Feature Expansion Word2Vec for Sentiment Analysis of Public Policy in
Twitter
Dublin Core
Title
Feature Expansion Word2Vec for Sentiment Analysis of Public Policy in
Twitter
Subject
sentiment analysis, feature expansion, word2vec, public policy
Description
Social media users, especially on Twitter, can freely express opinions or other information in the form of tweets
about anything, including responding to a public policy. In a written tweet, there is a limit of 280 characters per
tweet and this allows for problems such as vocabulary mismatches. Therefore, in this study, the feature expansion
Word2vec method was applied to overcome when the vocabulary mismatches occur. This study implements and
compares the Twitter sentiment analysis using the feature expansion Word2vec method and the baseline model.
To perform classification on this sentiment data, two different machine learning algorithms including Support
Vector Machine (SVM) and Logistic Regression (LR) are used to compare the model. The result is feature
expansion Word2Vec with SVM classifier has a slightly better performance which succeeded in increasing the
system accuracy up to 0,99% with 78,99% accuracy score, rather than LR classifier which achieved 78,31%
accuracy score.
about anything, including responding to a public policy. In a written tweet, there is a limit of 280 characters per
tweet and this allows for problems such as vocabulary mismatches. Therefore, in this study, the feature expansion
Word2vec method was applied to overcome when the vocabulary mismatches occur. This study implements and
compares the Twitter sentiment analysis using the feature expansion Word2vec method and the baseline model.
To perform classification on this sentiment data, two different machine learning algorithms including Support
Vector Machine (SVM) and Logistic Regression (LR) are used to compare the model. The result is feature
expansion Word2Vec with SVM classifier has a slightly better performance which succeeded in increasing the
system accuracy up to 0,99% with 78,99% accuracy score, rather than LR classifier which achieved 78,31%
accuracy score.
Creator
Alvi Rahmy Royyan1
, Erwin Budi Setiawan2
, Erwin Budi Setiawan2
Publisher
Telkom University
Date
27 Februari 2022
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Alvi Rahmy Royyan1
, Erwin Budi Setiawan2, “Feature Expansion Word2Vec for Sentiment Analysis of Public Policy in
Twitter,” Repository Horizon University Indonesia, accessed June 1, 2025, https://repository.horizon.ac.id/items/show/9078.
Twitter,” Repository Horizon University Indonesia, accessed June 1, 2025, https://repository.horizon.ac.id/items/show/9078.