Abstract
Opinion Mining or Sentiment Analysis is a task in the processing of natural language to find the customers' mood about buying a specific product or subject. It involves developing a framework in many online shopping sites to gather and review opinions about the product made. Opinion mining is a sub-field of the mining of web content. Data mining is a branch of Web content mining. Opinions are statements that reflect the opinion or sentiment of individuals. Opinion on objects or events is also given in this statement. For any person, reviewing consumer review is more relevant in making the right buying product and organization decision. CS is the best search algorithm inspired by cuckoos' breeding behavior. It provides a short overview of the nature-inspired algorithm's applications. The CS algorithm is used in various fields, such as business, image processing, wireless sensor networks, flood forecasting, document clustering, speaker recognition, distributed system shortest path, health sector, job scheduling. In terms of better efficiency and less processing time, the Cuckoo algorithm performs various nature-inspired algorithms. Therefore, this research paper proposes a hybrid feature selection which is a combination of cuckoo search and mRMR (Minimum Redundancy Maximum Relevance) algorithm. Due to the subjective nature of social media reviews, hybrid feature selection technique outperforms the traditional technique. The performance factors like f-measure, recall, precision, and accuracy tested on Amazon dataset using Support Vector Machine (SVM) classifier.
Highlights
As we all know very well, e-commerce websites are becoming increasingly popular all over the world
The performance factors like f-measure, recall, precision, and accuracy tested on twitter dataset using Support Vector Machine (SVM) classifier and compared with convolution neural network
Opinion mining and sentiment analysis, the words that are used interchangeably these days are a field of text data mining that involves extracting opinions from evaluative texts and classifying the polarity of the opinion as positive or negative based on the orientation of the text results after the computational treatment of opinions expressed towards the main features
Summary
As we all know very well, e-commerce websites are becoming increasingly popular all over the world. In order to solve optimization problems related to engineering designs, data mining, machine learning and image processing, metaheuristic algorithms are commonly used. Velasquez et al, extended the Bing Liu’s aspect-based opinion mining technique to apply it to the tourism domain Using this extension, we offer an approach for considering a new alternative to discover consumer preferences about tourism products, hotels and restaurants, using opinions available on the Web as reviews. Results showed that tourism product reviews available on web sites contain valuable information about customer preferences that can be extracted using an aspect-based opinion mining approach. Widyantoro proposed a combination of rule-based and machine learning approach to classify aspect and its sentiment of online marketplace opinions. M-Cuckoo And Svm Classification Algorithm Based Opinion Mining categories collected from Indonesian online marketplace site. The average f-measures for all aspects ranging from 78.9% to 92%
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