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With the fast growth of World Wide Web 2.0, a great number of opinions about a variety of products have been published on blogs, forums, and social networks. Online opinions play an important role in supporting consumers make decisions about purchasing products or services. In addition, customer reviews allow companies to understand the strengths and limitations of their products and services, which aids in improving their marketing campaigns. The challenge is that online opinions are predominantly expressed in natural language text, and hence opinion mining tools are required to facilitate the effective analysis of opinions from the unstructured text and to allow for qualitative information extraction. This research presents a Hybrid Semantic Knowledgebase-Machine Learning approach for mining opinions at the domain feature level and classifying the overall opinion on a multi-point scale. The proposed approach benefits from the advantages of deploying a novel Semantic Knowledgebase .
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Data & Knowledge Engineering
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Expert Systems with Applications
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Trends and Perspectives
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2016 UKSim-AMSS 18th International Conference on Computer Modelling and Simulation (UKSim)
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Asia-Pacific Journal of Information Technology & Multimedia
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International Journal for Research in Applied Science & Engineering Technology (IJRASET)
Nowadays there is huge growth in data People post their views and opinions through the web on different apps, blogs, articles, etc. Customers post their reviews on shopping sites about the product or service. So, it becomes beneficial for companies, manufactures, business owners and sellers to understand customers, product users or buyers but due to huge data/feedbacks or posted opinions manually analyzing text data, is impossible to do. So, opinion mining is very important so as to analyze all the data and know the sentiments from that data without much human effort and in less time huge data can be analyzed. Many researches have made the base in this field of opinion mining. Here opinion mining will be discussed starting with what is opinion mining, how opinion mining is performed, levels, types and approaches for opinion mining, and applications. Also, methods for Text Preprocessing, Feature Extraction, Evaluation and Classification Approaches that are Machine Learning approaches and Lexicon Based approaches also, various opinion mining methods such as Support Vector machines (SVM), Neural Network, Naïve Bayes, Bayesian Network, Maximum Entropy, Corpus and Dictionary based methods are discussed here.
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