الفهرس | Only 14 pages are availabe for public view |
Abstract Research Problem and Relevance Recently, with the significant use of the internet in everything in our life from commercial transactions, marketing, e-learning, political, manufacturing, entertainment, and many more of our daily treatments. The opinions of people are now occupying important place in most if not all fields, especially with the increasing the people’s ability to share their opinion in very easy manner any time any place. Thus, the user’s opinion has the ability to make radical changes in several fields in particular with the high speed of the internet to share and publish these opinions. As the outliers could be found in different data formats, it also could be found in the opinionated text. Outlier opinion means that opinion does not comply with the general opinions (unusual pattern). Research Questions This research will be guided by a main research question, which has been formulated as follows: ”How can help the proposed mining outlier opinion model in detection the anomalies from an opinionated comment? ” Research Objectives This research aims to support decision makers in different application domains. The provided support specially helps those domains which extremely depend in their works on the opinionated comments and reviews which are expressed from users or general people. Therefore, the research objective is to detect the outliers from people’s opinionated text to avoid their impact on the sentiment analysis process and what follows this from changes in decision making process. Research Methodology The research methodology (As shown in Figure1.2) can be illustrated in the following points: 1) Reviewing the related literature work in the research areas of opinion mining and anomaly detection. 2) Conducting a comparison study to highlight the points of strengths and weaknesses of related research work. 3) Development of the proposed mining outlier detection model “MOOM”. 4) Applying the proposed MOOM on a benchmark dataset 5) Testing different performance factors of the proposed model through an experimental study. 6) Analyzing the results to evaluate the efficiency of the proposed model. |