Integrating a Lexicon Based Approach and K Nearest Neighbour for Malay Sentiment Analysis

Al-Saffar, Ahmed; Omar, Nazlia

doi:10.3844/jcssp.2015.639.644

Cited by 18 publications

(19 citation statements)

References 10 publications

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“…Many researchers utilize machine learning approach to classify sentiments such as Naïve Bayes [10] [11], k-NN [12], [13] [14], and Support Vector Machine (SVM) [11]. In general, this approach collected large data and split into two; training data set and test data set.…”

Section: A Machine Learning Approachmentioning

confidence: 99%

Developing Cross-lingual Sentiment Analysis of Malay Twitter Data Using Lexicon-based Approach

Zabha¹,

Ayop²,

Anawar³

et al. 2019

ijacsa

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Sentiment analysis is a process of detecting and classifying sentiments into positive, negative or neutral. Most sentiment analysis research focus on English lexicon vocabularies. However, Malay is still under-resourced. Research of sentiment analysis in Malaysia social media is challenging due to mixed language usage of English and Malay. The objective of this study was to develop a cross-lingual sentiment analysis using lexicon based approach. Two lexicons of languages are combined in the system, then, the Twitter data were collected and the results were determined using graph. The results showed that the classifier was able to determine the sentiments. This study is significant for companies and governments to understand people's opinion on social network especially in Malay speaking regions.

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Section: A Machine Learning Approachmentioning

confidence: 99%

Developing Cross-lingual Sentiment Analysis of Malay Twitter Data Using Lexicon-based Approach

Zabha¹,

Ayop²,

Anawar³

et al. 2019

ijacsa

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“…Alsaffar et al [4] displayed a methodology for the usage of total knowledge in remedial examination by applying accord systems. They differentiated the accuracy procured and that system against the diagnostics precision came to through the data of a lone ace.…”

Section: Literature Workmentioning

confidence: 99%

Machine Learning Methodology for Medical Data Analysis for Prediction of Risk

Sravanthi¹,

Harika²,

Archana³

et al. 2020

jcr

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Mining data is a nontrivial procedure of finding information from a large volume of data. Such information can be helpful in settling on significant choices. Medical data show special features including noise coming about because of human just as methodical blunders, missing qualities and even meager conditions. The nature of data has huge ramifications for the nature of the mining results. Medical data classification is important to perform preprocessing steps so as to expel or at least lighten a portion of the issues related with medical data. Clustering is a descriptive-based data mining task. The clustering algorithm is also called as unsupervised learning algorithm that learns the unlabeled dataset and groups or clusters the instances based on their similarity and builds the clustering model. Clustering is same as classification in which data is grouped, but in this, groups are not predefined. In clustering, clusters are not predefined. Classification of different types of clustering is as follows: Hierarchical clustering, Partition clustering, Categorical clustering, Density based clustering and Grid based clustering. The main intension of the research is to classify the medical data with high accuracy value. In order to achieve promising results, a novel data classification methods have been designed that utilize a Improved Cluster Optimal Classifier (ICOC). The proposed method is compared with traditional methods and the results show that the proposed method performance is better and accurate.

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“…As such, it is called non-parametric or nonlinear as it does not assume a functional form. The weighted sum in KNN classification [30] can be represented as (9):…”

Section: Tf Idf Tf T D Idf T  (3)mentioning

confidence: 99%

Hybrid optimization for feature selection in opinion mining

Moorthi¹,

Mathivanan²

2017

IJET

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A sub-discipline of Information Retrieval (IR) is opinion mining and the lexicon of computers is not concerned of the subject of the document, but about the opinion expressed. It has caused a large impact in the arena of academics and industry as it has a wide area of research and the applications are widespread. Feature selection is a vital step in opinion mining, as its individual feature decides the opinions expressed by the customers. Feature selection reduces the dimensionality of data by avoiding non-relevant features; it can be considered as a necessary and excellent process for data mining applications. In this study, feature subset is optimized through Particle Swarm Optimization (PSO) algorithm, Cuckoo Search (CS) algorithm and hybridized PSO-CS algorithm. Classification is done through Naïve bayes and K-Nearest Neighbours (KNN) classifiers. Feature extraction has its basis on Term Frequency-Inverse Document Frequency (TF-IDF). The accuracy of classification precision is increased by the reduction in size of feature subset and computational complexity.

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Integrating a Lexicon Based Approach and K Nearest Neighbour for Malay Sentiment Analysis

Cited by 18 publications

References 10 publications

Developing Cross-lingual Sentiment Analysis of Malay Twitter Data Using Lexicon-based Approach

Developing Cross-lingual Sentiment Analysis of Malay Twitter Data Using Lexicon-based Approach

Machine Learning Methodology for Medical Data Analysis for Prediction of Risk

Hybrid optimization for feature selection in opinion mining

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