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

Review atau saran dari customer dapat menjadi sangat penting bagi penyedia layanan, begitu pula saran dari mahasiswa mengenai layanan sebuah unit kerja di perguruan tinggi. Review 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 review mahasiswa terhadap kinerja unit kerja atau departemen di perguruan tinggi, yaitu Politeknik Caltex Riau. Analisis sentimen dilakukan dengan menggunakan Convolutional Neural Network (CNN) dan word embedding Word2vec sebagai representasi kata. CNN merupakan metode yang memiliki performa yang baik dalam mengklasifikasi teks, yaitu dengan teknik convolutional yang menggabungkan beberapa window kata pada kalimat dan mengambil window yang paling representative. Word2Vec digunakan sebagai representasi data saran dan inputan awal pada CNN, dimana Word2Vec merupakan dense vectors yang dapat merepresentasikan hubungan antar kata pada data saran dengan baik. Saran mahasiswa dapat mengandung kalimat yang sangat panjang, karena itu perpaduan Word2Vec sebagai representasi kata dan CNN dengan teknik convolutional, dapat menghasilkan representasi yang representative dari kalimat panjang tersebut. Penelitian ini menggunakan dua arsitektur CNN, yaitu Simple CNN dan DoubleMax CNN untuk mengidentifikasi pengaruh kompleksitas arsitektur terhadap hasil klasifikasi sentimen. Berdasarkan hasil pengujian, DoubleMax CNN dapat mengklasifikasi sentimen pada saran mahasiswa dengan sangat baik, yaitu mencapai Akurasi tertinggi sebesar 98%, Recall 97%, Precision 98% dan F1-Score 98%. AbstractStudent’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%.

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Convolutional neural network with contextualized word embedding for text classification

2019 International Conference on Image and Video Processing, and Artificial Intelligence ◽

10.1117/12.2544614 ◽

2019 ◽

Cited By ~ 1

Author(s):

Gaoyang Fan ◽

cui zhu ◽

wenjun zhu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Text Classification ◽

Word Embedding

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

PeerJ Computer Science ◽

10.7717/peerj-cs.422 ◽

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.

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Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification

Neurocomputing ◽

10.1016/j.neucom.2015.09.096 ◽

2016 ◽

Vol 174 ◽

pp. 806-814 ◽

Cited By ~ 134

Author(s):

Peng Wang ◽

Bo Xu ◽

Jiaming Xu ◽

Guanhua Tian ◽

Cheng-Lin Liu ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Text Classification ◽

Word Embedding ◽

Short Text ◽

Semantic Expansion

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Modeling the relationship between acoustic stimulus and EEG with a dilated convolutional neural network

2020 28th European Signal Processing Conference (EUSIPCO) ◽

10.23919/eusipco47968.2020.9287417 ◽

2021 ◽

Author(s):

Bernd Accou ◽

Mohammad Jalilpour Monesi ◽

Jair Montoya ◽

Hugo Van hamme ◽

Tom Francart

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Acoustic Stimulus ◽

The Relationship

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Convolutional Neural Network for Customer’s Opinion on Amazon Products

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5670.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 6634-6643 ◽

Cited By ~ 1

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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