dubi̇ted

Duzce University Journal of Science and Technology

2148-2446

Duzce University

10.29130/dubited.1190860

Engineering

Mühendislik

YouTube'da Kanal Performansını Ölçmek İçin Neden Duygu Analizi Temelli Metrikler Gereklidir: Deneysel Bir Çalışma

Why Sentiment Analysis-Based Metrics are Essential for Measuring Channel Performance on YouTube: An Experimental Study

https://orcid.org/0000-0002-1084-7745

Elbaş

Hakan

BURSA TEKNİK ÜNİVERSİTESİ

https://orcid.org/0000-0003-1970-1731

Mesri

Alparslan

04 29 2024

12 2 1086 1100 10 18 2022 09 17 2023

2013

Duzce University Journal of Science and Technology

YouTube, kullanıcılarının video paylaşmasına ve paylaşılan videolara yorum yazmasına olanak tanıyan evrensel bir sosyal ortamdır. Kullanıcıların Youtube videolarına yaptığı yorumlar YouTube kanal sahipleri için faydalı olabilir. Bir YouTube videosundaki beğen/beğenme oranı, kullanıcıların videoya yönelik tutumunu tahmin etmek için yeterli değildir. Bu çalışma, bu tutum için üç aşamalı bir yöntem önermektedir: İlk adımda, "iJustin" YouTube kanalının videolarındaki kullanıcı yorumlarına bir duygu analizi görevi uygulanmaktadır. İkinci adımda, Duygu İndeksi (SI) adlı yeni bir metrik önerilmiş ve videoların Duygu İndeksleri hesaplanmıştır. Üçüncü adımda, SI metriğinin zamandan bağımsız olup olmadığını göstermek için bir analiz yapılmıştır. Sonuç olarak videolara yapılan yorumların çoğunun (%89) video yayınlandıktan sonraki ilk 30 gün içinde yazıldığı görüldü. Deneylerimiz, videoları yayınladıktan sonra 30 günden fazla kalan yorumların, videoların ortalama SI değerlerini yalnızca %0,4 oranında değiştirdiğini ve bu açıdan SI metriğinin zaman parametresinden ihmal edilebilir düzeyde etkilendiğini ortaya koymuştur.

YouTube is a universal social medium that lets its users share videos and write comments on shared videos. Comments of the users on Youtube videos can be useful for YouTube channel owners, and it is worth analyzing them in terms of sentiment. The rate of like/dislike on a YouTube video is not sufficient for estimating the attitude of users towards it. This study proposes a three-step method for this attitude: In the first step, a sentiment analysis task is applied to the user comments on the videos of the YouTube channel "iJustin". In the second step, a new metric named Sentiment Index (SI) has been proposed and the Sentiment Indexes of the videos have been calculated. In the third step, an analysis is performed to show if the SI metric is time-independent. As a result, it was seen that most of the comments left on the videos (89%) have been written within the first 30 days after the video was published. Our experiments revealed that comments left more than 30 days after publishing the videos change the average SI values of the videos by only 0.4%, and in this respect, the SI metric is negligibly affected by the time parameter.

Natural Language Processing Sentiment Analysis Data Science Youtube Opinion Mining Social Media

Doğal Dil İşleme Duygu Analizi Veri Bilimi YouTube Fikir Madenciliği Sosyal Medya

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