Research Directions, Challenges and Issues in Opinion Mining

Rapid growth of Internet and availability of user reviews on the web for any product has provided a need for an effective system to analyze the web reviews. Such reviews are useful to some extent, promising both the customers and product manufacturers. For any popular product, the number of reviews can be in hundreds or even thousands. This creates difficulty for a customer to analyze them and make important decisions on whether to purchase the product or to not. Mining such product reviews or opinions is termed as opinion mining which is broadly classified into two main categories namely facts and opinions. Though there are several approaches for opinion mining, there remains a challenge to decide on the recommendation provided by the system. In this paper, we analyze the basics of opinion mining, challenges, pros & cons of past opinion mining systems and provide some directions for the future research work, focusing on the challenges and issues

Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

Funding Information: Funding Statement: This work was funded by the Researchers Supporting Project Number (RSP2023R 509) King Saud University, Riyadh, Saudi Arabia. This work was supported in part by the Higher Education Sprout Project from the Ministry of Education (MOE) and National Science and Technology Council, Taiwan, (109-2628-E-224-001-MY3), and in part by Isuzu Optics Corporation. Dr. Shih-Yu Chen is the corresponding author. Publisher Copyright: © 2023 Tech Science Press. All rights reserved.Peer reviewedPublisher PD

INTELLIGENT E-MAIL PERSONALIZATION SYSTEM

In Internet era E-mail has become the most important mode of communication in every day life. E-mail offers several advantages like secure delivery, speed, cheaper cost, acknowledgement report, transparent service, and distributed environment. As spammers try to induce large amount of spam or unsolicited mails, managing these E-mails’s in an efficient manner requires huge attention. This paper focus on personalizing the E-mail messages after eliminating the spam messages. The basic step starts with pre-processing the documents and classifying the contents into several folders or categories. The next step is to cluster the documents based on the relativeness they have using cosine similarity metric. This clustering approach is carried out using unsupervised method. The mail messages are the parsed through a filter that would identify the spam immediately. Studies on personalization of mails after spam identification, prioritizing the E-mail’s based on the importance and summarization of were also proposed. The results were quiet promising leading to efficient spam identification providing a platform for further improvements to build a domain independent personalizer system

A Comparison of Similarity Measures for Text Documents

Similarity is an important and widely used concept in many applications such as Document Summarisation, Question Answering, Information Retrieval, Document Clustering and Categorisation. This paper presents a comparison of various similarity measures in comparing the content of text documents. We have attempted to find the best measure suited for finding the document similarity for newspaper reports.Stop words, stemming, normalisation, similarity measure, discriminant

Classifying product reviews from balanced datasets for Sentiment Analysis and Opinion Mining

The Online reviews provided for a product enables web user to make decisions appropriately. These reviews may be positive, negative or neutral in nature. Analyzing and classifying such product reviews have attracted reasonable interest. It has become quite hard to make decisions since we aren’t able to obtain the decisions quickly. Hence it is required to classify the reviews from balanced data sets for analysis and opinion mining of any applications. The reason for considering balanced data sets is that the decision will not be biased on the category of reviews considered. We have carried out investigations using similarity measures to categorize the reviews correctly. Experiments reveal that the reviews that were mixed in nature were able to be grouped correctly