A Rule-Based Approach to Sentiment Classification of Chinese Microblogging Texts

2013 ◽  
Vol 765-767 ◽  
pp. 1441-1445
Author(s):  
Jia Jun Cheng ◽  
Xin Zhang ◽  
Peng Yi Fan ◽  
Pei Li ◽  
Hui Wang
Keyword(s):  
Sentiment Classification ◽  
Classification Methods ◽  
Rule Based ◽  
Rule Based Approach ◽  
Basic Semantic ◽  
Better Than

Chinese microblogging texts are always short and casual, which bring some troubles to the traditional sentiment classification methods based on learning. To overcome this problem, we use a rule-based approach to classify the sentiment of Chinese microblogging texts. According to the characteristics of Chinese microblogging texts, we construct a thesaurus of subjective words for it, summarize the basic semantic rules expressing emotion and propose a rule-based approach to sentiment classification of Chinese microblogging texts. Finally, we compare our approach with a SVM-based approach. Our rule-based approach achieves an accuracy of 0.865, which is better than that of SVM-based approach.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

scholarly journals Impact of ICD10 and secular changes on electronic medical record rheumatoid arthritis algorithms

Rheumatology ◽  
2020 ◽  
Vol 59 (12) ◽  
pp. 3759-3766 ◽  
Author(s):  
Sicong Huang ◽  
Jie Huang ◽  
Tianrun Cai ◽  
Kumar P Dahal ◽  
Andrew Cagan ◽  
...  
Keyword(s):  
Language Processing ◽  
Gold Standard ◽  
International Classification Of Diseases ◽  
Data Mart ◽  
Classification Of Diseases ◽  
Icd Codes ◽  
Rule Based Approach ◽  
Citrullinated Peptide ◽  
Better Than

Abstract Objective The objective of this study was to compare the performance of an RA algorithm developed and trained in 2010 utilizing natural language processing and machine learning, using updated data containing ICD10, new RA treatments, and a new electronic medical records (EMR) system. Methods We extracted data from subjects with ≥1 RA International Classification of Diseases (ICD) codes from the EMR of two large academic centres to create a data mart. Gold standard RA cases were identified from reviewing a random 200 subjects from the data mart, and a random 100 subjects who only have RA ICD10 codes. We compared the performance of the following algorithms using the original 2010 data with updated data: (i) a published 2010 RA algorithm; (ii) updated algorithm, incorporating ICD10 RA codes and new DMARDs; and (iii) published algorithm using ICD codes only, ICD RA code ≥3. Results The gold standard RA cases had mean age 65.5 years, 78.7% female, 74.1% RF or antibodies to cyclic citrullinated peptide (anti-CCP) positive. The positive predictive value (PPV) for ≥3 RA ICD was 54%, compared with 56% in 2010. At a specificity of 95%, the PPV of the 2010 algorithm and the updated version were both 91%, compared with 94% (95% CI: 91, 96%) in 2010. In subjects with ICD10 data only, the PPV for the updated 2010 RA algorithm was 93%. Conclusion The 2010 RA algorithm validated with the updated data with similar performance characteristics as the 2010 data. While the 2010 algorithm continued to perform better than the rule-based approach, the PPV of the latter also remained stable over time.

Metric Analyzer for Kannada Verse Using Rule-Based Approach

2020 ◽  
Vol 17 (9) ◽  
pp. 4007-4011
Author(s):  
R. J. Prathibha ◽  
K. H. Manju Skanda ◽  
S. Juned ◽  
Anup R. Shetty ◽  
B. V. Shashank
Keyword(s):  
Teaching And Learning ◽  
Learning Tool ◽  
Rule Based ◽  
Metric Analysis ◽  
Level Of Understanding ◽  
Rule Based Approach

Metrical poetry in any language is called Chandassu ( ). It generates rhythm to poem when the predefined metric rules are properly followed. The classification of Chandassu is done with the help of syllables known as Laghu ( ), Guru (....) and Gana (..). The proposed metric analyzer for Kannada verse is a rule-based teaching and learning tool devised to identify and classify the Chandassu of input Kannada poem. This tool also contains an exercise module to test the level of understanding of learners about metric analysis. Accuracy obtained by the proposed system is very good.

scholarly journals Self-Organizing Fuzzy Rule-Based Approach for Dealing with the Classification of Indoor Environments for IoT Applications

2019 ◽  
Author(s):  
Ualison Dias ◽  
Eduardo Aguiar ◽  
Michel Hell ◽  
Alvaro Medeiros ◽  
Daniel Silveira
Keyword(s):  
Fuzzy Rule ◽  
Indoor Environments ◽  
Rule Based ◽  
Iot Applications ◽  
Rule Based Approach ◽  
Self Organizing

Atualmente, grande parte dos sensores utilizados em Internet das Coisas adota tecnologia sem fio, a fim de facilitar a construção de redes de sensoriamento. Neste sentido, a classificação do tipo de ambiente no qual estes sensores estão localizados exerce um importante papel no desempenho de tais redes de sensoriamento, uma vez que pode ser utilizada na determinação de níveis mais eficientes de consumo de energia dos sensores que as compõe. Assim, neste trabalho é apresentada uma abordagem baseada em Classificadores Fuzzy Auto-organizáveis para a classificação de ambientes internos a partir de medições em tempo real do sinal de radiofrequência de uma rede de sensoriamento sem fio em um ambiente real. Os resultados experimentais apresentados mostram que a abordagem proposta obteve alto desempenho com baixo custo computacional na solução do problema apresentado.

scholarly journals Deep Rule-Based Approach for the Classification of Wagon Bogie Springs Condition

2021 ◽  
Author(s):  
Carlos Manuel Viriato Neto ◽  
Luca Garcia Honorio ◽  
Eduardo Aguiar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Learning Approach ◽  
Data Set ◽  
Rule Based ◽  
New Model ◽  
Railway Company ◽  
Rule Based Approach ◽  
Classifier Learning

This paper focuses on the new model of classification of wagon bogie springs condition through images acquired by a wayside equipment. As such, we are discussing the application of a deep rule-based (DRB) classifier learning approach to achieve ahigh classification of a bogie, and check if they either have spring problems or not. We use a pre-trained VGG19 deep convolutional neural network to extract the attributes from images to be used as input to the classifiers. The performance is calculated based on the data set composed of images provided by a Brazilian railway company. The presented results of the report demonstrate the relative performance of applying the DRB classifier to the questions raised.

Predictive aspect-based sentiment classification of online tourist reviews

2018 ◽  
Vol 45 (3) ◽  
pp. 341-363 ◽  
Author(s):  
Muhammad Afzaal ◽  
Muhammad Usman ◽  
Alvis Fong
Keyword(s):  
Prediction Accuracy ◽  
Sentiment Classification ◽  
Semantic Relations ◽  
Automatic Extraction ◽  
Data Sets ◽  
Classification Methods ◽  
Real World Data ◽  
The Past ◽  
Negative Orientation

With the increase of online tourists reviews, discovering sentimental idea regarding a tourist place through the posted reviews is becoming a challenging task. The presence of various aspects discussed in user reviews makes it even harder to accurately extract and classify the sentiments. Aspect-based sentiment analysis aims to extract and classify user’s positive or negative orientation towards each aspect. Although several aspect-based sentiment classification methods have been proposed in the past, limited work has been targeted towards the automatic extraction of implicit, infrequent and co-referential aspects. Moreover, existing methods lack the ability to accurately classify the overall polarity of multi-aspect sentiments. This study aims to develop a predictive framework for aspect-based extraction and classification. The proposed framework utilises the semantic relations among review phrases to extract implicit and infrequent aspects for accurate sentiment predictions. Experiments have been performed using real-world data sets crawled from predominant tourist websites such as TripAdvisor and OpenTable. Experimental results and comparison with previously reported findings prove that the predictive framework not only extracts the aspects effectively but also improves the prediction accuracy of aspects.

Multi-criteria classification of spare parts inventories – a web based approach

2015 ◽  
Vol 21 (4) ◽  
pp. 456-477 ◽  
Author(s):  
S. P. Sarmah ◽  
U. C. Moharana
Keyword(s):  
Fuzzy Rule ◽  
Spare Parts ◽  
Multiple Criteria ◽  
Classification Model ◽  
Web Based ◽  
Rule Based ◽  
Content Type ◽  
Total Cost ◽  
Rule Based Approach

Purpose – The purpose of this paper is to present a fuzzy-rule-based model to classify spare parts inventories considering multiple criteria for better management of maintenance activities to overcome production down situation. Design/methodology/approach – Fuzzy-rule-based approach for multi-criteria decision making is used to classify the spare parts inventories. Total cost is computed for each group considering suitable inventory policies and compared with other existing models. Findings – Fuzzy-rule-based multi-criteria classification model provides better results as compared to aggregate scoring and traditional ABC classification. This model offers the flexibility for inventory management experts to provide their subjective inputs. Practical implications – The web-based model developed in this paper can be implemented in various industries such as manufacturing, chemical plants, and mining, etc., which deal with large number of spares. This method classifies the spares into three categories A, B and C considering multiple criteria and relationships among those criteria. The framework is flexible enough to add additional criteria and to modify fuzzy-rule-base at any point of time by the decision makers. This model can be easily integrated to any customized Enterprise Resource Planning applications. Originality/value – The value of this paper is in applying Fuzzy-rule-based approach for Multi-criteria Inventory Classification of spare parts. This rule-based approach considering multiple criteria is not very common in classification of spare parts inventories. Total cost comparison is made to compare the performance of proposed model with the traditional classifications and the result shows that proposed fuzzy-rule-based classification approach performs better than the traditional ABC and gives almost the same cost as aggregate scoring model. Hence, this method is valid and adds a new value to spare parts classification for better management decisions.

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