Comparison of n-stage latent dirichlet allocation versus other topic modeling methods for emotion analysis

Yıl 2020, Cilt: 35 Sayı: 4, 2135 - 2146, 21.07.2020

Zekeriya Anıl Güven , Banu Diri , Tolgahan Çakaloğlu

https://doi.org/10.17341/gazimmfd.556104

Cited By: 4

Öz

Understanding
the emotions of sharing in social media plays a key role in learning people's
thoughts. Knowing the emotion of human being with developing technology
provides benefit in various fields. For example, media, marketing and
advertising areas allow people to reflect on their use and idea specific
content. In our study, Latent Dirichlet Allocation (LDA), Latent Semantic
Analysis (LSA) and Probabilistic-Latent Semantic Analysis (P-LSA) were used to
determine the emotions of individuals from Turkish tweets. In addition, the
success of the developed n-stage state of the LDA algorithm in the emotion
analysis was compared with the existing methods. The dataset consists of 4000
tweets of 5 different emotions, including angry, fear, happiness, sadness and
surprise. All topic modeling methods were modeled for 3 and 5 class datasets
and their successes and running times were measured. It has been observed that
the developed n-stage LDA method achieves success in terms of running time and
performance according to LDA and P-LSA. The most successful and fastest modeled
method was LSA.

Anahtar Kelimeler

Sentiment analysis, topic modelling, social network analysis, natural language processing

Duygu analizi için n-aşamalı Gizli Dirichlet Ayırımı ile diğer konu modelleme yöntemlerinin karşılaştırılması

Öz

Sosyal medyada
yer alan paylaşımlara ait duyguları anlamak, insanların düşüncelerini öğrenmede
kilit bir rol almaktadır. Gelişen teknoloji ile insanın duygusunu bilmek,
çeşitli alanlarda yarar sağlamaktadır. Örneğin medya, pazarlama ve reklam gibi
alanlar insanların kullanımlarına ve fikrine özgü içerikleri kullanıcıya
yansıtabilme imkanı tanımaktadır. Çalışmamızda konu modelleme algoritmalarından
Gizli Dirichlet Ayırımı (GDA), Gizli Anlamsal Analiz (GAA) ve Olasılıksal-Gizli
Anlamsal Analiz (O-GAA) Türkçe tivitlerden kişilerin duygularını belirlemede
kullanılmıştır. Ayrıca, GDA algoritmasının geliştirilen n-aşamalı halinin duygu
analizindeki başarısı mevcut yöntemlerle de karşılaştırılmıştır. Kullanılan
veri seti kızgın, korku, mutlu, üzgün ve şaşkın olmak üzere 5 farklı duyguya
ait 4000 tivitten oluşmaktadır. Tüm konu modelleme yöntemleri 3 ve 5 sınıflı
veri seti için modellenerek başarıları ve çalışma süreleri ölçülmüştür.
Geliştirilen n-aşamalı GDA yönteminin, GDA ve O-GAA’ya göre çalışma süresi ve
performansı açısından başarı sağladığı gözlemlenmiştir. En başarılı ve en hızlı
modellenen yöntem ise GAA olmuştur.

Anahtar Kelimeler

Duygu analizi, konu modelleme, doğal dil işleme, sosyal ağ analizi, sosyal medya

Kaynakça

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