Mining the Web Data: Intelligent Information Retrieval System for Filtering Spam and Sentiment Analysis - Manek, Asha S; Shenoy, P Deepa (2022 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE), 2022)
World Wide Web has great impact on all aspects of our day-to-day activities and people retrieve all kinds of information available on the Web. Numerous informal written documents, such as tweets, SMS messages, emails, and reviews, can be found on online shopping platforms. This extremely large collection of text documents has made the text analysis now popular field of study and encompasses a wide range of activities, such as sentiment analysis, opinion mining, spam identification, and harmful link detection. This work identifies three challenging research problems: spam filtering, malicious web page detection and opinion mining services. Three models RePID-OK, ReP-ETD and RePC-SSMSM are proposed to filter spam. Analysis of opinion is important and crucial for both individuals and companies by eliminating malicious reviews for decision making. To handle and help the Web users and consumers in making decisions, two models SentReP and W-LRSVM are proposed for classification of sentiments for medical related drugs and movie review data set. The model DeMalFier and BLRFier are designed for detecting spam from websites to test whether a website is malicious or legitimate.