Rule Based Systems in a Distributed Environment: Survey

Bagavathi, Arunkumar; Tzacheva, Angelina A.

doi:10.11159/cca17.107

Cited by 7 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Authors Tzacheva et al [18], discover low cost actionable patterns and recommendations in distributed environment. They use the algorithm described by [19] and develop the system using Apache Spark framework and Hadoop Distributed File System (HDFS) for scalability and efficiency. They evaluate the approach using car evaluation dataset, mammographic dataset and achieve best results.…”

Section: Actionable Pattern Mining and Applicationsmentioning

confidence: 99%

Emotion Mining in Social Media Data

Ranganathan

Tzacheva

2019

Computer Science &Amp; Information Technology (CS &Amp; IT )

View full text Add to dashboard Cite

Emotions are known to influence the perception of human beings along with their memory, thinking and imagination. Human perception is important in today's world in a wide range of factors including but not limited to business, education, art, and music. Microblogging and Social networking sites like Twitter, Facebook are challenging sources of information that allow people to share their feelings and thoughts on a daily basis. In this paper we propose an approach to automatically detect emotions on Twitter messages that explores characteristics of the tweets and the writer's emotion using Support Vector Machine LibLinear model and achieve 98% accuracy. Emotion mining gained attraction in the field of computer science due to the vast variety of systems that can be developed and promising applications like remote health care system, customer care services, smart phones that react based on users's emotion, vehicles that sense emotion of the driver. These emotions help understand the current state of user. In order to perform suitable actions or provide suggestions on how user's can enhance their feeling for a better healthy lifestyle we use actionable recommendations. In this work we extract action rules with respect to the user emotions that help provide suggestions for user's.

show abstract

Section: Actionable Pattern Mining and Applicationsmentioning

confidence: 99%

Emotion Mining in Social Media Data

Ranganathan

Tzacheva

2019

Computer Science &Amp; Information Technology (CS &Amp; IT )

View full text Add to dashboard Cite

show abstract

“…Since the launch of Twitter, there has been lots of attention given to it in order to implemented sentiment analysis on the data available on it. In order to understand the opinions of Twitter users, both academic as well as business organizations have focused a lot such that effective solutions can be provided [2]. Several tools were generated and comparisons were made amongst them in order to evaluate the performances.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis Approach Based N-Gram and KNN Classifier

Tripathi

Singh²

2018

ijarcs

View full text Add to dashboard Cite

Abstract:The sentiment analysis is the approach which is design to analysis positive, negative and neural aspects towards any approach. In the past years, many techniques are designed for the sentiment analysis of twitter data. Based on the previous study about sentiment analysis, novel approach is presented in this research paper for the sentiment analysis of twitter data. The proposed approach is the combination of feature extraction and classification techniques. The N-gram algorithm is applied for the feature extraction and KNN classifier is applied to classify input data into positive, negative and neural classes. To validate the proposed system, performance is analyzed in terms of precision, recall and accuracy. The experiments results of proposed system show that it performs well as compared to existing system which is based on SVM classifier.

show abstract

“…They have substantiated the reliability and efficiency for storage and processing bulk data sets. Apache provides various open source like Hadoop [4], Spark [21] [8], Hive, and Pig to process and handle huge data in the distributed system [2]. Hadoop is a distributed computing framework, to work with large datasets, across multiple computers, using a single programming model in a parallel fashion.…”

Section: Introductionmentioning

confidence: 99%

Actionable pattern discovery for Sentiment Analysis on Twitter Data in clustered environment

Ranganathan

Irudayaraj

Bagavathi

et al. 2018

IFS

Self Cite

View full text Add to dashboard Cite

Actionable Patterns are desired knowledge to be mined from large datasets. Action Rules are vital data mining method for gaining actionable knowledge from the datasets. They recommend actions which users can undertake to their advantage, or to accomplish their goal. Meta actions are the sub-actions to the Action Rules, which intends to change the attribute value of an object, under consideration, to attain the desirable value. The essence of this paper is to propose a new optimized and more promising system, in terms of speed and efficiency, for generating meta-actions by implementing Specific Action Rule discovery based on Grabbing strategy (SARGS) algorithm, and to apply that for Sentiment Analysis on Twitter data. We perform a comparative analysis of meta-actions generating algorithmic implementation in Apache Spark driven system, conventional Hadoop driven system and Single node machine using the Twitter social networking data and evaluate the results. We implement corpus based Sentimental Analysis of social networking data, and test the total time taken by the systems and their sub components for the data processing. Results show faster computational time for Spark system compared to Hadoop MapReduce and Single node machine for the meta-action generation methods.

show abstract

Rule Based Systems in a Distributed Environment: Survey

Cited by 7 publications

References 25 publications

Emotion Mining in Social Media Data

Emotion Mining in Social Media Data

Sentiment Analysis Approach Based N-Gram and KNN Classifier

Actionable pattern discovery for Sentiment Analysis on Twitter Data in clustered environment

Contact Info

Product

Resources

About