Author Profiling: Prediction of Gender and Language Variety from Document

Mamgain, Sunakshi; Balabantaray, Rakesh Chandra; Das, Ajit Kumar

doi:10.1109/icit48102.2019.00089

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…However, some papers considered common preprocessing techniques, similar to those used in PAN shared tasks [20]. At least five research groups represented in PAN's shared tasks from 2013 to 2017 removed retweet tags from the texts during preprocessing [13]- [17]; 17 groups removed hashtags [13]- [17], [20]; and 19 teams considered removing URLs. For the removal of the mentioned tags, 17research groups considered removing them from the processed text.…”

Section: Related Workmentioning

confidence: 99%

“…Lundeqvist & Svensson removed HTML tags, and used Twitter custom tokenizer (nltk.tokenize package -NLTK 3.6.2 documentation) [19]. However, some papers considered common preprocessing techniques, similar to those used in PAN shared tasks [20]. At least five research groups represented in PAN's shared tasks from 2013 to 2017 removed retweet tags from the texts during preprocessing [13]- [17]; 17 groups removed hashtags [13]- [17], [20]; and 19 teams considered removing URLs.…”

Section: Related Workmentioning

confidence: 99%

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How Different Text-Preprocessing Techniques using the Bert Model Affect the Gender Profiling of Authors

Alzahrani¹,

Jololian²

2021

Advances in Machine Learning

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Forensic author profiling plays an important role in indicating possible profiles for suspects. Among the many automated solutions recently proposed for author profiling, transfer learning outperforms many other state-of-the-art techniques in natural language processing. Nevertheless, the sophisticated technique has yet to be fully exploited for author profiling. At the same time, whereas current methods of author profiling, all largely based on features engineering, have spawned significant variation in each model used, transfer learning usually requires a preprocessed text to be fed into the model. We reviewed multiple references in the literature and determined the most common preprocessing techniques associated with authors' genders profiling. Considering the variations in potential preprocessing techniques, we conducted an experimental study that involved applying five such techniques to measure each technique’s effect while using the BERT model, chosen for being one of the most-used stock pretrained models. We used the Hugging face transformer library to implement the code for each preprocessing case. In our five experiments, we found that BERT achieves the best accuracy in predicting the gender of the author when no preprocessing technique is applied. Our best case achieved 86.67% accuracy in predicting the gender of authors.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

How Different Text-Preprocessing Techniques using the Bert Model Affect the Gender Profiling of Authors

Alzahrani¹,

Jololian²

2021

Advances in Machine Learning

View full text Add to dashboard Cite

show abstract

“…The paper [4] describes the approach to classifying the messages in social networks, the various combinations of n-gram symbols and n-gram words at the level of parts of speech were investigated. As a result, this approach demonstrated the accuracy of 70%.…”

Section: Related Workmentioning

confidence: 99%

“…The stylometry is constantly being improved, although the universal methods leading to a reliable determination of authorship have not yet been developed for most tasks. Nevertheless, in recent years, the methods of stylometry have become actively used in the protection of information, as described in [1][2][3], and criminology [4].…”

Section: Introductionmentioning

confidence: 99%

Determination of the Features of the Author’s Style of A.S. Pushkin’s Poems by Machine Learning Methods

2022

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This paper presents the study of the author’s style of A.S. Pushkin based on the comparison of his poetic texts with the texts of contemporary poets. The purpose of this study is to determine the features of the author’s style of A.S. Pushkin using machine learning methods. This paper describes the construction of several classifications based on different groups of features, as well as the classification based on a combined set of features from different groups. The quality of all constructed classifications is also analyzed; special attention is paid to the interpretation of the neural network solution and the identification of features of the author’s style.

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Sentiment Analysis in Social Media Marketing: Leveraging Natural Language Processing for Customer Insights

Singh,

Kumar,

Kumar

et al. 2024

Lecture Notes in Networks and Systems

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Author Profiling: Prediction of Gender and Language Variety from Document

Cited by 6 publications

References 5 publications

How Different Text-Preprocessing Techniques using the Bert Model Affect the Gender Profiling of Authors

How Different Text-Preprocessing Techniques using the Bert Model Affect the Gender Profiling of Authors

Determination of the Features of the Author’s Style of A.S. Pushkin’s Poems by Machine Learning Methods

Sentiment Analysis in Social Media Marketing: Leveraging Natural Language Processing for Customer Insights

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