Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias

Film business and its individual reviews cannot be separated and film review sites such as IMDb is a credible source of reviews posted in public forums. With IMDb site reviews being unstructured and bias-heavy, classification methods by reducing additional sentiment bias is needed to create a balanc...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Fery Ardiansyah Effendi, Yuliant Sibaroni
Formato:	article
Lenguaje:	ID
Publicado:	Ikatan Ahli Indormatika Indonesia 2021
Materias:	sentiment classification, machine learning, ann, lexicon-based method, bat, so-cal. Systems engineering TA168 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/66590540a5d9479e997e0bddb74956b7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:66590540a5d9479e997e0bddb74956b7
record_format	dspace
spelling	oai:doaj.org-article:66590540a5d9479e997e0bddb74956b72021-11-16T13:16:12ZSentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias2580-076010.29207/resti.v5i5.3400https://doaj.org/article/66590540a5d9479e997e0bddb74956b72021-10-01T00:00:00Zhttp://jurnal.iaii.or.id/index.php/RESTI/article/view/3400https://doaj.org/toc/2580-0760Film business and its individual reviews cannot be separated and film review sites such as IMDb is a credible source of reviews posted in public forums. With IMDb site reviews being unstructured and bias-heavy, classification methods by reducing additional sentiment bias is needed to create a balanced classification with lower polarity bias. Elimination of additional sentiment bias will improve the model as polarity is defined by non-bias method, resulting in models correctly defined which sequences of words is either positive or negative. This research limits the dataset by 50.000 rows of randomly extracted reviews from the IMDb website using dataset preparation methods such as Preprocessing, POS-Tagging, and Word Embeddings. Then preprocessed data is used in classification methods such as ANN, SWN, and SO-Cal. This paper also used bias processing methods such as Hyperparameter Tuning and BPM, with outputs evaluated using Accuracy and PBR metrics. This research yields 77.39 % for ANN, 66.32% for BPM, 75.6% for SO-Cal, and 76.26% for Hybrid classification. Best PBR resulted in two lexicon-based methods on 0.0009 for BPM, and 0.00006 for SO-Cal. More advanced model configuration in ANN can improve the model, and much complex lexicon models will be a future in the research topic.Fery Ardiansyah EffendiYuliant SibaroniIkatan Ahli Indormatika Indonesiaarticlesentiment classification, machine learning, ann, lexicon-based method, bat, so-cal.Systems engineeringTA168Information technologyT58.5-58.64IDJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 5, Iss 5, Pp 863-875 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	sentiment classification, machine learning, ann, lexicon-based method, bat, so-cal. Systems engineering TA168 Information technology T58.5-58.64
spellingShingle	sentiment classification, machine learning, ann, lexicon-based method, bat, so-cal. Systems engineering TA168 Information technology T58.5-58.64 Fery Ardiansyah Effendi Yuliant Sibaroni Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias
description	Film business and its individual reviews cannot be separated and film review sites such as IMDb is a credible source of reviews posted in public forums. With IMDb site reviews being unstructured and bias-heavy, classification methods by reducing additional sentiment bias is needed to create a balanced classification with lower polarity bias. Elimination of additional sentiment bias will improve the model as polarity is defined by non-bias method, resulting in models correctly defined which sequences of words is either positive or negative. This research limits the dataset by 50.000 rows of randomly extracted reviews from the IMDb website using dataset preparation methods such as Preprocessing, POS-Tagging, and Word Embeddings. Then preprocessed data is used in classification methods such as ANN, SWN, and SO-Cal. This paper also used bias processing methods such as Hyperparameter Tuning and BPM, with outputs evaluated using Accuracy and PBR metrics. This research yields 77.39 % for ANN, 66.32% for BPM, 75.6% for SO-Cal, and 76.26% for Hybrid classification. Best PBR resulted in two lexicon-based methods on 0.0009 for BPM, and 0.00006 for SO-Cal. More advanced model configuration in ANN can improve the model, and much complex lexicon models will be a future in the research topic.
format	article
author	Fery Ardiansyah Effendi Yuliant Sibaroni
author_facet	Fery Ardiansyah Effendi Yuliant Sibaroni
author_sort	Fery Ardiansyah Effendi
title	Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias
title_short	Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias
title_full	Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias
title_fullStr	Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias
title_full_unstemmed	Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias
title_sort	sentiment classification for film reviews by reducing additional introduced sentiment bias
publisher	Ikatan Ahli Indormatika Indonesia
publishDate	2021
url	https://doaj.org/article/66590540a5d9479e997e0bddb74956b7
work_keys_str_mv	AT feryardiansyaheffendi sentimentclassificationforfilmreviewsbyreducingadditionalintroducedsentimentbias AT yuliantsibaroni sentimentclassificationforfilmreviewsbyreducingadditionalintroducedsentimentbias
_version_	1718426484874412032

Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias

Ejemplares similares