scholarly journals IMPLEMENTASI PROBABILISTIC NEURAL NETWORK DAN WORD EMBEDDING UNTUK ANALISIS SENTIMEN VAKSIN SINOVAC

2021 ◽  
Vol 3 (2) ◽  
pp. 233-242
Author(s):  
Abdul Rahman Wahid Rapsanjani ◽  
Erfian Junianto
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Probabilistic Neural Network ◽  
Word Embedding

Penelitian ini bertujuan melakukan implementasi Probabilistic neural network dan Word Embedding dalam kasus sentiment analysis tentang tanggapan masyarakat tentang pemberian vaksin sinovac yangg diunggah di Twitter dan 3 class:positif, negative dan netral. Metode yang dipilih adalah metode klasifikasi Probabilistic Neural Network. Sebelum melakukan klasifikasi, praprocessing pada penelitian ini meliputi tokenizasi, normalisasi, menghilangkan emoticon, Convert Negasi, Stemming, Stopword Removal serta Word embedding. dataset yang digunakan berjumlah 1177 dataset dengan pembagiannya yaitu 560 dataset positif, 355 dataset negative dan 262 dataset netral. Program dirancang menggunakan Bahasa pemrograman python dengan beberapa library seperti keras, tensorflow dan pandas. Akurasi yang didapatkan pada pelatihan menggunakan Probabilistic  Neural Network sebesar 91%. Hasil pengujian adalah penelitian ini mampu melakukan sentiment analysis dengan kesalahan sebesar 9%.

scholarly journals CAMPUS SENTIMENT ANALYSIS E-COMPLAINT USING PROBABILISTIC NEURAL NETWORK ALGORITHM

Kursor ◽  
2017 ◽  
Vol 8 (3) ◽  
pp. 135
Author(s):  
Mohammad Zoqi Sarwani
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Probabilistic Neural Network ◽  
Training Data ◽  
Electronic Systems ◽  
Testing Dataset ◽  
Testing Data ◽  
Neural Network Algorithm ◽  
Last Stage ◽  
Text Preprocessing

E-complaint is one of the technologies which is used to collect feedback from customers in the form of criticism and suggestions using electronic systems. For some companies or agencies, ecomplaint is used to provide better services to its customers. This study is aimed to perform sentiment analysis of an e-complaint service, with the case of Brawijaya University. There are three main stages for the proposed system, i.e. Text Preprocessing, Text Weighting, and PNN forthe classification. Tokenization, filtering, and stemming are done in the text preprocessing. Resulted text from the preprocessing stage is weighting using Term Inverse Document Frequent (TFIDF). To classify the negative or positive complaints, PNN are used in the last stage. For the experiments, 70 data are used as the training data, and 20 data are used as the testing data. The experimental results based on the combination of the number of training and testing dataset, showed that the accuracy achieved up to 90%.

scholarly journals Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings

2021 ◽  
Vol 7 ◽  
pp. e422
Author(s):  
Sajjad Shumaly ◽  
Mohsen Yazdinejad ◽  
Yanhui Guo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Web Mining ◽  
Word Embedding ◽  
Processing Stage ◽  
Online Store ◽  
Persian Language ◽  
Text Preprocessing ◽  
Better Than

Sentiment analysis plays a key role in companies, especially stores, and increasing the accuracy in determining customers’ opinions about products assists to maintain their competitive conditions. We intend to analyze the users’ opinions on the website of the most immense online store in Iran; Digikala. However, the Persian language is unstructured which makes the pre-processing stage very difficult and it is the main problem of sentiment analysis in Persian. What exacerbates this problem is the lack of available libraries for Persian pre-processing, while most libraries focus on English. To tackle this, approximately 3 million reviews were gathered in Persian from the Digikala website using web-mining techniques, and the fastText method was used to create a word embedding. It was assumed that this would dramatically cut down on the need for text pre-processing through the skip-gram method considering the position of the words in the sentence and the words’ relations to each other. Another word embedding has been created using the TF-IDF in parallel with fastText to compare their performance. In addition, the results of the Convolutional Neural Network (CNN), BiLSTM, Logistic Regression, and Naïve Bayes models have been compared. As a significant result, we obtained 0.996 AUC and 0.956 F-score using fastText and CNN. In this article, not only has it been demonstrated to what extent it is possible to be independent of pre-processing but also the accuracy obtained is better than other researches done in Persian. Avoiding complex text preprocessing is also important for other languages since most text preprocessing algorithms have been developed for English and cannot be used for other languages. The created word embedding due to its high accuracy and independence of pre-processing has other applications in Persian besides sentiment analysis.

scholarly journals SENTIMEN ANALISIS COVID-19 DENGAN METODE PROBABILISTIC NEURAL NETWORK DAN TF-IDF

2021 ◽  
Vol 3 (1) ◽  
pp. 100-111
Author(s):  
Ina Najiyah ◽  
Ifani Hariyanti
Keyword(s):  
Neural Network ◽  
User Interface ◽  
Sentiment Analysis ◽  
Probabilistic Neural Network ◽  
Confusion Matrix ◽  
Corona Virus

Penelitian ini bertujuan melakukan sentiment analysis tentang corona virus pada kegiatan sehari hari yang diunggah di facebook, Twitter dan Instagram dengan output yaitu 3 class:positif, negative dan netral. Metode yang dipilih adalah metode klasifikasi Probabilistic Neural Network. Sebelum melakukan klasifikasi, praprocessing pada penelitian ini meliputi tokenizasi, normalisasi, menghilangkan emoticon, Convert Negasi, Stopword Removal sertaTF-IDF. dataset yang digunakan berjumlah 1177 dataset dengan pembagiannya yaitu 560 dataset positif, 355 dataset negative dan 262 dataset netral. Program dirancang menggunakan Bahasa pemrograman python dengan beberapa library seperti keras, tensorflow dan pandas. User interface dibuat berbasis android. Akurasi yang didapatkan pada pelatihan menggunakan Probabilistic Neural Network sebesar 89%. Hasil pengujian adalah penelitian ini mampu melakukan sentiment analysis dengan kesalahan sebesar 11% dilihat dari confusion matrix.

scholarly journals Analisis Sentimen pada Data Saran Mahasiswa Terhadap Kinerja Departemen di Perguruan Tinggi Menggunakan Convolutional Neural Network

2021 ◽  
Vol 8 (5) ◽  
pp. 1067
Author(s):  
Yuliska Yuliska ◽  
Dini Hidayatul Qudsi ◽  
Juanda Hakim Lubis ◽  
Khairul Umum Syaliman ◽  
Nina Fadilah Najwa
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Word Embedding ◽  
Great Performance ◽  
Word Representation ◽  
The Relationship

<p class="Abstrak"><em>Review</em> atau saran dari <em>customer</em> dapat menjadi sangat penting bagi penyedia layanan, begitu pula saran dari mahasiswa mengenai layanan sebuah unit kerja di perguruan tinggi. <em>Review</em> menjadi penting karena dapat menjadi indikator kinerja penyedia layanan. Pengolahan review juga sangat penting karena dapat menjadi referensi untuk pengambilan keputusan dan peningkatan layanan yang lebih baik ke depannya. Penelitian ini menerapkan analisis sentimen pada data saran atau <em>review</em> mahasiswa terhadap kinerja unit kerja atau departemen di perguruan tinggi, yaitu Politeknik Caltex Riau. Analisis sentimen dilakukan dengan menggunakan <em>Convolutional Neural Network (CNN)</em> dan <em>word embedding</em> <em>Word2vec</em> sebagai representasi kata. <em>CNN</em> merupakan metode yang memiliki performa yang baik dalam mengklasifikasi teks, yaitu dengan teknik <em>convolutional</em> yang menggabungkan beberapa <em>window</em> kata pada kalimat dan mengambil <em>window</em> yang paling <em>representative</em>. <em>Word2Vec</em> digunakan sebagai representasi data saran dan inputan awal pada <em>CNN</em>, dimana <em>Word2Vec</em> merupakan <em>dense vectors</em> yang dapat merepresentasikan hubungan antar kata pada data saran dengan baik. Saran mahasiswa dapat mengandung kalimat yang sangat panjang, karena itu perpaduan <em>Word2Vec</em> sebagai representasi kata dan <em>CNN</em> dengan teknik <em>convolutional</em>, dapat menghasilkan representasi yang <em>representative</em> dari kalimat panjang tersebut. Penelitian ini menggunakan dua arsitektur <em>CNN</em>, yaitu <em>Simple</em> <em>CNN</em> dan <em>DoubleMax CNN</em> untuk mengidentifikasi pengaruh kompleksitas arsitektur terhadap hasil klasifikasi sentimen.  Berdasarkan hasil pengujian, <em>DoubleMax CNN</em> dapat mengklasifikasi sentimen pada saran mahasiswa dengan sangat baik, yaitu mencapai Akurasi tertinggi sebesar 98%, <em>Recall</em> 97%, <em>Precision</em> 98% dan <em>F1-Score</em> 98%.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Student’s reviews about department performance can be essential for a college for it can be used to evaluate the department performance and to take an immediate action to improve its performance. This research applies sentiment analysis in the student’s reviews of college department in Politeknik Caltex Riau. Convolutional Neural Network and Word2Vec are employed to analyze the sentiment. CNN is known for its good performance in text classification by applying a convolutional technique to the input sentences. Word2Vec is used as word representation and as an input to the CNN. Word2Vec are dense vectors which can represent the relationship between words excellently. Student’s reviews can be a long sentence; hence the combination of Word2Vec as word representation and CNN with convolutional technique can produce a representative fiture from that long sentence. This research utilizes two CNN architectures, which are Simple CNN dan DoubleMax CNN to identify the effect of the complexity of CNN architecture to final result. Our experiments show that DoubleMax CNN has a great performance in classifying sentiment in the student’s reviews with the best Accuracy value of 98%, Recall 97%, Precision 98% and F1-Score value of 98%.<strong> </strong></em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Attention-Based CNN and Bi-LSTM Model Based on TF-IDF and GloVe Word Embedding for Sentiment Analysis

2021 ◽  
Vol 11 (23) ◽  
pp. 11255
Author(s):  
Marjan Kamyab ◽  
Guohua Liu ◽  
Michael Adjeisah
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Feature Weighting ◽  
Word Embedding ◽  
Max Pooling ◽  
Document Frequency ◽  
High Level ◽  
Computational Resources

Sentiment analysis (SA) detects people’s opinions from text engaging natural language processing (NLP) techniques. Recent research has shown that deep learning models, i.e., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer-based provide promising results for recognizing sentiment. Nonetheless, CNN has the advantage of extracting high-level features by using convolutional and max-pooling layers; it cannot efficiently learn a sequence of correlations. At the same time, Bidirectional RNN uses two RNN directions to improve extracting long-term dependencies. However, it cannot extract local features in parallel, and Transformer-based like Bidirectional Encoder Representations from Transformers (BERT) are the computational resources needed to fine-tune, facing an overfitting problem on small datasets. This paper proposes a novel attention-based model that utilizes CNNs with LSTM (named ACL-SA). First, it applies a preprocessor to enhance the data quality and employ term frequency-inverse document frequency (TF-IDF) feature weighting and pre-trained Glove word embedding approaches to extract meaningful information from textual data. In addition, it utilizes CNN’s max-pooling to extract contextual features and reduce feature dimensionality. Moreover, it uses an integrated bidirectional LSTM to capture long-term dependencies. Furthermore, it applies the attention mechanism at the CNN’s output layer to emphasize each word’s attention level. To avoid overfitting, the Guasiannoise and GuasianDroupout are adopted as regularization. The model’s robustness is evaluated on four English standard datasets, i.e., Sentiment140, US-airline, Sentiment140-MV, SA4A with various performance matrices, and compared efficiency with existing baseline models and approaches. The experiment results show that the proposed method significantly outperforms the state-of-the-art models.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

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