Analisis Sentimen Akun Twitter Apex Legends Menggunakan VADER
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Bibliographic record
Abstract
Abstrak - Pesatnya peningkatan jasa internet saat ini, ada banyak informasi yang dihasilkan dalam jumlah besar secara terus menerus dalam waktu yang singkat. Akhir-akhir ini, analisis sentimen dengan menggunakan ulasan dan pesan telah menjadi topik penelitian yang populer dibicarakan di bidang Natural Language Processing. Selama bertahun-tahun, permainan online telah menjadi suatu aktivitas yang tidak bisa dipisahkan dari sebagian besar orang. Apex Legends adalah salah satu contoh game yang sangat popular di seluruh dunia. Untuk mendapatkan informasi bagaimana pendapat para pemain tentang permainan ini diperlukan analisis sentimen. Pada penelitian ini dilakukan analisis sentimen menggunakan bantuan aplikasi Orange Data Mining dengan metode VADER pada akun twitter Apex Legends menggunakan data sebanyak 500 tweet. Pengujian data dilakukan dengan membandingkan hasil yang didapat menggunakan metode VADER dengan hasil pengujian pakar, yaitu native speaker dari Canada dan Amerika. VADER mengklasifikasikan data yang didapatkan melalui twitter berdasarkan nilai compound yang didapat. Penelitian ini menghasilkan kesimpulan yaitu perbandingan dari pengujian menggunakan VADER dan pengujian pakar tidak berbeda jauh, yang mana total persentase dari penggunaan metode VADER untuk menganalisis sentiment dari twitter ini adalah : Positif = 18%, Negatif = 4,6%, Netral = 73,6%. Sedangkan hasil pengujian pakar adalah : Positif = 27%, Negatif = 10,8%, Netral = 62,2%.Kata kunci: VADER, Apex Legends, Game, Twitter, Uji Pakar Abstract - With the rapid increase in internet services today, there is a lot of information produced in large quantities continuously in a short time. Recently, sentiment analysis using reviews and messages has become a popular research topic discussed in the Natural Language Processing field. Over the years, online gaming has become an activity that cannot be separated from most of the people. Apex Legends is one example of a game that is very popular around the world. To get information on how the players think about the game, sentiment analysis is needed. In this study, sentiment analysis was carried out using the Orange Data Mining application with the VADER method on the Apex Legends twitter account using 500 tweets (data). Data testing is done by comparing the results obtained using the VADER method with the results of expert testing, native speaker from Canada and America. VADER classifies the data obtained through twitter based on the compound value obtained. This study concludes that the comparison of testing using VADER and expert testing is not much different, where the total percentage of using the VADER method to analyze sentiment from Twitter is : Positive = 18%, Negative = 4,6%, Neutral = 73,6%. While the results of expert testing is : Positive = 27%, Negative = 10,8%, Neutral = 62,2%.Keywords : VADER, Apex Legends, Game, Twitter, Expert Test (Uji Pakar)
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it