Penelitian ini bertujuan untuk menganalisis : 1) pengaruh strategi diferensiasi terhadap keunggulan bersaing, 2) pengaruh strategi diferensiasi terhadap kinerja bisnis, 3) pengaruh orientasi pasar terhadap keunggulan bersaing, 4) pengaruh orientasi pasar terhadap kinerja bisnis, 5) pengaruh keunggulan bersaing terhadap kinerja bisnis, 6) mediasi keunggulan bersaing dalam hubungan antara strategi diferensiasi dengan kinerja bisnis, dan 7) mediasi keunggulan bersaing dalam hubungan antara orientasi pasar dengan kinerja bisnis. Data yang digunakan adalah data primer yang diperoleh dari kuisioner yang dibagikan kepada 115 manajer UMKM kecil sebagai responden. Data hasil penelitian kemudian dianalisis menggunakan Structural Equation Modelling (SEM). Hasil penelitian menunjukkan bahwa : 1) Strategi diferensiasi berpengaruh positif signifikan terhadap keunggulan bersaing, 2) Strategi diferensiasi berpengaruh positif signifikan terhadap kinerja bisnis, 3) Orientasi pasar berpengaruh positif signifikan terhadap keunggulan bersaing 4) Orientasi pasar berpengaruh positif signifikan tehadap kinerja bisnis, 5) Keunggulan bersaing berpengaruh positif signifikan tehadap kinerja bisnis 6) Keunggulan bersaing memediasi pengaruh strategi diferensiasi tehadap kinerja bisnis, 7) Keunggulan bersaing memediasi pengaruh orientasi pasar tehadap kinerja bisnis.

Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance.