Item-Based Collaborative Filtering Using Sentiment Analysis of User Reviews

Dubey, Abhishek; Gupta, Ayush; Raturi, Nitish; Saxena, Pranshu

doi:10.1007/978-981-13-2035-4_8

Cited by 26 publications

(13 citation statements)

References 27 publications

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“…To do so, Joint Aspect-Based Sentiment Topic with Maximum Entropy (JABST-ME) classifier is applied. Dubey et al (2018) [25] have suggested an item based collaborative filtering system to overcome the demerits of the conventional collaborative algorithm. The authors have proposed an improved recommender system.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Aspect based Model for Efficient Sentiment Analysis of User Reviews

Priya¹,

Rithika²

2021

CVRJST

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Mining online reviews to extract opinion targets and words are substantial tasks in fine-grained opinion mining. The main aim is to mine sensible multi-grain aspects and opinion words from unlabeled reviews. In this paper, Combined Aspect based Sentiment Model (CASM) is propounded to cooperatively mine multi-grain features and opinions. CASM deals with aspects, opinions, sentiment polarity and granularity concurrently. Support Vector Machine-Radial Basis Function kernel (SVM-RBF) classifier is applied to improve CASM by splitting aspects and opinion words. CASM-SVM-RBF deals with two different kinds of aspects and opinions: general and particular aspects, overall opinions and aspect-precise opinions. Candidates with improved confidence are mined as opinion targets or words. The performance is analyzed in terms of Accuracy, Precision, Recall and F-Score for KNN, BPNN, NBC and ME classifiers.

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Section: Related Workmentioning

confidence: 99%

Intelligent Aspect based Model for Efficient Sentiment Analysis of User Reviews

Priya¹,

Rithika²

2021

CVRJST

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“…It takes a wide range of importance in industry as well as from a study point of view. Sentiment analysis provides measurable study for mining out the knowledge coming from a consumer's opinion, moods, emotions and feelings towards the product and their characteristics [12]. Today the world has become a global village and the use of internet is excessively growing day by day.…”

Section: Literature Surveymentioning

confidence: 99%

“…After collecting the information about the consumer's opinion, we can distinguish what is necessary and what is not. The tracking of opinions, feeling, responses and mood of the customers is known as opinion mining and sentiment analysis [12]. The recent type of text analysis that targets to conclude the opinion and polarity of reviews is referred to as Sentiment Analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Previously, general purpose sentiment analysis of tweets and posts have been carried out [3][4][5][6][7][8][9][10][11][12], however a task-oriented sentiment analysis of users' reviews of a product to find key features liked by the users and measuring their confidence level is a new idea. A challenge in performing a task-oriented sentiment analysis is measuring a customer's loyalty to a specific product on the basis of customers' views about a product.…”

Section: Introductionmentioning

confidence: 99%

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A Fuzzy Logic Based Intelligent System for Measuring Customer Loyalty and Decision Making

2018

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In this paper, an intelligent approach is presented to measure customers’ loyalty to a specific product and assist new customers regarding a product’s key features. Our approach uses an aggregated sentiment score of a set of reviews in a dataset and then uses a fuzzy logic model to measure customer’s loyalty to a product. Our approach uses a novel idea of measuring customer’s loyalty to a product and can assist a new customer to take a decision about a particular product considering its various features and reviews of previous customers. In this study, we use a large sized data set of online reviews of customers from Amazon.com to test the performance of the customer’s reviews. The proposed approach pre-processes the input text via tokenization, Lemmatization and removal of stop words and then applies fuzzy logic approach to take decisions. To find similarity and relevance to a topic, various libraries and API are used in this work such as SentiWordNet, Stanford Core NLP, etc. The approach utilized focuses on identifying polarity of the reviews that may be positive, negative and neutral. To find customer’s loyalty and help in decision making, the fuzzy logic approach is applied using a set of membership functions and rule-based system of fuzzy sets that classify data in various types of loyalty. The implementation of the approach provides high accuracy of 94% of correct loyalty to the e-commerce products that outperforms the previous approaches.

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“…Movie recommendation in general has been an actively researched area in conversational recommendation systems (Bennett et al, 2007;Khatri et al, 2018) and has been explored with a variety of approaches, most popular of which include collaborative filtering (CF) (Katarya and Verma, 2017;He et al, 2019), content-based filtering (Elahi et al, 2017), incorporating user reviews (Zhao et al, 2017;Dubey et al, 2018). Several attempts at both conversational recommendation (Christakopoulou et al, 2016;Sun and Zhang, 2018;Torbati et al, 2021), and specifically conversational movie recommendations (Dalton et al, 2018) have been made.…”

Section: Introductionmentioning

confidence: 99%

You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions

Volokhin¹,

Ho²,

Rokhlenko³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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The increasing popularity of voice-based personal assistants provides new opportunities for conversational recommendation. One particularly interesting area is movie recommendation, which can benefit from an open-ended interaction with the user, through a natural conversation. We explore one promising direction for conversational recommendation: mapping a conversational user, for whom there is limited or no data available, to most similar external reviewers, whose preferences are known, by representing the conversation as a user's interest vector, and adapting collaborative filtering techniques to estimate the current user's preferences for new movies. We call our proposed method ConvExtr (Conversational Collaborative Filtering using External Data), which 1) infers a user's sentiment towards an entity from the conversation context, and 2) transforms the ratings of "similar" external reviewers to predict the current user's preferences. We implement these steps by adapting contextual sentiment prediction techniques, and domain adaptation, respectively. To evaluate our method, we develop and make available a finely annotated dataset of movie recommendation conversations, which we call MovieSent . Our results demonstrate that Con-vExtr can improve the accuracy of predicting users' ratings for new movies by exploiting conversation content and external data.

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Item-Based Collaborative Filtering Using Sentiment Analysis of User Reviews

Cited by 26 publications

References 27 publications

Intelligent Aspect based Model for Efficient Sentiment Analysis of User Reviews

Intelligent Aspect based Model for Efficient Sentiment Analysis of User Reviews

A Fuzzy Logic Based Intelligent System for Measuring Customer Loyalty and Decision Making

You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions

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