Swarm optimization clustering methods for opinion mining

Abstract

I thank my parents, husband, and family for all support and encouragement for the realization of this dream. I thank also my friend, Richard McGill, for the support and review of papers related to this Thesis."Obstacles are those frightful things you see when you take your eyes off your goal."

OMLML: a helpful opinion mining method based on lexicon and machine learning in social networks

“…To classify opinions, six techniques, decision tree, naive Bayes, SVM, k-nearest neighbors (KNN), random forest and logistic regression, have been applied. In Souza et al (2018), a novel algorithm for opinion mining and sentiment analysis of the text reviews posted in Twitter has been proposed based on unsupervised clustering. In this method, a hybrid version of particle swarm optimization (PSO) and Cuckoo Search (CS) has been used.…”

“…On the other hand, this method has satisfactory accuracy and is suitable for opinion mining in various fields. Some methods have been proposed to mine opinions in particular applications, environments or fields such as Smart City (Puri et al 2018;Mishra et al 2018), Tourism Industry (Bhatnagar et al 2018), Advertisement (Tudoran 2018;Dragoni 2018), Nutrition Industry (Mostafa 2018), Stock Investment (Jeong et al 2018), Economy, Commerce and Marketing (Karami et al 2018;Yun et al 2018;Rathan et al 2018;Narayan et al 2018), Energy (Nuortimo and Härkönen 2018) and Literature Review like Movie Review (Souza et al 2018). In contrast, this paper proposes a new method called OMLML that is usable in various applications and fields.…”

“…In contrast, this paper proposes a new method called OMLML that is usable in various applications and fields. In comparison with methods using just machine learning or lexicon to mine opinions such as Puri et al (2018), Bhatnagar et al (2018), Tudoran (2018), Mostafa (2018), Karami et al (2018), Yun et al (2018), Narayan et al (2018), Nuortimo and Härkönen (2018), Souza et al (2018), Rozi et al (2018), Akhmedova et al (2018), Solanki et al (2019) and Kang et al (2018), the method applied for users' opinions mining in this paper combines a machine learning-based method and a lexicon-based method in order to classify opinions and sentiments with more accuracy. In addition, in this paper to create a model of mining based on machine learning and to improve accuracy and performance of the opinion mining method, an improved neural-fuzzy network is proposed.…”

“…Employing a neural-fuzzy network brings the advantages of neural network and fuzzy logic at the same time. Some methods proposed recently have paid attention to only the weight based on word frequency in opinion mining and recognition of emotional concepts of some words like (Puri et al 2018;Mishra et al 2018;Mostafa 2018;Souza et al 2018;Akhmedova et al 2018;Kang et al 2018) compared to OMLML in which three types of weights are used:…”

“…In comparison with some methods that have used the polarity of each word in the opinion or many features to analyze and classify the opinion like (Puri et al 2018;Mishra et al 2018;Mostafa 2018;Jeong et al 2018;Yun et al 2018;Narayan et al 2018;Souza et al 2018;Kang et al 2018), in our method, the polarity of the opinions toward a target word is first determined using a method based on lexicon and textual features of words and sentences. Next, having mapped feature space into a 3-D vector, opinions are analyzed and classified based on a new machine learning method.…”