Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity

  • Wahid D
  • SN A
N/ACitations
Citations of this article
385Readers
Mendeley users who have this article in their library.

Abstract

The using of Twitter by selebrities has become a new trend of impression management strategy. Mining public reaction in social media is a good strategy to obtain feedbacks, but extracting it are not trivial matter. Reads hundred of tweets while determine their sentiment polarity are time consuming. Extractive sentiment summarization machine are needed to address this issue. Previous research generally do not include sentiment information contained in a tweet as weight factor, as a results only general topics of discussion are extracted. This research aimed to do an extractive sentiment summarization on both positive and negative sentiment mentioning Indonesian selebrity, Agnes Monica, by combining SentiStrength, Hybrid TF-IDF, and Cosine Similarity. SentiStrength is used to obtain sentiment strength score and classify tweet as a positive, negative or neutral. The summarization of posisitve and negative sentiment can be done by rank tweets using Hybrid TF-IDF summarization and sentiment strength score as additional weight then removing similar tweet by using Cosine Similarity.

Cite

CITATION STYLE

APA

Wahid, D. H., & SN, A. (2016). Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 10(2), 207. https://doi.org/10.22146/ijccs.16625

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free