Multinomial Naive Bayes Algorithm Research Articles

Machine Learning for Predicting Personality using Facebook-Based Posts

Social media contributes a lot to human life. People can share their thoughts through text, photos, and voice through social media. Information from social media can be useful, including in personality research. Personality can generally be known through personality tests. In this research, personality prediction is formed to determine personality through Facebook posts without using a personality test. We create a model based on big five personality traits using 5 machine learning algorithms: Support Vector Machine (SVM), Multinomial Naive Bayes, Decision Tree, K-Nearest Neighbor, and Logistic Regression. Data augmentation was also used for balancing the dataset value and trained using stratified 10-fold cross-validation. This research yields the highest f1 score on Openness using Multinomial Naive Bayes algorithm of 82.31% and the highest average is 68.62%. So the five supervised Machine Learning algorithms used in this research produced Multinomial Naive Bayes as the best algorithm to predict personality based on big five personality traits from user postings on Facebook.

Multinomial Naive Bayes Algorithm for Indonesian language Sentiment Classification Related to Jakarta International Stadium (JIS)

The research focuses on analysing public evaluations, particularly those on Google Maps, about the Jakarta International Stadium (JIS). The study aims to employ the multinomial Naive Bayes algorithm to ascertain the sentiment expressed in these reviews. The objective of this study was to employ the multinomial Naive Bayes method to analyse the reviews on Google Maps pertaining to the Jakarta International Stadium (JIS). The utilised data consists of 2971 public reviews on Google Maps specifically pertaining to Jakarta International Stadium (JIS). These reviews were acquired through web scraping using a data miner. The acquired data is next processed in the text preparation phase to generate a prepared dataset suitable for analysis. This preprocessing stage includes operations such as casefolding, stopword removal, tokenizing, and stemming. The study yielded an accuracy of 0.83, or 83%, when tested on 733 data points. Out of these, 292 positive data points were correctly anticipated, while 59 positive data points were incorrectly forecast. Additionally, 317 negative data points were correctly predicted, while 65 negative data points were incorrectly predicted. The conducted modelling is subsequently categorised using a novel dataset of 161 review data points, with the objective of discerning the sentiment expressed within the dataset. The analysis of the new dataset yielded 101 reviews with positive sentiment and 50 reviews with negative sentiment.

OPTIMIZATION NAÏVE BAYES ALGORITHM IN SENTIMENT ANALYSIS OF BUKALAPAK APP REVIEWS

Bukalapak application reviews on Google Play Store include useful information if processed correctly. The activity of analyzing application reviews is not enough to see the number of stars, it is necessary to see the entire contents of the review comments to be able to know the intent of the review. Sentiment analysis system is a system used to automatically analyze reviews. Review data is retrieved via the bukalapak application API and then classified using Naive Bayes Multinomial. A total of 1,000 reviews of bukalapak application users were collected to be used as dataset samples. The purpose of this research is to determine the accuracy level of sentiment analysis using the multinomial Naive Bayes algorithm. The stages of this research include, data collection, automatic labeling using python, pre-processing, sentiment classification, and evaluation. In the pre-processing stage there are 6 stages, namely Cleaning, Casefolding, Word Normalizer, Tokenizing, Stopword Removal and Stemming. TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighting. The data will be grouped into two categories, namely negative and positive. The test results show an accuracy value of 90%, this result shows that the bukalapak application reviews tend to be negative. The research at this time only looks for accuracy values and provides an overview of the bulapak application to potential new users.

Comparison of NB and SVM in Sentiment Analysis of Cyberbullying using Feature Selection

In the past few decades, the internet has become an inseparable part of human life. It provides ease of access and permeates almost every aspect of human existence. One of the internet platforms that is widely used by people around the world is social media. Apart from being spoiled with the convenience and efficiency offered by social media to support daily life, it has gained popularity among a wide audience. This has positive implications when utilized effectively, but it cannot be denied that there are negative consequences if not utilized properly. One such consequence is the prevalence of cyberbullying activities on social media. Cyberbullying has become a major concern for the public and social media users, prompting researchers to leverage information technology in developing technologies that can identify the elements of cyberbullying, particularly on social media platforms. Sentiment analysis has been employed by researchers to identify the components of cyberbullying in online platforms. Sentiment analysis involves the application of natural language processing techniques and text analysis to identify and extract subjective information from text. This study aims to compare the Naive Bayes algorithm and the Support Vector Machine algorithm, while utilizing feature selection, specifically chi-square, to enhance the accuracy of both algorithms in classifying Instagram comments. The experimental results indicate that the Multinomial Naive Bayes (MNB) algorithm outperforms the Support Vector Machine (SVM) algorithm, achieving an accuracy of 83.85% without feature selection and 90.77% with feature selection. Meanwhile, SVM achieves an accuracy of 82.31% without feature selection and 90% with feature selection. Evaluation through the confusion matrix and classification report reveals that MNB exhibits better precision and recall rates compared to SVM in identifying bullying and non-bullying classes. The use of feature selection enhances the performance of both algorithms in classifying Instagram comments related to cyberbullying.

Sentiment Analysis on Social Media Against Public Policy Using Multinomial Naive Bayes

Purpose: The purpose of this study is to analyze text documents from Twitter about public policies in handling COVID-19 that are currently or have been determined. The text documents are classified into positive and negative sentiments by using Multinomial Naive Bayes.Methods: In this research, CRISP-DM is used as a method for conducting sentiment analysis, starting from the business understanding process, data understanding, data preparation, modeling, and evaluation. Multinomial Naive Bayes has been applied in building classification based on text documents. The results of this study made a model that can be used in classifying texts with maximum accuracy.Result: The results of this research are focused on the model or pattern generated by the Multinomial Naive Bayes Algorithm. The classification results of social media users' tweets against the new normal policy obtained good results with an accuracy value of 90.25%. After classifying the tweets of social media users regarding the new normal policy, the results show that more than 70% agreed and supported the new normal policy.Novelty: This study resulted in how classification can be done with Multinomial Naive Bayes and this algorithm can work well in recognizing text sentiments that generate positive or negative opinions regarding public policies handling COVID-19. So, the research provided conclusions about the views of people around the world on new normal public policy.

Linguistic Analysis of Hindi-English Mixed Tweets for Depression Detection

According to recent studies, young adults in India faced mental health issues due to closures of universities and loss of income, low self-esteem, distress, and reported symptoms of anxiety and/or depressive disorder (43%). This makes it a high time to come up with a solution. A new classifier proposed to find those individuals who might be having depression based on their tweets from the social media platform Twitter. The proposed model is based on linguistic analysis and text classification by calculating probability using the TF ∗ IDF (term frequency-inverse document frequency). Indians tend to tweet predominantly using English, Hindi, or a mix of these two languages (colloquially known as Hinglish). In this proposed approach, data has been collected from Twitter and screened via passing them through a classifier built using the multinomial Naive Bayes algorithm and grid search, the latter being used for hyperparameter optimization. Each tweet is classified as depressed or not depressed. The entire architecture works over English and Hindi languages, which shall help in implementation globally and across multiple platforms and help in putting a stop to the ever-increasing depression rates in a methodical and automated manner. In the proposed model pipeline, composed techniques are used to get the better results, as 96.15% accuracy and 0.914 as the F1 score have been attained.

SMS CLASSIFICATION: CONJOINT ANALYSIS OF MULTINOMIAL NAIVE BAYES APPLICATION

Nowadays there are ham and spam messages that are sent to the users via SMS. The aim of this article is to show how machine learning and text processing technologies can be used in order to predict the trustworthiness of SMS messages. The data we are going to use is collected from Kaggle. This study is very important because it helps us to understand how machine learning and text processing can be used in order to predict message trustworthiness. At the time of writing this article, there was not an article explaining how this can be done using the Multinomial Naive Bayes algorithm. The methodology we used in this article consists of dataset collection, data cleaning, data analysis, text preparation, and training model. This will be seen in the methodology section in great detail. At the end of this article, we will show to u the accuracy that we have got when implementing a Multinomial Naive Bayes algorithm for the classification of SMS messages. This study was quite beneficial because anyone can see how Multinomial Naive Bayes algorithm usage can be beneficial in order to predict the trustworthiness of SMS messages.

Financial Context News Sentiment Analysis for the Lithuanian Language

Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).

A comparison of classification algorithms for hate speech detection

Freedom of opinion through social media is frequently affect a negative impact that spreads hatred. This study aims to automatically detect Indonesian tweets that contain hate speech on Twitter social media. The data used amounted to 4,002 tweets related to politics, religion, ethnicity and race in Indonesia. The application model uses classification methods with machine learning algorithms such as Naïve Bayes, Multi Level Perceptron, AdaBoost Classifier, Decision Tree and Support Vector Machine. The study also compared the performance of the model using SMOTE to overcome imbalanced data. The results show that the Multinomial Naive Bayes algorithm produces the best model with the highest recall value of 93.2% which has an accuracy value of 71.2% for the classification of hate speech. Therefore, the Multinomial Naïve Bayes algorithm without SMOTE is recommended as the model to detect hate speech on social media.

A Comprehensive Framework for Ontology Based Classifier using Unstructured Data

The knowledge contained within the natural language data can be used to build expert systems. Classifying unstructured data using ontology and text classification algorithms to extract information is one way of approaching the problem of building intelligent systems. One major problem with text processing is most data generated is unstructured and ambiguous, as, data with a structure helps to identify meaningful patterns and eventually exhibit the latent knowledge. Ambiguity in natural language affects accuracy of categorization. Also, Natural Language Processing techniques when combined with semantic data modeling through ontological knowledge will also solve the problem of domain knowledge representation thereby enabling improved data classification facilities, particularly in large datasets where number of features scale to unmanageable proportions. In this paper, the domain knowledge is presented as a knowledge graph, derived from the semantic data modeling. Further, to achieve better Multi Class classification, Multinomial Naive Bayes algorithm is applied to categorize items in their respective classes. For the experiments, Data about various news groups were used for testing the accuracy of the model. Experimental results have proved that the proposed classifier performs better compared to existing systems.

Emotion Identification between POMS and Multinomial Naive Bayes Algorithm Using Twitter API

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Identifikasi Konten Kasar Pada Tweet Bahasa Indonesia

Penelitian ini bertujuan untuk melakukan identifikasi tweet yang mengandung konten kasar atau ofensif. Untuk melakukan hal tersebut, ada lima tahap yang dilalui yaitu pengumpulan data, preprocessing, ekstraksi fitur, klasifikasi, dan evaluasi. Adapun algoritma klasifikasi yang digunakan adalah Multinomial Naïve Bayes dan Support Vector Machine dengan linear kernel. Berdasarkan eksperimen, diketahui bahwa performa algoritma Support Vector Machine dengan linear kernel lebih unggul secara keseluruhan dibandingkan dengan algoritma Multinomial Naïve Bayes. Hal tersebut dilihat dari perolehan nilai accuracy, precision, recall, dan F1-score untuk algoritma SVM berturut-turut adalah 0.9928; 0.9914; 0.9946; dan 0.9930. Sedangkan perolehan accuracy, precision, recall, dan F1-score algoritma Multinomial Naïve Bayes berturut-turut adalah 0.9834; 0.9912; 0.9762; dan 0.9836. Namun demikian, dapat disimpulkan bahwa algoritma Support Vector Machine dan Multinomial Naïve Bayes memiliki performa yang hampir sama baiknya. Hal tersebut dibuktikan dengan selisih capaian performa yang tidak terlalu mencolok dari keduanya.

Labeling Analysis in the Classification of Product Review Sentiments by using Multinomial Naive Bayes Algorithm

Along with the development of technology, e-commerce also experienced a fairly rapid development. The existence of e-commerce becomes another consumer alternative to make it easier for them to fulfill their needs. After buying the goods, consumers are free to assess the products they buy. Product reviews and ratings provided by consumers are one means that can be used to increase sales and can also be used to determine the decision in purchasing a product by reading the product reviews. However, using ratings and reviews alone is not enough to summarize one’s opinion. Therefore, in this Final Project built a system that can classify opinions on product reviews into positive and negative sentiments by utilizing the rating. The dataset used is Grocery and Gourmet Food from Amazon as much as 50,000 which will then be labeled using Labeling Methods Average and Binary. The classification of this opinion uses the approach of Supervised learning Algorithm Multinomial Naïve Bayes. The result of this research shows that labeling using Method Average is suitable for processing Grocery and Gourmet Food Dataset and proves that the best ratio of feature selection usage is 20% succeed to produce 80.48% accuracy.

A Case Study in Text Mining of Discussion Forum Posts: Classification with Bag of Words and Global Vectors

Abstract Despite the rapid growth of other types of social media, Internet discussion forums remain a highly popular communication channel and a useful source of text data for analyzing user interests and sentiments. Being suited to richer, deeper, and longer discussions than microblogging services, they particularly well reflect topics of long-term, persisting involvement and areas of specialized knowledge or experience. Discovering and characterizing such topics and areas by text mining algorithms is therefore an interesting and useful research direction. This work presents a case study in which selected classification algorithms are applied to posts from a Polish discussion forum devoted to psychoactive substances received from home-grown plants, such as hashish or marijuana. The utility of two different vector text representations is examined: the simple bag of words representation and the more refined embedded global vectors one. While the former is found to work well for the multinomial naive Bayes algorithm, the latter turns out more useful for other classification algorithms: logistic regression, SVMs, and random forests. The obtained results suggest that post-classification can be applied for measuring publication intensity of particular topics and, in the case of forums related to psychoactive substances, for monitoring the risk of drug-related crime.

KLASIFIKASI OPINI GREEN AND CLEAN KABUPATEN LAMONGAN MENGGUNAKAN ALGORITMA MULTINOMIAL NAIVE BAYES

This study aims to process and classify an opinion (Opinion mining), opinion is a subjective statement that reflects public sentiment or perception of the entity or activity. Most opinions has not been managed well, if The Opinions properly managed will provide important information can be used to make improvements toward better at an activity or program. This study focuses on the processing of opinions that come from public opinion In Lamongan against LGC program which includes cleanliness, green and financial. The study was divided into two phases, namely the training process to produce data (dataset) to perform the classification process and the subjective (datates). Both processes are aimed to extract attributes and object components that have been commented upon in any document and to determine whether positive or negative comments. The results of the subjective test classification using Multinomial Naive Bayes algorithm has a success rate above 80% classification accuracy when it is matched with the manual classification

A novel classification approach based on Naïve Bayes for Twitter sentiment analysis

With rapid growth of web technology and dissemination of smart devices, social networking service(SNS) is widely used. As a result, huge amount of data are generated from SNS such as Twitter, and sentiment analysis of SNS data is very important for various applications and services. In the existing sentiment analysis based on the Naive Bayes algorithm, a same number of attributes is usually employed to estimate the weight of each class. Moreover, uncountable and meaningless attributes are included. This results in decreased accuracy of sentiment analysis. In this paper two methods are proposed to resolve these issues, which reflect the difference of the number of positive words and negative words in calculating the weights, and eliminate insignificant words in the feature selection step using Multinomial Naive Bayes(MNB) algorithm. Performance comparison demonstrates that the proposed scheme significantly increases the accuracy compared to the existing Multivariate Bernoulli Naive Bayes(BNB) algorithm and MNB scheme.

Analysis on Hinglish Opinion Using Multinomial Naive Bayes Algorithm

Sentimental Analysis is the study that analyses people’s sentiments, appraisals, attitudes, and emotions towards entities such as products, services, organizations, individuals, issues, events, the topics, and their attributes. These days, the latest mobile devices and websites are interpreting mashup language based keyboards; this has enabled many users to express their opinions and views about products in ‘Hinglish’. This research is focused on conducting sentimental analysis of ‘Tweets’ written in Hinglish mashup language. The system built here is highly accurate as it is on Multinomial Naive Bayes algorithm that uses machine learning as basis to classify positive, negative or neutral opinion of products safety. The results obtained using that system has high true positive rate and low false rate as compared to previous Naive Bayes algorithm.