New Words Identification Based on Ensemble Methods

2014 ◽  
Vol 602-605 ◽  
pp. 1626-1629
Author(s):  
Chen Zhang ◽  
Yu Quan Chen
Keyword(s):  
Naive Bayes ◽  
Ensemble Methods ◽  
Chinese Word ◽  
Frequency Filtering ◽  
Layer Model ◽  
High Coverage ◽  
New Words ◽  
Bayes Model ◽  
Segment Tree ◽  
Drawing Method

In order to identify new words in huge Chinese corpus efficiently, this paper comes up with an algorithm based on ensemble methods. At first we perform Chinese word segmenting with Trie and build segment-tree. Then we select words pattern drawing method, frequency filtering, independent word probability and naive Bayes model to be sub-models of ensemble methods and train them independently. At last we integrate results from different sub-models with a multi-layer model. In experiment, this algorithm is proved to be quite fast as well as product precise and high-coverage results.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

scholarly journals Session Segmentation Method Based on Naïve Bayes Model

2012 ◽  
Vol 6-7 ◽  
pp. 576-582
Author(s):  
Ping Li ◽  
Ming Liang Cui ◽  
Zhen Shan Hou ◽  
Liu Liu Wei ◽  
Wen Hao Ying ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Time Interval ◽  
Segmentation Method ◽  
Retrieval Process ◽  
Search Activity ◽  
Query Suggestion ◽  
Bayes Model ◽  
Discrimination Model ◽  
Naïve Bayes Model

Session segmentation can not only contribute a lot to the further and deeper analysis of user’s search behavior but also act as the foundation of other retrieval process researches based on users’ complicated search behaviors. This paper proposes a session boundary discrimination model utilizing time interval and query likelihood on the basis of Naive Bayes Model. Compared with previous study, the model proposed in this paper shows a prominent improvement through experiment in three aspects, which is: recall ratio, precision ratio and value F. Owing to its advantage in session boundary discrimination, the application of the model can serve as a tool in fields like personalized information retrieval, query suggestion, search activity analysis and other fields which is related to search results improvement.

scholarly journals An Enhanced Naive Bayes Model for Dissolved Oxygen Forecasting in Shellfish Aquaculture

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 217917-217927
Author(s):  
Dashe Li ◽  
Jiajun Sun ◽  
Huanhai Yang ◽  
Xueying Wang
Keyword(s):  
Dissolved Oxygen ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Shellfish Aquaculture ◽  
Bayes Model ◽  
Naïve Bayes Model

Sign prediction by motif naive Bayes model in social networks

2020 ◽  
Vol 541 ◽  
pp. 316-331
Author(s):  
Si-Yuan Liu ◽  
Jing Xiao ◽  
Xiao-Ke Xu
Keyword(s):  
Social Networks ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Model ◽  
Naïve Bayes Model

scholarly journals A Multilayer Naïve Bayes Model for Analyzing User’s Retweeting Sentiment Tendency

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Mengmeng Wang ◽  
Wanli Zuo ◽  
Ying Wang
Keyword(s):  
Information Diffusion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Structure Information ◽  
Bayes Model ◽  
Dynamic Social Network ◽  
Dynamic Social Networks ◽  
Text Information ◽  
Naïve Bayes Model ◽  
Tendency Analysis

Today microblogging has increasingly become a means of information diffusion via user’s retweeting behavior. Since retweeting content, as context information of microblogging, is an understanding of microblogging, hence, user’s retweeting sentiment tendency analysis has gradually become a hot research topic. Targeted at online microblogging, a dynamic social network, we investigate how to exploit dynamic retweeting sentiment features in retweeting sentiment tendency analysis. On the basis of time series of user’s network structure information and published text information, we first model dynamic retweeting sentiment features. Then we build Naïve Bayes models from profile-, relationship-, and emotion-based dimensions, respectively. Finally, we build a multilayer Naïve Bayes model based on multidimensional Naïve Bayes models to analyze user’s retweeting sentiment tendency towards a microblog. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of dynamic retweeting sentiment features and temporal information in retweeting sentiment tendency analysis. What is more, we provide a new train of thought for retweeting sentiment tendency analysis in dynamic social networks.

scholarly journals The Research of Clinical Decision Support System Based on Three-Layer Knowledge Base Model

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yicheng Jiang ◽  
Bensheng Qiu ◽  
Chunsheng Xu ◽  
Chuanfu Li
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Base ◽  
Clinical Decision Support ◽  
Support System ◽  
Naive Bayes ◽  
Clinical Decision ◽  
Disease Symptom ◽  
Layer Model ◽  
Bayes Algorithm

In many clinical decision support systems, a two-layer knowledge base model (disease-symptom) of rule reasoning is used. This model often does not express knowledge very well since it simply infers disease from the presence of certain symptoms. In this study, we propose a three-layer knowledge base model (disease-symptom-property) to utilize more useful information in inference. The system iteratively calculates the probability of patients who may suffer from diseases based on a multisymptom naive Bayes algorithm, in which the specificity of these disease symptoms is weighted by the estimation of the degree of contribution to diagnose the disease. It significantly reduces the dependencies between attributes to apply the naive Bayes algorithm more properly. Then, the online learning process for parameter optimization of the inference engine was completed. At last, our decision support system utilizing the three-layer model was formally evaluated by two experienced doctors. By comparisons between prediction results and clinical results, our system can provide effective clinical recommendations to doctors. Moreover, we found that the three-layer model can improve the accuracy of predictions compared with the two-layer model. In light of some of the limitations of this study, we also identify and discuss several areas that need continued improvement.

scholarly journals Mining the crime data using naïve Bayes model

2021 ◽  
Vol 23 (2) ◽  
pp. 1084
Author(s):  
Lourdes M. Padirayon ◽  
Melvin S. Atayan ◽  
Jose Sherief Panelo ◽  
Carlito R. Fagela, Jr
Keyword(s):  
Law Enforcement ◽  
Police Officers ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Police Departments ◽  
Data Set ◽  
Crime Data ◽  
Bayes Model ◽  
Massive Number ◽  
Index Crime

<p>A massive number of documents on crime has been handled by police departments worldwide and today's criminals are becoming technologically elegant. One obstacle faced by law enforcement is the complexity of processing voluminous crime data. Approximately 439 crimes have been registered in sanchez mira municipality in the past seven years. Police officers have no clear view as to the pattern crimes in the municipality, peak hours, months of the commission and the location where the crimes are concentrated. The naïve Bayes modelis a classification algorithm using the Rapid miner auto model which is used and analyze the crime data set. This approach helps to recognize crime trends and of which, most of the crimes committed were a violation of special penal laws. The month of May has the highest for index and non-index crimes and Tuesday as for the day of crimes. Hotspots were barangay centro 1 for non-index crimes and barangay centro 2 for index crimes. Most non-index crimes committed were violations of special law and for index crime rape recorded the highest crime and usually occurs at 2 o’clock in the afternoon. The crime outcome takes various decisions to maximize the efficacy of crime solutions.</p>

scholarly journals Constructing decision rules from naive bayes model for robust and low complexity classification

2021 ◽  
Vol 7 (1) ◽  
pp. 76
Author(s):  
Nabeel Hashim Al-Aaraji ◽  
Safaa Obayes Al-Mamory ◽  
Ali Hashim Al-Shakarchi
Keyword(s):  
Naive Bayes ◽  
Decision Rules ◽  
Low Complexity ◽  
Naïve Bayes ◽  
Bayes Model ◽  
Coverage Accuracy ◽  
Rule Set ◽  
Classification Tasks ◽  
M Estimate ◽  
Classification Capability

A large spectrum of classifiers has been described in the literature. One attractive classification technique is a Naïve Bayes (NB) which has been relayed on probability theory. NB has two major limitations: First, it requires to rescan the dataset and applying a set of equations each time to classify instances, which is an expensive step if a dataset is relatively large. Second, NB may remain challenging for non-statisticians to understand the deep work of a model. On the other hand, Rule-Based classifiers (RBCs) have used IF-THEN rules (henceforth, rule-set), which are more comprehensible and less complex for classification tasks. For elevating NB limitations, this paper presents a method for constructing a rule-set from the NB model, which serves as RBC. Experiments of the constructing rule-set have been conducted on (Iris, WBC, Vote) datasets. Coverage, Accuracy, M-Estimate, and Laplace are crucial evaluation metrics that have been projected to rule-set. In some datasets, the rule-set obtains significant accuracy results that reach 95.33 %, 95.17% for Iris and vote datasets, respectively. The constructed rule-set can mimic the classification capability of NB, provide a visual representation of the model, express rules infidelity with acceptable accuracy; an easier method to interpreting and adjusting from the original model. Hence, the rule-set will provide a comprehensible and lightweight model than NB itself.

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