scholarly journals Analisis Sentimen Opini Publik Terhadap Undang-Undang Cipta Kerja pada Twitter Menggunakan Metode Naive Bayes Classifier

2021 ◽  
Vol 3 ◽  
pp. 201-212
Author(s):  
Yanuar Nurdiansyah ◽  
Fatchur Rahman ◽  
Priza Pandunata
Keyword(s):  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Analisis sentiment atau Opinion Mining merupakan cara memecahkan suatu permasalahan berdasarkan opini masyarakat yang beredar luas di media sosial yang diekspresikan dalam bentuk teks. analisis sentimen sangat membantu pemerintahan/ suatu instansi dalam mengetahui opini publik mengenai suatu kebijakan yang baru saja dikeluarkan tanpa menggunakan metode survey konvensional. Pada analisis sentimen yang dilakukan berfokus pada Trending topik tweet pada Twitter dengan trending topic pada tanggal 5 sampai 10 oktober yaitu #Omnibuslaw, #tolakruuciptakerja, #UUCiptaKerja,  dan #tolakomnibuslaw, dan trending topic pada tanggal 21 dan 22 november yaitu "obl makmurkan buruh". Proses Analisis sentimen dilakukan setelah data didapatkan pada tahapan crawling data, dilanjutkan dengan pembersihan kata pada proses preprocessing, dan pembobotan kata dengan algoritma TF-IDF. Analisis sentimen menggunakan metode naive bayes classifier bertujuan agar mendapatkan klasifikasi mengenai opini publik terhadap undang-undang cipta kerja pada twitter. Terdapat dua kelas pada penelitian ini yaitu kelas positif, dan negatif. Dari 2000 dataset yang terdiri dari 1400 tweet yang bersentimen negatif & 600 tweet yang bersifat positif dipakai akan dibagi antara data training dan data testing dengan perbandingan sebesar 60%:40%, 70%:30%, 80%:20%, dan 90%:10%. Dari hasil evaluasi pada Analisis sentimen mengenai opini publik terhadap undang-undang cipta kerja pada twitter didapatkan nilai akurasi tertinggi sebesar 94% dengan data training yang dipakai sebesar 90%, data testing sebesar 10%. Pada implementasinya, hasil dari uji sentimen menunjukkan hasil sentimen negatif yang lebih tinggi dibandingkan sentimen positif.

scholarly journals Opinion Mining Classification using Naive Bayes Algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 495-498
Keyword(s):  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Product Review ◽  
Online Services ◽  
Recent Advancement ◽  
Bayes Algorithm

With the recent advancement in the field of online services, the importance of a review for a product has also gone up. In this paper we focus on the aspect of reducing the time and effort for the user by recommending the best product to him. For this to be achieved, this paper proposes a Naive Bayes Classifier which labels the reviews accurately and combines the reviews to give a final rating to the product. The amazon product review data consisting of both negative and positive reviews was used for training and testing purposes. The model’s performance is evaluated, and results are analysed.

scholarly journals Aspect Βased Classification Model for Social Reviews

2017 ◽  
Vol 7 (6) ◽  
pp. 2296-2302 ◽  
Author(s):  
J. Mir ◽  
A. Mahmood ◽  
S. Khatoon
Keyword(s):  
Opinion Mining ◽  
Naive Bayes ◽  
Word Identification ◽  
Naïve Bayes ◽  
Classification Model ◽  
Product Reviews ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Proposed Model

Aspect based opinion mining investigates deeply, the emotions related to one’s aspects. Aspects and opinion word identification is the core task of aspect based opinion mining. In previous studies aspect based opinion mining have been applied on service or product domain. Moreover, product reviews are short and simple whereas, social reviews are long and complex. However, this study introduces an efficient model for social reviews which classifies aspects and opinion words related to social domain. The main contributions of this paper are auto tagging and data training phase, feature set definition and dictionary usage. Proposed model results are compared with CR model and Naïve Bayes classifier on same dataset having accuracy 98.17% and precision 96.01%, while recall and F1 are 96.00% and 96.01% respectively. The experimental results show that the proposed model performs better than the CR model and Naïve Bayes classifier.

scholarly journals Polarity Detection on Real-Time News Data Using Opinion Mining

2020 ◽  
Author(s):  
Boppuru Rudra Prathap ◽  
Sujatha A K ◽  
Chandragiri Bala Satish Yadav ◽  
Mummadi Mounika
Keyword(s):  
Machine Learning ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
News Article ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Knowledge Based ◽  
The Given

Sentimental Analysis or Opinion Mining plays a vital role in the experimentation field that determines the user’s opinions, emotions and sentiments concealing a text. News on the Internet is becoming vast, and it is drawing attention and has reached the point of adequately affecting political and social realities. The popular way of checking online content, i.e. manual knowledge-based on the facts, is practically impossible because of the enormous amount of data that has now generated online. The issue can address by using Machine Learning Algorithms and Artificial Intelligence. One of the Machine Learning techniques used in this is Naive Bayes classifier. In this paper, the polarity of the news article determined whether the given news article is a positive, negative or neutral Naive Bayes Classifier, which works well with NLP (Natural Language problems) used for many purposes. It is a family of probabilistic algorithms that used to identify a word from a given text. In this, we calculate the probability of each word in a given text. Using Bayes theorem, they are getting the probabilities based on the given conditions. Topic Modeling is analytical modelling for finding the abstract of topics from a cluster of documents. Latent Dirichlet Allocation (LDA) is a topic model is used to classify the text in a given document to a specified topic. The news article is classified as positive or negative or neutral using Naive Bayes classifier by calculating the probabilities of each word from a given news article. By using topic modelling (LDA), topics of articles are detected and record data separately. The calculation of the overall sentiment of a chosen topic from different newspapers from previously recorded data done.

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