This paper presents a simple unsupe

This paper presents a simple unsupervised
learning algorithm for classifying reviews
as recommended (thumbs up) or not recommended
(thumbs down). The classification
of a review is predicted by the
average semantic orientation of the
phrases in the review that contain adjectives
or adverbs. A phrase has a positive
semantic orientation when it has good associations
(e.g., “subtle nuances”) and a
negative semantic orientation when it has
bad associations (e.g., “very cavalier”). In
this paper, the semantic orientation of a
phrase is calculated as the mutual information
between the given phrase and the
word “excellent” minus the mutual
information between the given phrase and
the word “poor”. A review is classified as
recommended if the average semantic orientation
of its phrases is positive. The algorithm
achieves an average accuracy of
74% when evaluated on 410 reviews from
Epinions, sampled from four different
domains (reviews of automobiles, banks,
movies, and travel destinations). The accuracy
ranges from 84% for automobile
reviews to 66% for movie reviews.

Results (Indonesian) 1:

Makalah ini menyajikan sederhana tanpa pengawasanbelajar algoritma untuk mengklasifikasikan ulasansebagai rekomendasi (acungan jempol) atau tidak dianjurkan(jempol ke bawah). KlasifikasiTinjauan diperkirakan olehrata-rata semantik orientasifrase dalam review yang berisi kata sifatatau kata. Frase yang telah positifsemantik orientasi ketika telah baik Asosiasi(misalnya, "nuansa halus") danorientasi semantik negatif ketika telahAsosiasi buruk (misalnya, "sangat cavalier"). Dalamkertas ini, orientasi semantikfrase dihitung sebagai saling informasiantara frase tertentu dankata "sangat bagus" minus Reksainformasi antara frase tertentu dankata "miskin". Review diklasifikasikan sebagaiApabila yang disarankan orientasi semantik rata-ratafrasa adalah positif. Algoritmamencapai akurasi rata-rata74% ketika dievaluasi pada 410 ulasan dariEpinions, sampel dari empat yang berbedadomain (Ulasan Mobil, Bank,film, dan tujuan perjalanan). Akurasiberkisar dari 84% untuk mobilUlasan tamu untuk 66% untuk ulasan film.