As E-Commerce is becoming more popular, the number of product reviews that a product received grows exponentially. In this context, others’ opinions will play a vital role to make a decision to select among multiple options involves valuable resources like money and time, where people usually depends on their peers’ past experiences in the form of reviews. Many companies use opinion mining and sentiment analysis as part of their research. Detecting sentiment in social media like Facebook, Twitter, online blogs and reviews have become an essential task as they have been influencing every business organization. In this paper, we would like to analyze the Fuzzy rule-based systems (FRBSs) with FRBS models, namely, Mamdani and Takagi Sugeno Kang (TSK) using FRBS package in R, and a comparison with other common classification methods. Additionally, the performance of FRBSs method is measured in terms of precision, Recall and F-measure to find accuracy in classification. © 2017 SERSC.

Dharmendra Singh Rajput

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Basha S.M

Zhenning Y

Iyengar N.C.S.N

Caytiles R.D.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

International Journal of Grid and Distributed Computing

Nowadays in entertainment, cinema industry has become one of the most popular industries, gaining the attention of public toward them by making unnecessary stunts by the production team in promoting their movie and influencing the public to watch the movie at least for one time. By deeply understanding the impact of a particular movie in advance, using reviews made after watching the movie benefits others in saving the major resources like time and money. The objective of our research is to save the time and money spent on watching the movie in theaters and motivating them to use up their valuable time with family members, especially during weekends. In this paper, we aim to demonstrate the application on sentiment classification using decision tree algorithm available in KNIME to rate the movie performance. In which, the textual data from the document are converted into strings, and these strings are preprocessed to get numerical document vectors. Later, from the document vectors the sentiment class is extracted and the predicted model is built and evaluated. In our experimental work, 93.97% of classification accuracy with 0.863 Cohen’s value was achieved in classifying the sentiments from the movie reviews. © 2019, Springer Nature Singapore Pte Ltd.

Proceedings of the 2nd International Conference on Data Engineering and Communication Technology Advances in Intelligent Systems and Computing

Classification of Sentiments from Movie Reviews Using KNIME

In Machine Learning and statistics attribute/feature selection is used in predictive model construction. This help the Machine in interpreting the features easily by discovering good insight and improves efficiency in predictive modeling. The objective of our research is to improve the classification accuracy by knowing the most important feature from any given dataset. In this research, we used two techniques namely Data partition and K Fold, in evaluating the importance of each feature from the randomly generated dataset with 5399 instances and 20 attributes. In Data partitioning, the attribute with lowest accuracy is filtered out. Where as in K Fold cross validation, attributes with biggest error is removed from the original dataset. In our experiments, the evaluation parameters considered are Recall. Precision and F-Measure. Finally the accuracy rate of both the techniques are compared. The finding in our research stats that K Fold approach achieves better accuracy of 97.03% than Data partitioning(96.11%) in estimating the importance of features in classification. © 2018 IEEE.

2018 8th International Conference on Communication Systems and Network Technologies (CSNT)

Evaluating the Importance of each Feature in Classification task

The classification task is to predict the value of the target variable from the values of the input variables. If a target is provided as part of the dataset, then classification is a supervised task. It is important to analysis the performance of supervised classification models before using them in classification task. In our research we would like to propose a novel way to evaluated the performance of supervised classification models like Decision Tree and Naïve Bayes using KNIME Analytics platform. Experiments are conducted on Multi variant dataset consisting 58000 instances, 9 columns associated specially for classification, collected from UCI Machine learning repositories (http://archive.ics.uci.edu/ml/datasets/statlog+(shuttle)) and compared the performance of both the models in terms of Classification Accuracy (CA) and Error Rate. Finally, validated both the models using Metric precision, recall and F-measure. In our finding, we found that Decision tree acquires CA (99.465%) where as Naïve Bayes attain CA (90.358%). The F-measure of Decision tree is 0.984, whereas Naïve Bayes acquire 0.7045.

Fulltext

International Journal of Engineering & Technology

Evaluating the Performance of Supervised Classification Models: Decision Tree and Naïve Bayes Using KNIME

2018 International Conference on Advances in Computing and Communication Engineering (ICACCE)

Evaluating the Performance of Deep Learning Techniques on Classification Using Tensor Flow Application

International Journal of Security and Its Applications

Analyzing the performance of Various Fraud Detection Techniques

Weighted Fuzzy Rule Based Sentiment Prediction Analysis on Tweets

Journal	International Journal of Grid and Distributed Computing
Publisher	NADIA
ISSN	2005-4262
Open Access	0