Sentiment analysis is a method of extracting subjective information from customer reviews. The analysis helps to reveal the consumer insights about the product, a theme, or a service. In the existing literature, various methods such as BoW and TF-IDF are employed for sentiment analysis and deep learning methods are not explored much. We made an attempt to apply Word2Vec feature weighting method for this problem. We carried out various experiments for sentiment analysis on a large dataset IMDB that contains movie review. We compared various feature weighting methods and analyzed using different classifiers, and the best combination was determined. From the experimental results, we conclude that Word2Vec with SGD is the best combination for sentiment classification problem on IMDB dataset. The result shown in the paper can be used as a base for future exploration of opinioned value on any textual data. © 2019, Springer Nature Singapore Pte Ltd.

Sivakumar S

Department of Social Sciences

School of Social Sciences & Languages

Vellore Campus

Rajalakshmi R

Computer Science

School of Computer Science and Engineering

Chennai Campus

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

Comparative Evaluation of Various Feature Weighting Methods on Movie Reviews

Advances in Intelligent Systems and Computing Computational Intelligence in Data Mining

Malware domains generated by Domain Generated Algorithms (DGA) are highly dynamic in nature. The traditional approach of blacklisting the malicious domains is a time consuming approach and are not effective, as the DGA randomly generate the domain names for the malware. For real-time applications, malware detection is to be performed on the fly and hence sophisticated techniques are in demand to address this issue. Even though various machine learning techniques are employed for this purpose, the performance of such algorithms depends on how good the features are designed. In this work, we have proposed a transfer learning technique by combining the best performing Convolutional Neural Network with the machine learning algorithms such as Naive Bayes classifier for detection and classification of DGA generated domains. We have evaluated our approach using the dataset released by DMD 2018 Shared Task for both binary classification and multiclass classification scenario. Our methodology of CNN with NB for binary classification has been awarded the first rank in this DMD 2018 shared task. © Springer Nature Singapore Pte Ltd. 2019.

Communications in Computer and Information Science Security in Computing and Communications

Transfer Learning Approach for Identification of Malicious Domain Names

Information retrieval task has become a difficult task due to the growing size of the web. This demands a simple method for classifying the web pages. We propose an URL based approach, as it avoids downloading the web page contents. Feature weighing methods play an important role in improving the performance of a classifier. In this paper, we explored different weighting methods and conducted various experiments on WebKB dataset. Results show that tf.mi feature weighting technique achieves F1 measure of 79% and outperforms other weighting methods, which is an improvement of 19.6% over existing works on URL based classification. © 2017 The Author(s).

Fulltext

Procedia Computer Science

Experimental Study Of Feature Weighting Techniques For URL Based Webpage Classification

2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Movie review analysis: Emotion analysis of IMDb movie reviews

International Journal of Computer Applications

Comparative Evaluation of Supervised Learning Algorithms for Sentiment Analysis of Movie Reviews

Proceedings of the Third International Symposium on Women in Computing and Informatics - WCI '15

Identifying Health Domain URLs using SVM

2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s)

Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification

Journal	Data powered by TypesetAdvances in Intelligent Systems and Computing Computational Intelligence in Data Mining
Publisher	Data powered by TypesetSpringer Singapore
ISSN	2194-5357
Open Access	No