Feature Expansion Word2Vec for Sentiment Analysis of Public Policy in Twitter

Alvi Rahmy Royyan Alvi Rahmy Royyan,Erwin Budi Setiawan Erwin Budi Setiawan

doi:10.29207/resti.v6i1.3525

Alvi Rahmy Royyan Alvi Rahmy Royyan, Erwin Budi Setiawan Erwin Budi Setiawan

Open Access

https://doi.org/10.29207/resti.v6i1.3525

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Abstract

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 develops and compares the Twitter sentiment analysis system using the feature expansion Word2vec method with the Logistic Regression (LR) and Support Vector Machine (SVM) classification algorithms and the system without the feature expansion Word2Vec method. The results of this study, the feature expansion Word2Vec method on the SVM classification algorithm succeeded in increasing the system accuracy up to 0,99% with an accuracy value of 78,99%.

Highlights

Public policy is a regulation or set of rules that are legally allocated by the government that affects everyone in a country
The third scenario was to measure the effect of feature expansion using Word2Vec with three different corpuses that have been created previously
This research has been studied for Twitter sentiment analysis using feature expansion Word2Vec

Summary

Introduction

Public policy is a regulation or set of rules that are legally allocated by the government that affects everyone in a country. The government, which has the authority to make public policy, is expected to be able to make a policy that solves problems and brings goodness to various parties, especially the community. As a result of this, various pros and cons responses were expressed by users of social media networks especially in one of the well-known microblogging sites, namely Twitter. In uploading a tweet on Twitter, users are only given a sentence up to 280 characters. This can increase the use of word variations and emoticons so that vocabulary mismatches are possible

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Conclusion