{"id":7248,"date":"2020-06-13T11:46:47","date_gmt":"2020-06-13T04:46:47","guid":{"rendered":"http:\/\/binus.ac.id\/malang\/?p=7248"},"modified":"2020-06-13T11:48:25","modified_gmt":"2020-06-13T04:48:25","slug":"pengenalan-sentiment-analysis","status":"publish","type":"post","link":"https:\/\/binus.ac.id\/malang\/2020\/06\/pengenalan-sentiment-analysis\/","title":{"rendered":"Pengenalan Sentiment Analysis"},"content":{"rendered":"

Pernahkah anda membaca \u2013 baca komentar di media sosial seperti twitter, Instagram ataupun facebook. Beberapa contoh komentar dari sebuah produk kosmetik seperti ini :<\/p>\n

 <\/p>\n

    \n
  1. \u201csetiap selesai pakai produk ini, wajah terasa lebih bersih dan segar\u201d<\/em><\/li>\n
  2. \u201cproduknya tidak efek di wajah saya\u201d <\/em><\/li>\n
  3. \u201csaya pakai produk ini semakin membuat banyak jerawat di wajah saya \u201c <\/em><\/li>\n<\/ol>\n

     <\/p>\n

    Jika dilihat dari komentar di atas dapat dikategorikan bahwa komentar nomor 1 adalah komentar positif yang menjelaskan bahwa produk tersebut memberikan efek yang baik untuk wajah pengguna. Komentar kedua dikategorikan seabgai netral dimana tidak terlihat pendapat positif ataupun negatif. Pada komentar ketiga terlihat jelas bahwa produk tersebut mendapatkan komentar negatif.<\/p>\n

     <\/p>\n

    Komentar \u2013 komentar yang disampaikan pada media sosial dapat dianalisa, namun apabila di analisa satu \u2013 satu maka akan membutuhkan waktu yang banyak. Oleh karenanya, dikembangkan berbagai macam metode ataupun teknik yang kemudian digunakan untuk mengkategorikan komentar tersebut, yang biasanya di kenal dengan istilah sentiment analysis. Sentiment analysis (T. Luo et al , 2013) dikelompokan secara umum menjadi dua yaitu<\/p>\n

      \n
    1. Fakta (objective<\/em>) : pernyataan objektif terkait suatu entitas ataupun attributnya. Sebagai contoh \u201cSaya membeli hanphone itu kemarin<\/em> \u201c<\/li>\n
    2. Pendapat (subjective<\/em>) : pernyataan mengenai sentimen, emosi, pendapat ataupun perasaaan terhadai suat entitas ataupun attributnya.<\/li>\n<\/ol>\n

       <\/p>\n

      Pendapat lain menyatakan bahwa sentiment analysis <\/em>(Madhat, W. et.al., 2014) dapat dibagi menjadi 3 level yaitu :<\/p>\n

        \n
      1. Document level <\/em>: mengkategorikan apakah sebuah dokumen termasuk sentiment postif ataupun negative<\/li>\n
      2. Sentence level :<\/em> menkategorikan sentimen di setiap kaliman<\/li>\n
      3. Aspect level <\/em>: mengkategorikan sentimen menurut aspek tertentu<\/li>\n<\/ol>\n

         <\/p>\n

         <\/p>\n

        Ada berbagai macam metode ataupun teknik untuk mengklasifikasikan sentiment tersebut. Secara umum dibagi menjadi dua yaitu Pendekatan Machine Learning <\/em>dan \u00a0<\/em>Pendekatan Lexicon Approach Approach. <\/em>\u00a0Dalam Pendekatan Machine Learning <\/em>, maka algoritma machine learning <\/em>\u00a0akan diterapkan dan fitur linguistic akan diterapkan. Sedangkan pada Pendekatan Lexicon Based<\/em>,\u00a0 akan sangat bergantung pada kumpulan \u2013 kumpulan dari pernyataan sentimen yang sudah ada. Pendekatan yang terakhir adalah mengabungkan kedua pendekatan di atas dan dikenal dengan istilah pendekatan Hybrid.<\/em><\/p>\n

         <\/p>\n

        (lanjut seri \u2013 2) Penjelasan masing masing pendekatan \u2026.<\/p>\n

         <\/p>\n

         <\/p>\n

         <\/p>\n

        Penulis : Cuk Tho S.Kom., MM., M.Com (IS)<\/p>\n

        Referensi<\/p>\n

         <\/p>\n

        Luo, T.,\u00a0 Chen, S., Xu, G., Zhou, J. (2013). Trust-Based Collective View Prediction. <\/em>Springer, New York.<\/p>\n

         <\/p>\n

        Medhat, W., Hassan, W., Korashy, H., (2014). Sentiment Analysis Algorithms and Application : A survey.<\/em> Aim Shams Engineering Journey<\/p>\n","protected":false},"excerpt":{"rendered":"

        Pernahkah anda membaca \u2013 baca komentar di media sosial seperti twitter, Instagram ataupun facebook. Beberapa contoh komentar dari sebuah produk kosmetik seperti ini :   \u201csetiap selesai pakai produk ini, wajah terasa lebih bersih dan segar\u201d \u201cproduknya tidak efek di wajah saya\u201d \u201csaya pakai produk ini semakin membuat banyak jerawat di wajah saya \u201c   […]<\/p>\n","protected":false},"author":19,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[128],"tags":[],"class_list":["post-7248","post","type-post","status-publish","format-standard","hentry","category-computer-science"],"yoast_head":"\nPengenalan Sentiment Analysis | BINUS UNIVERSITY MALANG | Pilihan Universitas Terbaik di Malang<\/title>\n<meta name=\"description\" content=\"BINUS University Malang Merupakan Universitas Terbaik di Malang dengan Program Computer Science, Entrepreneurship Bussiness Creation, Computer Science, Interior Design, Communication, Public Relations, Visual Communication Design.\" 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