Salam, rekan Nawala! Semoga kalian selalu dalam keadaan sehat.
Ini adalah Nawala IAES dari Institute of Advanced Engineering and Science. Hari ini kami akan berbagi kabar tentang analisis sentimen. Analisis sentimen adalah teknik natural language processing (NLP) yang digunakan untuk mengidentifikasi sentimen atau perasaan yang tercermin dalam teks. Tujuannya adalah untuk mengelompokkan perasaan yang tercermin dari teks tersebut sebagai sentiment positif, negatif, atau netral. Analisis sentimen digunakan dalam banyak aplikasi, termasuk pemantauan media sosial, ulasan produk, dan riset pasar. Dalam analisis sentimen, komputer digunakan untuk mengklasifikasikan teks berdasarkan kata-kata kunci dan konteks yang digunakan. Samah dkk. (2023) melakukan penelitian di sebuah rumah sakit di Malaysia. Mereka mengkalsifikasi review mengenai rumah sakit tersebut yang datanya diambil dari Twitter, kemudian membuat visualisasi dari hasil analisis sentiment yang dilakukan.
Classification and visualization: Twitter sentiment analysis of Malaysia’s private hospitals
Khyrina Airin Fariza Abu Samah, Nur Maisarah Nor Azharludin, Lala Septem Riza, Mohd Nor Hajar Hasrol Jono, Nor Aiza Moketar
Malaysia has many private’s hospitals. Thus, feedback is important to improve service quality, becoming reviews for other patients. Reviews use the channel service provided on social media, such as Twitter. Nevertheless, online reviews are unstructured and enormous in volume, which leads to difficulties in comparing private hospitals. In addition, no single websites compare private hospitals based on users’ interests, bilingual reviews, and less time-consuming. Due to that, this study aims to classify and visualize the Twitter sentiment analysis of private hospitals in Malaysia. The scope focuses on five factors: 1) administrative procedure, 2) cost, 3) communication, 4) expertise, and 5) service. Term frequency-inverse document frequency is used for text mining, information retrieval techniques, and the Naïve Bayes, a machine learning algorithm for the classification. The user can visualize the specified state’s private hospitals and compare them with any selected state. The system’s functionality and usability have been tested to ensure it meets the objectives. Functionality testing proved that the private hospital’s Twitter sentiment could be predicted based on the training and testing data as intended, with 77.13% and 77.96% accuracy for English and Bahasa Melayu, respectively, while the system usability scale based on the usability testing resulted in an average final score of 95.42%.
Dalam era media sosial dan internet saat ini, analisis sentimen menjadi semakin penting karena memberikan kemampuan bagi bisnis untuk memantau sentimen publik tentang produk dan merek mereka secara real-time. Analisis sentimen dan machine learning saling terkait, karena machine learning dapat digunakan dengan efektif untuk melakukan analisis sentimen. Dengan menggunakan teknik machine learning, data teks atau ucapan dapat diklasifikasikan ke dalam kategori sentimen yang berbeda, seperti positif, negatif, atau netral. Analisis sentimen berbasis machine learning dapat diterapkan pada berbagai domain seperti media sosial, ulasan pelanggan, dan artikel berita, yang membantu organisasi dalam membuat keputusan berdasarkan data dan mendapatkan wawasan tentang opini dan tren pelanggan. Shah dkk. (2022) menggunakan machine learning untuk mengalisis review film dalam bahasa Gujarati. Hasil selengkapnya terkait penelitian tersebut dapat dilihat pada artikel dibawah ini.
Sentiment analysis on film review in Gujarati language using machine learning
Parita Shah, Priya Swaminarayan, Maitri Patel
Opinion analysis is by a long shot most basic zone of characteristic language handling. It manages the portrayal of information to choose the motivation behind the wellspring of the content. The reason might be of a type of gratefulness (positive) or study (negative). This paper offers a correlation between the outcomes accomplished by applying the calculation arrangement using various classifiers for instance K-nearest neighbor and multinomial naive Bayes. These techniques are utilized to assess a significant assessment with either a positive remark or negative remark. The gathered information considered on the grounds of the extremity film datasets and an association with the results accessible proof has been created for a careful assessment. This paper investigates the word level count vectorizer and term frequency inverse document frequency (TF-IDF) influence on film sentiment analysis. We concluded that multinomial Naive Bayes (MNB) classier generate more accurate result using TF-IDF vectorizer compared to CountVectorizer, K-nearest-neighbors (KNN) classifier has the same accuracy result in case of TF-IDF and CountVectorizer.
Guha dan Sutikno (2022) mengkombinasikan analisis sentiment dan deep learning. Analisis sentimen dan deep learning sangat erat kaitannya, karena teknik deep learning secara signifikan telah meningkatkan kinerja dan akurasi dari analisis sentimen. Deep learning telah merevolusi bidang NLP dan telah menjadi pendekatan mutakhir untuk banyak tugas NLP, termasuk analisis sentimen.
Natural language understanding challenges for sentiment analysis tasks and deep learning solutions
Radha Guha, Tole Sutikno
When it comes to purchasing a product or attending an event, most people want to know what others think about it first. To construct a recommendation system, a user’s likeness of a product can be measured numerically, such as a five-star rating or a binary like or dislike rating. If you don’t have a numerical rating system, the product review text can still be used to make recommendations. Natural language comprehension is a branch of computer science that aims to make machines capable of natural language understanding (NLU). Negative, neutral, or positive sentiment analysis (SA) or opinion mining (OM) is an algorithmic method for automatically determining the polarity of comments and reviews based on their content. Emotional intelligence relies on text categorization to work. In the age of big data, there are countless ways to use sentiment analysis, yet SA remains a challenge. As a result of its enormous importance, sentiment analysis is a hotly debated topic in the commercial world as well as academic circles. When it comes to sentiment analysis tasks and text categorization, classical machine learning and newer deep learning algorithms are at the cutting edge of current technology.
Analisis sentimen, machine learning, dan deep learning saling berhubungan dalam konteks tugas NLP, dan mereka mewakili pendekatan yang berbeda untuk mengatasi masalah memahami dan mengkategorikan sentimen dalam data teks. Singkatnya, analisis sentimen adalah tugas NLP yang berharga untuk memahami dan mengkategorikan sentimen dalam teks. Pendekatan machine learning dan deep learning memiliki kekuatan masing-masing dan dapat diterapkan tergantung pada skala data, sumber daya komputasi, dan tingkat kinerja serta kemampuan interpretasi yang diinginkan. Deep learning, dengan kemampuannya untuk menangkap pola yang kompleks, telah menjadi sangat efektif dalam beberapa tahun terakhir yang mendorong batas-batas akurasi analisis sentimen. Bitto dkk. (2023) mengkombinasikan machine learning dan deep learning untuk menganalisis sentimen review startup pengiriman makanan.
Abu Kowshir Bitto, Md. Hasan Imam Bijoy, Md. Shohel Arman, Imran Mahmud, Aka Das, Joy Majumder
Food delivery methods are at the top of the list in today’s world. People’s attitudes toward food delivery systems are usually influenced by food quality and delivery time. We did a sentiment analysis of consumer comments on the Facebook pages of Food Panda, HungryNaki, Pathao Food, and Shohoz Food, and data was acquired from these four sites’ remarks. In natural language processing (NLP) task, before the model was implemented, we went through a rigorous data pre-processing process that included stages like adding contractions, removing stop words, tokenizing, and more. Four supervised classification techniques are used: extreme gradient boosting (XGB), random forest classifier (RFC), decision tree classifier (DTC), and multi nominal Naive Bayes (MNB). Three deep learning (DL) models are used: convolutional neural network (CNN), long term short memory (LSTM), and recurrent neural network (RNN). The XGB model exceeds all four machine learning (ML) algorithms with an accuracy of 89.64%. LSTM has the highest accuracy rate of the three DL algorithms, with an accuracy of 91.07%. Among ML and DL models, LSTM DL takes the lead to predict the sentiment.
Beberapa artikel di atas merupakan bagian kecil dari penelitian mengenai analisis sentimen. Untuk mendapatkan informasi lebih lanjut, pembaca dapat mengunjungi laman dan membaca artikel secara GRATIS melalui tautan-tautan berikut: https://ijai.iaescore.com/, https://ijece.iaescore.com/, https://ijict.iaescore.com/, dan https://beei.org/.
Redaksi: I. Busthomi
