Pembentukan Dataset Token Sentimen Berdasarkan Akun Instagram Brand Elektronik Menggunakan K-Nearest Neighbors

Abstract. Generating Sentiment Token Dataset Based on Electronics Brand Instagram Account using K-Nearest Neighbors. Instagram is currently one of the most popular social media platforms for businesses and brand owners to promote their products. Because Instagram is a two-way communication platform, people can respond to any promotional content posted on Instagram. People's reactions come in a variety of form, and frequently include both positive and negative sentiment. This study aims to identify the words used in one type of sentiment, then use the K-NN approach to construct a token dataset by summarizing the phrases in many labels according to the sentiment type. The total accuracy value of the dataset for K = 1 is 33.38% (positive), 59.96% (negative), and 56.60% (neutral) based on the results of the tests performed.Keywords: sentiment analysis, K-Nearest Neighbors, dataset, InstagramAbstrak. Instagram saat ini menjadi salah satu media sosial yang banyak digunakan oleh perusahaan atau pemilik brand untuk melakukan promosi terhadap produk-produk yang dimilikinya. Karena bersifat dua arah, masyarakat dapat memberikan respon terhadap aktivitas promosi yang dilakukan oleh sebuah perusahaan melalui Instagram. Respon dari masyarakat memiliki varian yang beragam dan seringkali mengandung unsur sentimen baik positif maupun negatif. Penelitian ini mencoba untuk mengidentifikasi kata-kata yang digunakan dalam satu jenis sentimen, kemudian membuat dataset token dengan cara merangkum kata-kata tersebut dalam beberapa label sesuai jenis sentimen masing-masing menggunakan metode K-NN. Berdasarkan hasil pengujian yang dilakukan, didapatkan nilai akurasi dari dataset sebesar 33.38% (positif), 59.96% (negatif), dan 56.60% (netral) untuk K = 1.Kata Kunci: analisis sentimen, K-Nearest Neighbors, dataset, Instagra

On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID

The past years have witnessed great progress on remote sensing (RS) image interpretation and its wide applications. With RS images becoming more accessible than ever before, there is an increasing demand for the automatic interpretation of these images. In this context, the benchmark datasets serve as essential prerequisites for developing and testing intelligent interpretation algorithms. After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation. Specifically, we first analyze the current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations. We then present the general guidances on creating benchmark datasets in efficient manners. Following the presented guidances, we also provide an example on building RS image dataset, i.e., Million-AID, a new large-scale benchmark dataset containing a million instances for RS image scene classification. Several challenges and perspectives in RS image annotation are finally discussed to facilitate the research in benchmark dataset construction. We do hope this paper will provide the RS community an overall perspective on constructing large-scale and practical image datasets for further research, especially data-driven ones