A Naïve Bayes Sentiment Analysis for Fintech Mobile Application User Review in Indonesia

Document text classification is utilized for information feature extraction and retrieval as the primary source of digitizing the written information using text classification techniques. Text classification can provide much more information by analyzing the text using machine learning methods. One of the practical applications of text classification is mood detection using machine learning algorithms. Machine learning algorithms allow a practical and beneficial platform for analyzing and detect mood from the text documents. However, there are few applications to analyze the text in Arabic with high accuracy and especially detecting mood using Arabic text documents, messages, or blogs. The main objective of using machine learning algorithms is to detect the accurate mood target value to the given messages. This study focuses on four mood classes (Happy, Sad, Angry, fear). The text analyzed for this study were gathered from some social media and internet blogs. Three kinds of techniques have been identified based on machine learning approaches: Naïve Bays algorithm, k-Nearest Neighbors (KNN) algorithm, and Support Vector Machine (SVM) algorithm. The text was further analyzed for feature selection related to text mining, feature correlation analysis, and information gain. Lastly, splitting the text into training and testing sets for possible models using robust classifiers. After running the selected classifiers for our study, the results showed that Naïve bays classier had the highest achievement in terms of accuracy. Naïve Bays classifiers received 70%, support vector machine classifiers obtained 68.33%, and k-Nearest Neighbors (KNN) algorithm yield 51.67%.

Section: Classificationmentioning

confidence: 99%

Section: Translation Datamentioning

confidence: 99%

Mood Detection Based on Arabic Text Documents using Machine Learning Methods

Hussein¹

2020

“…Both datasets also implemented data cleansing. Results showed that a 3% margin of accuracy with Bahasa Indonesia and English compared with Bahasa Only [9].…”

Section: A Naïve Bayes Sentiment Analysis For Fintech Mobile Application User Review In Indonesiamentioning

confidence: 99%

A Semantic Approach of the Naïve Bayes Classification Algorithm

Tesoro¹

2020

This study aims to study the semantic approach of Naïve Bayes Classification Algorithm. From a statistical, probabilistic machine learning model, the classical decision-level classification algorithm which is the Naïve Bayes classifier shows to be efficient on a variety of sentiment classification problems. Naive Bayes is often used in sentiment classification applications and practical experiments because of its simplicity and effectiveness. However, its performance is often degraded because of the reliability of the result. This paper focuses on developing a different approach to the primary sentiment analysis of the NB classifier. The approach leads to the implementation of providing semantic information from lexicon resources together with the semantic calculator. Addressing the problems of the baseline algorithm show that it can be solved by incorporating other algorithm approaches. The comparative results show that the semantic approach is statistically superior and yielded improvements to the baseline classifier. In comparison with the baseline NB algorithm, SNB achieved a relatively favorable classification accuracy with the threshold of 50% to 60%and an average improvement of 11.394% for its accuracy rating while significantly reducing the training time.

“…Putra et al, [12], Oueslati el at., [13] conducted studies on utilizing hashtags and emoticons in tweets to build training datasets for sentiment analysis. The basic idea is to collect tweets containing the sentiment hashtags (e.g., "#sucks", "#notcute") or emoticons (e.g., ":)", ": D", ": -("), and label each tweet as positive or negative based on the polarity of hashtags and emoticons.…”

Section: Emotion Detection Using Hashtags and Emoticonsmentioning

confidence: 99%

Emotion Recognition from Blog Comments Based Automatically Generated Datasets and Ensemble Models

Patacsil¹

2020

Emotions have a large influence on product loyalty and found that an individual have a positive emotional relationship with identified product, trust in the company and make purchases. The customer's decision is not founded on their facts/ information, but they rely on their emotions to get brand loyalty decisions. However, companies are struggling to adapt this strategy even with the huge amount of data they acquired, their utilization of this data as marketing influence and ongoing personalization tactics is limited due to the problem of analyzing voluminous data. This work offers new approach for automated categorization of emotions from textual data and was based on a sentence level utilizing the language translation machine and applying of learning machines and ensemble models. The experiment reveals that the proposed automatic emotion classifier performs well in classifying angry comments both in training and testing evaluation periods. Furthermore, the proposed emotion classifier produces a result of 75% accuracy under ensemble Naïve Bayes (NB) in the classification of sentence emotion during training period. Although during the actual testing period of the test data set, the proposed system was able to correctly classify 76% emotions of the sentences. Some of the reasons and causes for misclassification are analyzed and presented in this paper.