Sentiment Analysis of Product Reviews using Naive Bayes Algorithm: A Case Study

2018 ◽  
Author(s):  
Suci Laila Ramdhani ◽  
Rachmadita Andreswari ◽  
Muhammad Azani Hasibuan
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Product Reviews ◽  
Bayes Algorithm

scholarly journals Application of Naïve Bayes Algorithm in Sentiment Analysis of Filipino, English and Taglish Facebook Comments

2020 ◽  
Vol 4 (5) ◽  
pp. 73-77
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Product Reviews ◽  
Documentary Data ◽  
The Social ◽  
Bayes Algorithm

The World Wide Web has boosted its content for the past years, it has a vast amount of multimedia resources that continuously grow specifically in documentary data. One of the major contributors of documentary contents can be evidently found on the social media called Facebook. People or netizens on Facebook are actively sharing their opinion about a certain topic or posts that can be related to them or not. With the huge amount of accessible documentary data that are seen on the so-called social media, there are research trends that can be made by the researchers in the field of opinion mining. A netizen’s comment on a particular post can either be a negative or a positive one. This study will discuss the opinion or comment of a netizen whether it is positive or negative or how she/he feels about a specific topic posted on Facebook; this is can be measured by the use of Sentiment Analysis. The combination of the Natural Language Processing and the analytics in textual form is also known as Sentiment Analysis that is use to the extraction of data in a useful manner. This study will be based on the product reviews of Filipinos in Filipino, English and Taglish (mixed Filipino and English) languages. To categorize a comment effectively, the Naïve Bayes Algorithm was implemented to the developed web system.

scholarly journals Performance of Naïve Bayes in Sentiment Analysis of User Reviews Online

2020 ◽  
Vol 10 (2) ◽  
pp. 64-68
Author(s):  
Mir Habeebullah Shah Quadri ◽  
R. K. Selvakumar
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Product Reviews ◽  
User Reviews ◽  
Bayes Algorithm ◽  
The Right ◽  
Processing Techniques

Both sellers and buyers heavily depend on the opinions of customers in purchasing and selling products online. When it comes to text-based data, sentiment analysis of user reviews has become a prominent facet of machine learning. Text data is generally unstructured which makes opinion mining very challenging. A wide array of pre-processing and post-processing techniques need to be applied. But the major challenge is selecting the right classifier for the job. Naïve Bayes algorithm is a commonly used machine learning classifier when it comes to opinion mining and sentiment analysis. The focus of this survey is to observe and analyze the performance of Naïve Bayes algorithm in sentiment analysis of user reviews online. Recent research from a wide array of use-cases such as sentiment analysis of movie reviews, product reviews, book reviews, blog posts, microblogs and other sources of data have been taken into account. The results show that Naïve Bayes algorithm performs exceptionally well with accuracies between 75% to 99% across the board.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

Sentiment Analysis in Teachers Performance Rating Using Naïve Bayes Algorithm

2022 ◽  
pp. 421-428
Author(s):  
Irish C. Juanatas ◽  
Ma. Corazon G. Fernando ◽  
Ace C. Lagman ◽  
John Benedict C. Legaspi
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Performance Rating ◽  
Bayes Algorithm
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