clave: Categorización de texto, Minería de texto, Mashup web, Procesamiento de

lenguaje natural.

Introduction

Technology has radically transformed
the way we learn. Today, students and teachers have many digital resources to complement their
academic activities. The Internet has become a notable source of up-to-date and accessible information, with videos being
the preferred format for learning (Hsin & Cigas, 2013). However,
this activity is interrupted when people encounter unfamiliar terms
or concepts. This situation requires
searching for additional information from
other sources, which
generates intermittent viewing
of the video (Manasrah et al., 2021). Thus, developing a methodology and tool
that integrates and organizes these contents coherently is imperative.

Thanks to advances in Natural Language
Processing (NLP), analyzing and categorizing
texts and videos available online
is possible. This branch of Artificial Intelligence (AI) enables computer
systems to understand and process human language similarly to people (Litman, 2016).
NLP faces a considerable challenge due to the evolutionary nature
of human language. Factors such as people's speaking and writing styles make training NLP models continuous and complex.
These models improve
by being trained
with a large amount of data, such as Google
Translate, which uses neural networks to translate full texts using the
available context, achieving more accurate and fluid translations. This
technique, called Zero-shot, translates directly from the original language to
the target language (Vaswani et al., 2017).

Keywords serve as guides to finding relevant information in documents. They are helpful both for people searching for
information and for NLP systems, such as those that generate summaries (Pal et al., 2013), categorize texts (Özgür et al., 2005), and extract
information from texts (Marujo et al., 2011), among others. Additionally, automatic
frameworks are proposed to extract
keywords (Litvak, 2019;
Medelyan et al., 2010; Turney,
2000). These systems
were designed to work in ideal
conditions, with structured and controlled data, such as news or texts
from the Internet. However, their performance often decreases significantly
when applied to more complex tasks such as translation, name recognition, or
summary generation (Chang et al., 2014).

Text classification is an essential
task in the field of NLP, allowing us to sort and categorize a wide variety of
content, from questions to product reviews (Minaee et
al., 2022). Although various methods effectively classify texts into previously
labeled categories, their performance deteriorates significantly when faced with new classes not included in the previous
training (Pourpanah et al., 2019). Zero-shot classification aims to classify elements that have not been
seen during training (Wang et al., 2019). This
classification technique used in this study seeks to identify new topics and determine if they are related to the main topic of the text.
Zhang et al. (2022) generate features for unseen classes based on
lateral information, i.e., class-level attributes or text descriptions. Ye et al. (2020) also employ reinforced
self-training techniques to take advantage of unlabeled data during training.
By assuming that there is no information about unknown categories, they explore
different methods to classify texts without labels in a generalized way.

Based on the previous
studies that mentioned
techniques for extracting keywords such as YAKE! and Zero-shot classification, this
research proposes analyzing transcripts of videos obtained from the Internet (YouTube) through Application
Programming Interfaces (APIs). First, the keywords are extracted. Then,
new related topics are generated using Zero-shot classification. Finally, these topics are linked to Wikipedia articles and presented in a web mashup. The resulting
system allows a more complete video content exploration and related
terms.

Finally, this document is organized
as follows: Section II contains the related works; Section III is the methodology used for execution; Section IV shows the results achieved; Section
V addresses the evaluation; and finally, Section VI presents the
conclusions of the work.

Related Works

Several studies are focused
on applying different techniques and mechanisms to create
tools that help people improve their study methods (Burstein, 2009). However,
some studies have not used a combination of the proposed
techniques. Methodologically, some studies apply NLP
and text-mining techniques to find new topics from a base text (Devlin
et al., 2018). These
proposals are based on a similar process
since characteristics are extracted from the transcribed texts, or keywords from the entered
text are classified. At the same time, some studies use NLP
and AI processes to improve teaching and learning processes.

Keyword extraction is essential for
analyzing texts, allowing us to identify key points quickly. Shukla and Kakkar
(2016) propose a method to extract keywords from transcripts of MOOCs (Massive
Open Online Courses).
Using grammatical rules and term frequency-inverse
document frequency or Term Frequency and Inverse Document Frequency (TF-IDF)
allows this method to highlight the most important concepts and facilitate the
search for information in educational materials.

In the study of Zhang et al. (2022),
an automatic keyword extraction algorithm using supervised machine learning was developed. Through experiments, they
significantly improved the algorithm's performance by combining the
predictions of three different models. The preprocessing of the data, performed with NLP tools,
was crucial to the success
of this research.

NLP has revolutionized the way
students interact with information. By efficiently analyzing texts, NLP facilitates learning and the generation of new knowledge (Ferreira-Mello et al., 2019). In addition, it is a powerful tool that enhances learning by helping students understand,
improve language structures, and
effectively use search engines (Campos et al., 2020). Therefore,

a properly designed proposal would allow students to use
these tools effectively. Students' responses can be analyzed and compared with the search content by entering textual information
into the system and identifying their coincidences and discrepancies
(Burstein, 2009).

Vaswani et al. (2017) developed a
Zero-shot classification technique that links known classes to unknown classes
through auxiliary information. This information encodes the distinctive properties of the objects,
allowing the model
to identify types
that were not present
in the training set.

Unlike previous research that uses
NLP tools such as TF-IDF to identify
keywords in organized textual data, this study explores the possibility of extracting keywords from YouTube
videos and obtaining
their transcriptions through
specialized APIs. Additionally, new learning topics
linked to these keywords are generated with
this information, providing links to
related topics. A web mashup is developed, where these new links are displayed.
In contrast to the research above, which is limited to identifying keywords,
this study extracts keywords from video transcripts. It establishes meaningful
connections between these words and external information sources, such as
Wikipedia articles. In this way, a complete and contextualized vision of the
topics addressed in the video is provided.

Materials and Methods

The
exponential growth of data from various sources,
such as transactions, sensors, and
multimedia content, represents an unprecedented challenge. To deal with this massive
volume of information, it is necessary to develop systems
capable of automatically generating reports,
views, or summaries, thus facilitating data-based decision-making.

The
Software and Systems
Process Engineering Meta-Model 2.0 (SPEM 2.0) has been used to structure and represent the
methodology of this research. As seen in Figure 1, each phase is detailed here in a sequential order.
Each stage is characterized by its specific
inputs and outputs.
Likewise, the roles of those involved in each phase are identified. The methodology was divided
into five main tasks: i) Transcripts extraction; ii)
Text mining techniques application;

iii) Wikipedia article
extraction; iv) Topics
of interest classification; and v) Mashup generation.
Finally, the Technology Acceptance Model (TAM) was used to evaluate the
acceptance of the results obtained
using the new methodology. This model, widely used in information technologies, allows us to analyze the factors that influence users'
decisions to adopt and use a
new technology.

Figure 1

Phases of the Research Methodology represented with SPEM
2.0

Transcript Extraction

The elements necessary to extract
relevant information from the selected videos were identified. The duration of the videos,
according to the study by Manasrah et al. (2021),Click or tap
here to enter text. was established between
two to ten minutes. The source for extracting the videos is the YouTube
platform, one of the most popular and mature video
hosting platforms. In addition, this
platform allows integration with several programming languages through APIs
with different functionalities. Furthermore, the recovered videos
are limited to this language
because the AI model
has been trained
primarily with English
content. Thus, the identified components include the YouTube video link and the APIs that allow access to its content and
metadata.

Initially, the audio conversion of the videos to text was performed. For this, a specialized API, such as that of YouTube, was used to obtain an accurate
transcription of the audiovisual content. Subsequently, this transcript was
formatted to facilitate analysis and processing.

The
video platform API obtains all the information necessary for the subsequent stages of the process. This procedure was
implemented using the Python programming language in version 3.10.11
and requires the installation of the "youtube-transcript-API" library
in version 0.6.2; this library recommends a version of Python more significant than or equal
to version 3.8.

The transcription process is
illustrated with an example of how to get the transcript of the selected video
using the "youtube-transcript-api" module:

$pip install
youtube_transcript_api

from youtube_transcript_api import YouTubeTranscriptApi
YoutubeTranscriptApi.get_trasncript(video_id)

As an example, two YouTube videos
are selected to evaluate the process:

•
"What is Programming?" from the Khan Academy channel,
with 3.4 million
views,

available at: https://www.youtube.com/watch?v=FCMxA3m_Imc

•
"Factorizing Algebraic Expressions" from the Maths Explained channel, with 1.4 million views, available
at: https://www.youtube.com/watch?v=ctqviXu-mTE

Once
the video transcriptions were obtained, they were stored
in text variables
for later analysis. From
these transcripts, themes related to the content of each video are extracted.

The
video transcripts serve
as a starting point for this analysis. Through them and using
NLP techniques, it is possible
to identify the relevant terms
or words and obtain a more detailed understanding of the knowledge
of each video.

Text Mining Technique Application

In
this task, several
text mining techniques were applied to extract the most information from the transcribed videos.

Pre-processing

Before applying the computational model, it is necessary
to prepare the text data to maximize its effectiveness. This preprocessing process involves
several tasks:

•
Elimination of irrelevant words (stopwords):

•
In this technique, common words such as "the," "that," and "a," which do not

contribute significantly to the analysis, were removed. A search
engine configured to ignore these words was employed (Shukla & Kakkar,
2016).

•
Tokenization:

•
For this process, a list of tokens is created that excludes prevalent
and linguistically
uninformative words, such as conjunctions (and, or, nor), prepositions (a, in,
for), and common verbs (Campos et al., 2020).

•
Keyword extraction with YAKE!

YAKE! It is an automatic method to identify the most critical keywords in a text based on their statistical characteristics. It does not require prior training or depend on external dictionaries or
corpora. In addition, it includes a measure of relevance in the keywords, which
allows for the inclusion of a threshold for selecting them (Campos et al.,
2020).

Keyword extraction

In
this stage, the transcription obtained
from the selected
YouTube video from various
sources is used to analyze its content and apply data pre-processing
techniques. Next, the keyword extraction technique automatically identifies the
most relevant and essential words and phrases within the text. In this way, the
dimensionality of the data is reduced through
pre-processing techniques and keyword extraction, facilitating the
classification process and subsequent evaluation with the Artificial
Intelligence model.

For this, the YAKE! Library was used in version 0.4.8 in Python (3.10.11), together with the transcription of the video
selected as input.
Afterward, the library
was configured with the
desired parameters (language, n-gram size, among others.). Finally, the keywords were extracted
and stored in a text list, as presented in the following implementation:

$pip install git+https://github.com/LIAAD/yake import yake

custom_kw_extractor = yake.KeywordExtractor(lan=”en”, n=2, dedupLim=0.9, dedupFunc=’seqm’, windowsSize=1, top=10, features=None)

keywords = custom_kw_extractor.extract_keywords(transcription)

Through the application of the YAKE!
Tool, extracting between eight and ten of the most relevant keywords from each
video transcription was possible. This set of key terms, along with the most significant n-grams,
provided an overview
of the content of each video and served as a starting
point for deeper analyses. The frequency with which a word appeared
in the transcript was directly proportional to the value assigned by YAKE!, which allowed keywords to be ordered according to their
relative importance.

Continuing with the themes to be
evaluated for the topic of Computer Science and Mathematics, the extracted
keywords are presented in Table 1:

Table 1

Keywords extracted for each topic

COMPUTING

MATHEMATICS

“programming”

“common factor”

“program”

“highest common”

“programs”

“common divisor”

COMPUTING

MATHEMATICS

“computer”

“algebraic expressions”

“Code”

“factor analysis”

“process”

“lowest common factor”

“learn”

“expression (mathematics)”

“animations”

“factor solet”

“languages”

“closed-form expression”

Wikipedia Articles
Extraction

Once the critical topics in the
videos were identified, they were linked to relevant Wikipedia articles. To do
so, the API of this virtual encyclopedia was used, which allows searching
programmatically through its large search engine. Using the Wikipedia library,
in version 0.6.0 for Python 3.10.11, an
iterative cycle was established for each keyword, and queries were
performed with the API, obtaining a
maximum of four relevant results.

Topics of Interest Classification

Text classification within NLP is a
fundamental task where a model must predict the categories to which text documents belong.
However, traditional classification methods require
large amounts of labeled data to train the model and cannot be generalized to new data. Unlike
traditional methods, the Zero-shot technique
seeks to classify
text documents without
needing previously labeled data. This is achieved using large AI models
based on the Transformers architecture, which are trained on general language
understanding tasks.

This way, the pre-trained model
"bart-large-mnli"
from Meta was used, and a Natural Language Inference (NLI) task was trained on
it. NLI involves determining whether two sentences are related, contradictory,
or unrelated. Although this pre-trained model presents high performance in NLI tasks
in English, it has yet to be trained on a Spanish
language corpus, which limits its applicability to this language. The video transcription was proposed as a premise
for classifying a topic, and a hypothesis was built for each candidate
label, such as "This text is about mathematics" if the label is
"Mathematics." Then, the pre-trained model was used to evaluate the
relationship between the premise and the hypothesis, obtaining probabilities of
implication and contradiction. These probabilities are converted into
classification scores for each candidate label.

The
prediction model returns
indicate the probability that the video transcript is related
to a candidate tag. A high score suggests a strong
relationship, while a low score indicates the relationship is unlikely. The Zero-shot technique evaluated the relationship between the themes extracted from the video
transcription and the suggested Wikipedia articles. Each pair's score was calculated, indicating the relevance and correspondence between
the video content
and the articles.
The links to the articles related to the video were used as a starting point to explore other sources of information on the web, further expanding the understanding of the topics
of interest.

Mashup Generation

Through the stages described above, a
complete process has been created to extract topics associated with a YouTube video selected by the user.
The video transcription was used as a starting point,
and using text mining and NLP techniques, a processing flow implemented
in a Mashup was created. The main objective of this Mashup is to present the
user with new topics of interest
related to the selected video,
providing links to articles on the web where more information can be found.

This
study used Django,
a high-level framework for the rapid
development of websites with Python, to implement this
process. This way, a graphical interface was developed that allows the user to
enter the URL of the desired YouTube video and obtain related articles.

The Mashup processes the transcription by entering the video URL and presenting the related topics and relevant subtopics. Each topic and subtopic includes a direct link to a Wikipedia
article to complement
the information. For example, in the video with a computer theme, the main topics found were “Programming” and subtopics were “Computer programming,” “Program,”
“Programming language,” and
“Code.” In contrast, with the mathematics-themed video, the main topic was “Common Factor,” and the subtopics were “Greatest
common divisor,” “Common factors theory,” “Lowest common factor,” and “Factor analysis.” When a subtopic is selected, it is automatically redirected to the corresponding Wikipedia article, completing the process and providing detailed information on the topics of
interest.

Evaluation

The
TAM model is based on three primary
constructs: perceived usefulness, perceived ease of use, and attitude toward use. The objective is to evaluate the
acceptance of the results of the proposed
methodology, using the TAM and measuring perceptions of usefulness, ease of
use, and attitude towards the use of the results
through a seven-question questionnaire based on the five-point Likert scale. Additionally, the Goal Question Metric
(GQM) was used to define the objectives of the case study.

In this way, a case study was
performed in which the methodology was applied to a YouTube video about
mathematics, and the results were evaluated manually and through a survey of
experts in different areas. The GQM
template defines the case study's objectives, as seen in Table 2.

Table 2

Definition of objectives of the case study

QUESTIONS

ANSWERS

EVALUATE

What is being
studied?

The result of applying the methodology to identify related topics from a
mathematics video on YouTube
through implementing and experimenting with text mining
and NLP techniques and methods.

FOR THE PURPOSE
OF

What is the
intention of the study?

Evaluate the validity of the results obtained from the method.

WITH
RESPECT TO

What effect
is studied?

Related topics obtained from a video in the mathematics area.

FROM THE POINT OF VIEW OF

Who is affected? Refers
to the study
group

Systems and
Data Science Engineer.

IN THE CONTEXT OF

Where, how, and by whom is the study performed?

Researchers from the Computer Science Research and Development Laboratory (LIDI) of the University of Azuay.

Subsequently, the variables of perception of usefulness, perception of ease of use,
and attitude towards use are defined. Hypotheses were established, and expert
surveys were conducted to evaluate these variables. The survey included
questions about the effectiveness of the methodology results, the precision of
the results, the ease of implementation, and the simplicity of the user interface,
where a five-point Likert scale was used, the same as in indicator
one, it refers to "Strongly disagree" and five "Totally
agree".

Hypothesis

The hypotheses proposed for the evaluation are presented below:

•
H1: The perception of the
usefulness of the results is adequate for productivity or efficiency when
finding related topics.

•
H2: The results
are easy to understand and use.

•
H3: The results
have significant value for the
user.

The questionnaire for measuring
variables with TAM included questions about the effectiveness, precision, ease
of implementation, and simplicity of the user interface of the results.

Evaluation Execution

For the evaluation to be performed
successfully, the experts were introduced to the methodology used and how the
Mashup generated the results. Afterward, a demonstration and interaction were performed with each of the experts, who,
based on the results obtained, performed the provided survey.

Analysis and Interpretation of Collected Data

The survey results included the
variables’ minimum values,
maximums, and averages and the scale's
reliability through Cronbach's Alpha coefficient, which varies between
zero and one. The higher the
value, the higher the internal reliability of the items.

Results and Discussion

The links to related Wikipedia
articles were analyzed for their relevance to the video content. For this, the
Zero-shot classification technique was used. This process was divided into three
stages: i) data preparation, the video transcription is sent as a premise
and the related topics as candidate labels to the
classifier; ii) relationship analysis, the classifier evaluates the
relationship between the premise and each candidate label, assigning a
probability score; and

iii) interpretation of the results,
the scores are interpreted as percentages of relevance. A value
of 100% indicates a high relationship between the topic and the video.

Figure 2

Search Results with Computing Topic

As we can observe in Figure 2, four different themes
were analyzed: “Computer
Programming,” “Program,” “Programming Language,” and “Code,” classified in order of most significant relevance for
the analyzed video on “What is Programming.” The topic “Computer
Programming” obtained the
highest relevance with 99.41%, followed by “Program” with
97.47%, the topic “Programming Language” with 92.62%,
and “Code” had the slightest relevance with 76.56%, which
suggests that its connection with the video is less significant. These results
determined the most relevant and associated articles
for the video. Subsequently,
these results were transferred to the Mashup as a direct link to each Wikipedia
article.

Figure 3

Search Results with Mathematics Topic

Figure 3 presents the analysis of
four different topics: “Common Factors Theory,” “Greatest
Common Divisor,” “Lowest Common Factor,” and “Factor Analysis,” classified in order of
most significant relationship to the “Factorizing Algebraic
Expressions.” The most relevant topic was “Common Factor Theory” with 97.96%, followed by “Greatest Common Divisor” with 97.23%, and the topic “Lowest Common Factor” with 94.50%. Moreover,
“Factor Analysis” presented a lower relevance
of 91.07%. With these ratings,
the topics most related to the video analyzed were determined to
be viewed as part of the Mashup.

TAM Evaluation Results

Through the results obtained
in the survey, Cronbach's alpha
coefficient was used to evaluate the internal reliability
of the questionnaire and its results. The consistency and correlation between
the items that measure each variable are indicated to evaluate its validity.
Below are the scales used to measure the level of reliability:

•
0.90 or higher: Excellent reliability. The items are highly correlated, and the variable is
consistently measured.

•
0.80 to 0.89: Good reliability. The items are moderately correlated, and the variable is measured consistently.

•
0.70 to 0.79: Acceptable
reliability. The items are moderately correlated, and the variable is measured
acceptably.

•
0.60 to 0.69: Reliable
in certain circumstances. The items are weakly correlated, and the variable is measured moderately.

•
Less than 0.60: Low reliability.
The items are weakly correlated, and the variable needs to be measured
consistently.

The results indicated that
participants perceived the results as practical, easy to use, and valuable.
Cronbach's Alpha coefficient was 0.85, indicating solid variables and responses’
internal consistency.

Validation of Hypotheses

In this section, the hypotheses
raised at the beginning of the study are examined using the results obtained
from the survey.

•
Hypothesis 1: Are the results
helpful in finding
related topics?

The analysis of the average value for the variable
"perception of the usefulness of the results" was 4.16, indicating
that the participants consider the results obtained helpful in terms of
productivity and efficiency, validating hypothesis 1.

•
Hypothesis 2: Are the results
easy to understand and use?

The average value for the variable "perceived ease
of use of the results" was 4.5, indicating that participants consider the results easy to understand and use, supporting
hypothesis 2.

•
Hypothesis 3: Do the results
generate trust in the user?

The average value for the variable "attitude toward
use" was 4.13, indicating that participants perceived significant value in
the results and trust in their operation, validating hypothesis 3.

The average results of the survey supported the three hypotheses stated. The participants perceived that the proposed
methodology was practical, easy to use, and generated
trust, which suggests positive
acceptance by users. These results
are a positive indicator that the proposed methodology has the potential to be
a valuable tool for effectively identifying related topics in YouTube videos. However, several video
themes could hinder the developed system, and the keyword extraction technique
may not be the most effective in some cases. Therefore, it is necessary to investigate new techniques
and more advanced algorithms. Finally, the results of this research highlight
the marked disparity between the quantity and quality of pre-trained language models
available in Spanish
and English. This gap shows the urgent need to develop
more models in Spanish to democratize access to artificial intelligence
technologies in the Spanish-speaking world.

Conclusions

This study implemented a process of
identifying and extracting related topics from YouTube
videos. Text mining and NLP techniques, such as YAKE! and Zero-shot classification,
were combined to perform the task. Two specific subject areas were
selected to evaluate the effectiveness of this methodology: computer science
and mathematics. Experiments were performed in both fields to analyze the
results obtained and compare the performance of the different techniques.

The results were obtained through the
application of the YAKE! The tool accurately revealed the relevant themes
in the analyzed videos' transcripts. This tool proved
to be effective in extracting relevant keywords and concepts from
poorly structured text. On the other hand, NLP was essential in preparing the data for all processes, reducing dimensionality, and improving
its quality. The Zero-shot classification technique allowed us to objectively evaluate whether

the extracted themes aligned with the video's general
content. By assigning a score to each topic,
it was possible to establish a relevance ranking
and discard those
unrelated to the original
video. Visualizing results through a web mashup, which connected the identified
topics with Wikipedia articles, provided a powerful tool for exploring and understanding the data, enriching
its analysis. In conclusion, combining YAKE! NLP techniques, Zero-shot
classification, and visualization through a web mashup
allowed us to achieve the objectives proposed
in this study. It was possible to accurately
identify the main themes of the videos, evaluate their relevance, and provide
an interactive tool for exploring related information.

Acknowledgments

This
work was partially supported by the Vice rectorate of Research at Universidad del Azuay for their financial and
academic support and the entire staff in the Computer Science Research &
Development Laboratory (LIDI).

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