Data Mining - Concepts and Techniques.

The sentiment analysis has gained its importance in recent years. People had improved their way of expressing their opinions about products, services, celebrities, and current topics in internet portals, blogs and social networks. The social network websites like Face book, Twitter, WhatsApp, LinkedIn and Hike messenger, providing the users to express their feelings by using the different symbols like smiley’s, funny faces, etc., These social media websites provide a platform to display peoples’ opinions on topics like movies, products, fashion trends, politics, technologies were expressed. The E-Commerce portals like Amazon, Flip Kart, Snap deal etc., help the people to express their opinions on products. A framework is proposed in this work to find the scores of the opinions and derive conclusions. The classification of opinions is called opinion mining, whereas deriving the scores for those opinions are called sentiment analysis. Here the Classification techniques are used for opinion mining and the scores to those opinions are given by taking a scale from –5 to +5.In this work, a movie review data set has been collected from the twitter reviews (http://ai.stanford.edu/~amaas/data/sentiment/) between the years 2003 and 2012. The Word net lexicon dictionary is used to compare the emotions for obtaining the score. In this paper, the proposed improved RBF kernel of SVM-performed with 98.8% of accuracy when compared with the existing SVM-RBF classifier and other models.

Classification of tweets data based on polarity using improved RBF kernel of SVM

Online reviews about the purchase of products or services provided have become the main source of users’ opinions. In order to gain profit or fame, usually spam reviews are written to promote or demote a few target products or services. This practice is known as review spamming. In the past few years, a variety of methods have been suggested in order to solve the issue of spam reviews. In this study, the researchers carry out a comprehensive review of existing studies on spam review detection using the Systematic Literature Review (SLR) approach. Overall, 76 existing studies are reviewed and analyzed. The researchers evaluated the studies based on how features are extracted from review datasets and different methods and techniques that are employed to solve the review spam detection problem. Moreover, this study analyzes different metrics that are used for the evaluation of the review spam detection methods. This literature review identified two major feature extraction techniques and two different approaches to review spam detection. In addition, this study has identified different performance metrics that are commonly used to evaluate the accuracy of the review spam detection models. Lastly, this work presents an overall discussion about different feature extraction approaches from review datasets, the proposed taxonomy of spam review detection approaches, evaluation measures, and publicly available review datasets. Research gaps and future directions in the domain of spam review detection are also presented. This research identified that success factors of any review spam detection method have interdependencies. The feature’s extraction depends upon the review dataset, and the accuracy of review spam detection methods is dependent upon the selection of the feature engineering approach. Therefore, for the successful implementation of the spam review detection model and to achieve better accuracy, these factors are required to be considered in accordance with each other. To the best of the researchers’ knowledge, this is the first comprehensive review of existing studies in the domain of spam review detection using SLR process.

Spam Review Detection Techniques: A Systematic Literature Review

A comparative assessment of sentiment analysis and star ratings for consumer reviews

The opinion mining is very much essential in e-commerce websites, furthermore advantageous with individual. An ever increasing amount of results are stored in the web as well as the amount of people would acquiring items from web are increasing. As a result, the users' reviews or posts are increasing day by day. The reviews toward shipper sites express their feeling. Any organization for example, web forums, discourse groups, blogs etc., there will be an extensive add up for information. Records identified with items on the Web, which are functional to both makers and clients. The process of finding user opinion about the topic or product or problem is called as opinion mining. It can also be defined as the process of automatic extraction of knowledge by means of opinions expressed by the user who is currently using the product about some product is called as opinion mining. Analyzing the emotions from the extracted opinions is defined as Sentiment Analysis. The goal of opinion mining and Sentiment Analysis is to make computer able to recognize and express emotion. This work concentrates on mining reviews from the websites like Amazon, which allows user to freely write the view. It automatically extracts the reviews from the website. It also uses algorithm such as Naive Bayes classifier, Logistic Regression and SentiWordNet algorithm to classify the review as positive and negative review. At the end we have used quality metric parameters to measure the performance of each algorithm.

Opinion mining and sentiment analysis on online customer review

Opinion mining and sentiment analysis is rapidly growing area. There are numerous e-commerce sites available on internet which provides options to users to give feedback about specific product. These feedbacks are very much helpful to both the individuals, who are willing to buy that product and the organizations. An accurate method for predicting sentiments could enable us, to extract opinions from the internet and predict customer‟s preferences. There are various algorithms available for opinion mining. Before applying any algorithm for polarity detection, pre-processing on feedback is carried out. From these pre-processed reviews opinion words and object on which opinion is generated are extracted and any opinion mining technique is applied to find the polarity of the review. Opinion mining has three levels of granularities: Document level, Sentence level and Aspect level. In this paper various algorithms for sentiment analysis are studied and challenges and applications appear in this field are discussed.

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A Survey on Sentiment Analysis Algorithms for Opinion Mining

Privacy Preserving Data Mining (PPDM) is used to mine the potential valuable knowledge without revealing the personal information of the individuals. Now a days due to data privacy has become a major concern as quasi and sensitive attribute in terms of privacy needs a lot of research existing researches on privacy of quasi and sensitive attribute are time consuming and also are not space efficient so by this research to trying different techniques used in various papers for establish that which method is more convenient. It is often highly valuable for organizations to have their data analyzed by external agents.. In the era of information society, sharing and publishing data has been a common practice for their wealth of opportunities. However, the process of data collection and data distribution may lead to disclosure of their privacy. Privacy is necessary to conceal private information before it is shared, exchanged or published. PPDM has thus has received a significant amount of attention in the research literature in the recent years. Various methods have been proposed to achieve the expected goal. In this paper we have given a brief discussion on different dimensions of classification of privacy preservation techniques.

Jay Vala

Papers

A Survey on Sentiment Analysis Algorithms for Opinion Mining

A Review on ‘Privacy Preservation Data Mining’