Using BiLSTM Structure with Cascaded Attention Fusion Model for Sentiment Analysis

In the last decade, sentiment analysis has been a popular research area in the domains of natural language processing and data mining. Sentiment analysis has several commercial and social applications. The technique is essential to analyse the customer experience to develop customer loyalty and maintenance through better assistance. Deep Neural Network (DNN) models have recently been used to do sentiment analysis tasks with promising results. The disadvantage of such models is that they value all characteristics equally. We propose a Cascaded Attention Fusion Model-based BiLSTM to address these issues (CAFM-BiLSTM). Multiple heads with embedding and BiLSTM layers are concatenated in the proposed CAFM-BiLSTM. The information from both deep multi-layers is merged and provided as input to the BiLSTM layer later in this paper. The results of our fusion model are superior to those of the existing models. Our model outperforms the competition for lengthier sentence sequences and pays special attention to referral words. The accuracy of the proposed CAFM-BiLSTM is 5.1%, 5.25%, 6.1%, 12.2%, and 13.7% better than RNN-LSTM, SVM, NB, RF and DT respectively

Analisis Sentimen Berbasis Aspek dengan Deep Learning Ditinjau dari Sudut Pandang Filsafat Ilmu

Pesatnya pertumbuhan internet dan semakin populernya aplikasi media sosial memungkinkan orang untuk mengekspresikan opini dan pengalaman tentang sesuatu kepada public secara terbuka. Hal tersebut dapat dimanfaatkan dan dianalisis untuk mengeksplorasi customer behaviour (perilaku pengguna), memprediksi kebutuhan pengguna dan memahami opininya. Analisis sentimen berbasis aspek (aspect-based sentiment analysis) membuat analisis dan investigasi untuk mengidentifikasi polaritas sentimen pada aspek spesifik secara tepat. Deep learning untuk analisis sentimen berbasis aspek saat ini telah menunjukkan kinerja yang cukup menjanjikan karena efisiensinya dalam ekstraksi fitur otomatis dan kemampuannya untuk menangkap fitur sintaksis dan semantik teks tanpa perlu rekayasa fitur tingkat tinggi. Menurut Thomas Kuhn, ilmu pengetahuan tidak bersifat kumulatif, tetapi revolusioner dan berkembang secara historis. Ilmu pengetahuan tidak terlepas dari paradigma. Tulisan ini bertujuan untuk memberikan ulasan tentang penggunaan deep learning untuk analisis sentimen berbasis aspek dan tinjauannya menurut pandangan filsafat ilmu.The rapid growth of the internet and social media application make possiblity for people to express their opinion and experinces about something publicly. It can be utilized and analysed to explore the user behaviour, predict their demand and understand their opinion. Aspect-based sentiment analysis makes an analysis and investigation identify sentiment polarity on specific aspects precisely. Currently, deep learning for aspect-based sentiment analysis has shown a promising performance due to their efficiency of automatic feature extraction and their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering. According to Thomas Kuhn, the development of science is not cummulative but revolusionary and has a historical story. Science can not be separated from paradigm. The aim of this paper is for describing the used of deep learning for aspect-based sentiment analysis and its review from the philosophy of science