Opinion mining technique for developing student feedback analysis system using lexicon-based approach (OMFeedback)

2019 ◽  
Vol 25 (4) ◽  
pp. 2549-2560 ◽  
Author(s):  
Muslihah Wook ◽  
Noor Afiza Mat Razali ◽  
Suzaimah Ramli ◽  
Norshahriah Abdul Wahab ◽  
Nor Asiakin Hasbullah ◽  
...  
Keyword(s):  
Opinion Mining ◽  
Student Feedback ◽  
Feedback Analysis ◽  
Mining Technique ◽  
Analysis System

scholarly journals Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

2019 ◽  
Author(s):  
Muhammad Zubair Asghar ◽  
Ikram Ullah ◽  
Shahab Shamshirband ◽  
Fazal Masud Kundi ◽  
Ammara Habib
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Student Feedback ◽  
Feedback Analysis ◽  
Fine Grained ◽  
Online Social Media ◽  
Logic Module ◽  
Data Collection And Analysis ◽  
Analysis System ◽  
Sentiment Score

The feedback collection and analysis has remained an important subject matter since long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level. The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.

scholarly journals Feature extraction and analysis of online reviews for the recommendation of books using opinion mining technique

2016 ◽  
Vol 8 ◽  
pp. 754-756 ◽  
Author(s):  
Shahab Saquib Sohail ◽  
Jamshed Siddiqui ◽  
Rashid Ali
Keyword(s):  
Feature Extraction ◽  
Opinion Mining ◽  
Online Reviews ◽  
Mining Technique

Interactive E-Textbook Platform Based on Block Editing Model in Crowdsourcing E-Learning Environments

2021 ◽  
Vol 17 (1) ◽  
pp. 50-66
Author(s):  
Ja-Ryound Choi ◽  
Soon-Bum Lim
Keyword(s):  
Programming Languages ◽  
Learning Environments ◽  
Visual Programming ◽  
Student Feedback ◽  
Feedback Information ◽  
Feedback Analysis ◽  
Digital Textbooks ◽  
E Learning ◽  
Visual Programming Language

Instructors can now work with students to create various textbooks based on crowdsourcing. In particular, as feedback provided by students is essential for determining the quality and direction of classes, instructors should interact with students who are currently participating in classes by exchanging feedback. This paper proposes a block editing model that can reflect student feedback. The block editing model is an interactive e-textbook editing model that is dynamically updated based on the feedback provided by students in real time without modifying the structure of digital textbooks. In particular, in order for even non-developers who do not know web programming languages to be able to produce interactive digital textbooks easily, the authors developed an editor that could help implement them based on Blockly, a visual programming language. This paper enables instructors to improve the direction and quality of classes depending on the learning achievement of students and understanding based on feedback information provided by students and feedback analysis.

An Opinion Mining Approach for Drug Reviews in Spanish

2021 ◽  
pp. 445-480
Author(s):  
Karina Castro-Pérez ◽  
José Luis Sánchez-Cervantes ◽  
María del Pilar Salas-Zárate ◽  
Maritza Bustos-López ◽  
Lisbeth Rodríguez-Mazahua
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Adverse Drug Events ◽  
Hybrid Approach ◽  
Unstructured Data ◽  
Degenerative Diseases ◽  
Analysis System ◽  
Essential Contribution ◽  
Processing Techniques ◽  
Manual Review

In recent years, the application of opinion mining has increased as a boom and growth of social media and blogs on the web, and these sources generate a large volume of unstructured data; therefore, a manual review is not feasible. For this reason, it has become necessary to apply web scraping and opinion mining techniques, two primary processes that help to obtain and summarize the data. Opinion mining, among its various areas of application, stands out for its essential contribution in the context of healthcare, especially for pharmacovigilance, because it allows finding adverse drug events omitted by the pharmaceutical companies. This chapter proposes a hybrid approach that uses semantics and machine learning for an opinion mining-analysis system by applying natural-language-processing techniques for the detection of drug polarity for chronic-degenerative diseases, available in blogs and specialized websites in the Spanish language.

A hybrid recommender system for the mining of consumer preferences from their reviews

2019 ◽  
Vol 46 (5) ◽  
pp. 664-682
Author(s):  
Li Chen Cheng ◽  
Ming-Chan Lin
Keyword(s):  
Opinion Mining ◽  
Information Overload ◽  
Consumer Preferences ◽  
Online Reviews ◽  
User Generated Content ◽  
Mining Technique ◽  
Movie Recommendation ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Filtering Approach

Product review sites are widespread on the Internet and are rapidly gaining in popularity among consumers. This already large volume of user-generated content is dramatically growing every day, making it hard for consumers to filter out the worthwhile information which appears on the various review sites. There commendation system plays a significant role in solving the problem of information overload. This study proposes a framework which integrates a collaborative filtering approach and an opinion mining technique for movie recommendation. Within the proposed framework, sentiment analysis is first applied to the users’ reviews to detect consumer opinions about the movie they have watched and to explore the individual’s preference profile. Traditional recommendation models are overly dependent on preference ratings and often suffer from the problem of ‘data sparsity’. Experimental results obtained from real online reviews show that our proposed method is effective in dealing with insufficient data and is more accurate and efficient than existing traditional methods.

scholarly journals An adaptive sentimental analysis using ontology for retail market

2017 ◽  
Vol 7 (1.2) ◽  
pp. 176
Author(s):  
J Mannar Mannan ◽  
Jayavel J
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Domain Knowledge ◽  
Opinion Mining ◽  
Identification System ◽  
Present System ◽  
3D Space ◽  
User Data ◽  
New Type ◽  
Analysis System

The growth of digital documents on web becomes the massive sources for online market analyzing at broad level. The study of market research over online incorporating new parameter called sentiment analysis.  The sentiment analysis plays a crucial role for identifying behavior of customers by means of natural language processing from customer feedback about product or services.  The opinion mining have done from the user data over web related activities such as search history, blog activities, forums, comments on the social network, express the opinion about the concept/product and suggestion or recommendations. The present system is non-adaptive relation identification system works on existing, predetermined set of relations and it cannot identify the new type relation for opinion mining. The existing system are also neglected the static sentiments of users. This paper proposed ontology based adaptive sentiment analysis system for extracting new features added on the user space. In our work, the ontology and 3D space clustering framework which allows incorporation of domain knowledge for predicting sentimental analysis via opinion mining.

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