Sentiment Classification for Film Reviews by Reducing Additional
Introduced Sentiment Bias

Sentiment Classification for Film Reviews by Reducing Additional
Introduced Sentiment Bias

: Sentiment Classification, Machine Learning, ANN, Lexicon-based method, BAT, SO-Cal

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

Fery Ardiansyah Effendi1
, Yuliant Sibaroni2

Telkom University

25-10-2021

Fajar bagus W

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Indonesa

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