Aspect-Based Sentiment Analysis on Twitter Using Logistic Regression
with FastText Feature Expansion

Aspect-Based Sentiment Analysis on Twitter Using Logistic Regression
with FastText Feature Expansion

aspect-based sentiment analysis, logistic regression, fasttext, feature expansion, twitter

Social media has recently been widely used by users, especially Indonesians, as a place to express themselves in sentences,
pictures, sounds, or videos. Twitter is one of the social media favored by people of diverse ages. Twitter is a social media that
provides features like social media in general. However, Twitter has a unique feature where users can send or read text
messages limited to only a few characters. Therefore, user tweets with topics related to a particular product can be utilized by
companies to become input in the development of these products. This research was conducted using tweet data on the topic of
Telkomsel, which is divided into two aspects, namely signal and service. Aspect-based sentiment analysis of Telkomsel was
carried out using Logistic Regression with FastText feature expansion to reduce vocabulary mismatch in tweets so that the
classification stage can be performed optimally. In addition, the Synthetic Minority Oversampling Technique (SMOTE)
sampling method was applied to overcome data imbalance. The test results prove that feature expansion can improve F1-Score
values for signal and service aspects. For the signal aspect, F1-Score increased by 3.33% from the baseline with a value of
96.48%. While for the service aspect, F1-Score increased by 12.91% from the baseline with a value of 95.57%

Hanif Reangga Alhakiem1
, Erwin Budi Setiawan2

Telkom University

31-10-2022

Fajar bagus W

PDF

Indonesia

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