Evaluating text features for lyrics-based songwriter prediction

Kirmaci, Basar; Ogul, Hasan

doi:10.1109/ines.2015.7329743

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…When the existing studies were examined, it was seen that different types of data sets were used for author identification tasks. Some studies have been carried out on newspaper articles [4,15,18,19], while others were carried out on poems [13], novels [11,12,16], email content [20], song lyrics [21], source codes [22], or tweets, blog posts, and forums [8,9,23]. In some cases, different types of data sources were combined or compared [17,25] Early studies in author identification focused on different stylometric techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Author Identification with Machine Learning Algorithms

YÜLÜCE¹,

Dalkılıç²

2022

IJMSIT

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Author identification is one of the application areas of text mining. It deals with the automatic prediction of the potential author of an electronic text among predefined author candidates by using author specific writing styles. In this study, we conducted an experiment for the identification of the author of a Turkish language text by using classical machine learning methods including Support Vector Machines (SVM), Gaussian Naive Bayes (GaussianNB), Multi Layer Perceptron (MLP), Logistic Regression (LR), Stochastic Gradient Descent (SGD) and ensemble learning methods including Extremely Randomized Trees (ExtraTrees), and eXtreme Gradient Boosting (XGBoost). The proposed method was applied on three different sizes of author groups including 10, 15 and 20 authors obtained from a new dataset of newspaper articles. Term frequency-inverse document frequency (TF-IDF) vectors were created by using 1-gram and 2-gram word tokens. Our results show that the most successful method is the SGD with a classification performance accuracy of 0.976% by using word unigrams and most successful method is the LR with a classification performance accuracy of 0.935% by using word bigrams.

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Section: Literature Reviewmentioning

confidence: 99%

Author Identification with Machine Learning Algorithms

YÜLÜCE¹,

Dalkılıç²

2022

IJMSIT

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An Ensemble Approach to Cross-Domain Authorship Attribution

Custódio

Paraboni

2019

Lecture Notes in Computer Science

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Lyrics Mining for Music Meta-Data Estimation

Ogul¹,

Kirmaci²

2016

IFIP Advances in Information and Communication Technology

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Music meta-data comprise a number of structured attributes that provide descriptive annotations such as singer, author, genre and date of a song deposited in a digital library. While they provide a crucial knowledge to represent music entry in current information retrieval and recommendation systems applications, they suffer from two limitations in practice. First, they may contain missing or wrong attributes due to incomplete submissions. Second, available attributes may not suffice to characterize the music entry for the objective of the retrieval or recommendation task being considered. Here, we offer an automated way of estimating the meta-data of a song using its lyrics content. We focus on attributing the author, genre and release date of songs solely based on the lyrics information. To this end, we introduce a complete text classification framework which takes raw lyrics data as input and report estimated meta-data attributes. The performance of the system is evaluated based on its retrieval ability on a large dataset of Turkish songs, which was gathered in this study and made publicly available. The results promote the use of such technique as a complementary tool in organizing music repositories and implementing music information retrieval systems.

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Evaluating text features for lyrics-based songwriter prediction

Cited by 4 publications

References 24 publications

Author Identification with Machine Learning Algorithms

Author Identification with Machine Learning Algorithms

An Ensemble Approach to Cross-Domain Authorship Attribution

Lyrics Mining for Music Meta-Data Estimation

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