Context Based Classification of Reviews Using Association Rule Mining, Fuzzy Logics and Ontology
01 Sep 2017-Bulletin of Electrical Engineering and Informatics (Universitas Ahmad Dahlan)-Vol. 6, Iss: 3, pp 250-255
TL;DR: A new framework called Fuzzy based contextual recommendation system for classification of customer reviews based on the context given by users is proposed and the sentence based classification and fuzzy based context method are used.
Abstract: The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules.
Citations
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TL;DR: This paper develops an Ontology and Context Based Recommendation System (OCBRS) to assess the context of and determine the opinion of the review, and proposes a Neuro-Fuzzy Classification approach using fuzzy rules to extract the contextof the review.
43 citations
TL;DR: A system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus is presented, that achieved the best results in accuracy of 76.585%.
Abstract: Currently, sentiment analysis into positive or negative getting more attention from the researchers. With the rapid development of the internet and social media have made people express their views and opinion publicly. Analyzing the sentiment in people views and opinion impact many fields such as services and productions that companies offer. Movie reviewer needs many processing to be prepared to detect emotion, classify them and achieve high accuracy. The difficulties arise due of the structure and grammar of the language and manage the dictionary. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Propose an innovative formula to compute the polarity score for each word occurring in the text and find it in positive dictionary or negative dictionary we have to remove it from text. After classification, the words are stored in a list that will be used to calculate the accuracy. The results reveal that the system achieved the best results in accuracy of 76.585%.
12 citations
Cites background from "Context Based Classification of Rev..."
...A customary function of sentiment test is the programmed ascertainment of whether a web-based review (of a book, movie or consumer product) encompasses a positive or negative evaluation [3]....
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TL;DR: Comparison of proposed method with existing method show that proposed method results more detailed output in estimating risk of information security threat.
Abstract: Involvement of digital information in almost of enterprise sectors makes information having value that must be protected from information leakage. In order to obtain proper method for protecting sensitive information, enterprise must perform risk analysis of threat. However, enterprises often get limitation in measuring risk related information security threat. Therefore, this paper has goal to give approach for estimating risk by using information value. Techniques for measuring information value in this paper are text mining and Jaccard method. Text mining is used to recognize information pattern based on three classes namely high business impact, medium business impact and low business impact. Furthermore, information is given weight by Jaccard method. The weight represents risk levelof information leakage in enterprise quantitatively. Result of comparative analysis with existing method show that proposed method results more detailed output in estimating risk of information security threat.
7 citations
Cites background from "Context Based Classification of Rev..."
...It can be used in classification function that categorizes sets of string and inputs appropriate word into a category [13]....
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TL;DR: The major goal of the paper is to solve feature extraction problem and opinion classification problem from customers utilized product reviews which extract the feature words and opinion words from product reviews.
Abstract: Currently, customer's product review opinion plays an essential role in deciding the purchasing of the online product. A customer prefers to acquire the opinion of other customers by viewing their opinion during online products' reviews, blogs and social networking sites, etc. The majority of the product reviews including huge words. A few users provide the opinion; it is tough to analysis and understands the meaning of reviews. To improve user fulfillment and shopping experience, it has become a general practice for online sellers to allow their users to review or to communicate opinions of the products that they have sold. The major goal of the paper is to solve feature extraction problem and opinion classification problem from customers utilized product reviews which extract the feature words and opinion words from product reviews. To propose an Efficient Feature Extraction and Classification (EFEC) algorithm is implementing to extracts a feature from opinion words. The reviewer usually marks both positive and negative parts of the reviewed product, despite the fact that their general opinion on the product may be positive or negative. An EFEC algorithm is utilized to predict the number of positive and negative opinion in reviews. Based on Experimental evaluations, proposed algorithm improves accuracy 15.05%, precision 13.7%, recall 15.59% and F-measure 15.07% of the proposed system compared than existing methodologies
5 citations
Cites methods from "Context Based Classification of Rev..."
...In [24] introduced a new framework called Fuzzy based contextual recommendation system for classification of customer reviews....
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TL;DR: The results show that the maximum likelihood and MDL approach result in the same number of optimal topics, and the highest average accuracy is 61% with alpha 0.1 and beta 0.001.
Abstract: Latent Dirichlet Allocation (LDA) is a probability model for grouping hidden topics in documents by the number of predefined topics. If conducted incorrectly, determining the amount of K topics will result in limited word correlation with topics. Too large or too small number of K topics causes inaccuracies in grouping topics in the formation of training models. This study aims to determine the optimal number of corpus topics in the LDA method using the maximum likelihood and Minimum Description Length (MDL) approach. The experimental process uses Indonesian news articles with the number of documents at 25, 50, 90, and 600; in each document, the numbers of words are 3898, 7760, 13005, and 4365. The results show that the maximum likelihood and MDL approach result in the same number of optimal topics. The optimal number of topics is influenced by alpha and beta parameters. In addition, the number of documents does not affect the computation times but the number of words does. Computational times for each of those datasets are 2.9721, 6.49637, 13.2967, and 3.7152 seconds. The optimisation model has resulted in many LDA topics as a classification model. This experiment shows that the highest average accuracy is 61% with alpha 0.1 and beta 0.001.
5 citations
Cites background from "Context Based Classification of Rev..."
...Furthermore, it encourages an increased need to extract the information contained in a document to generate useful knowledge [1], [2], [3], [4]....
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References
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22 Aug 2004
TL;DR: This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.
Abstract: Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.
7,330 citations
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...A method for mining and summarizing the review of the user is proposed in [1]....
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01 Jan 2006
TL;DR: The preliminaries of basic concepts about association rule mining are provided and the list of existing association rulemining techniques are surveyed.
Abstract: In this paper, we provide the preliminaries of basic concepts about association rule mining and survey the list of existing association rule mining techniques. Of course, a single article cannot be a complete review of all the al- gorithms, yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions that have yet to be explored.
485 citations
"Context Based Classification of Rev..." refers background in this paper
...An overview of association rule mining in [6] demonstrates that review comparison on distributed data over the data taxonomies can be efficiently carried out with association rules...
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01 Mar 2011
TL;DR: A novel graphical model is proposed to extract and visualize comparative relations between products from customer reviews, with the interdependencies among relations taken into consideration, to help enterprises discover potential risks and further design new products and marketing strategies.
Abstract: Competitive Intelligence is one of the key factors for enterprise risk management and decision support. However, the functions of Competitive Intelligence are often greatly restricted by the lack of sufficient information sources about the competitors. With the emergence of Web 2.0, the large numbers of customer-generated product reviews often contain information about competitors and have become a new source of mining Competitive Intelligence. In this study, we proposed a novel graphical model to extract and visualize comparative relations between products from customer reviews, with the interdependencies among relations taken into consideration, to help enterprises discover potential risks and further design new products and marketing strategies. Our experiments on a corpus of Amazon customer reviews show that our proposed method can extract comparative relations more accurately than the benchmark methods. Furthermore, this study opens a door to analyzing the rich consumer-generated data for enterprise risk management.
293 citations
"Context Based Classification of Rev..." refers background in this paper
...Personalized summarization [10] is proposed to customize the user's review history....
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TL;DR: This paper proposes an innovative opinion mining methodology that takes advantage of new Semantic Web-guided solutions to enhance the results obtained with traditional natural language processing techniques and sentiment analysis processes and provides a new vector analysis-based method for sentiment analysis.
Abstract: The idiosyncrasy of the Web has, in the last few years, been altered by Web 2.0 technologies and applications and the advent of the so-called Social Web. While users were merely information consumers in the traditional Web, they play a much more active role in the Social Web since they are now also data providers. The mass involved in the process of creating Web content has led many public and private organizations to focus their attention on analyzing this content in order to ascertain the general public’s opinions as regards a number of topics. Given the current Web size and growth rate, automated techniques are essential if practical and scalable solutions are to be obtained. Opinion mining is a highly active research field that comprises natural language processing, computational linguistics and text analysis techniques with the aim of extracting various kinds of added-value and informational elements from users’ opinions. However, current opinion mining approaches are hampered by a number of drawbacks such as the absence of semantic relations between concepts in feature search processes or the lack of advanced mathematical methods in sentiment analysis processes. In this paper we propose an innovative opinion mining methodology that takes advantage of new Semantic Web-guided solutions to enhance the results obtained with traditional natural language processing techniques and sentiment analysis processes. The main goals of the proposed methodology are: (1) to improve feature-based opinion mining by using ontologies at the feature selection stage, and (2) to provide a new vector analysis-based method for sentiment analysis. The methodology has been implemented and thoroughly tested in a real-world movie review-themed scenario, yielding very promising results when compared with other conventional approaches.
166 citations
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...An approach for mining the context is proposed in [5]....
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TL;DR: A novel large-scale, context-aware recommender system that provides accurate recommendations, scalability to a large number of diverse users and items, differential services, and does not suffer from “cold start” problems is proposed.
Abstract: In this paper, we propose a novel large-scale, context-aware recommender system that provides accurate recommendations, scalability to a large number of diverse users and items, differential services, and does not suffer from “cold start” problems. Our proposed recommendation system relies on a novel algorithm which learns online the item preferences of users based on their click behavior, and constructs online item-cluster trees. The recommendations are then made by choosing an item-cluster level and then selecting an item within that cluster as a recommendation for the user. This approach is able to significantly improve the learning speed when the number of users and items is large, while still providing high recommendation accuracy. Each time a user arrives at the website, the system makes a recommendation based on the estimations of item payoffs by exploiting past context arrivals in a neighborhood of the current user's context. It exploits the similarity of contexts to learn how to make better recommendations even when the number and diversity of users and items is large. This also addresses the cold start problem by using the information gained from similar users and items to make recommendations for new users and items. We theoretically prove that the proposed algorithm for item recommendations converges to the optimal item recommendations in the long-run. We also bound the probability of making a suboptimal item recommendation for each user arriving to the system while the system is learning. Experimental results show that our approach outperforms the state-of-the-art algorithms by over 20 percent in terms of click through rates.
89 citations
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...The recommendation system proposed in [12] provides a user based contextual recommendation system based on the user behavior....
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...A context-aware personalized recommendations system in [12] analyzes the user’s context while they access the multimedia information....
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