A Text Mining and Sentiment Analysis Approach to the Dynamic Changes of Mobile Learning Engagement

2021 ◽  
Vol 09 (05) ◽  
pp. 1231-1238
Author(s):  
巍 肖
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Mobile Learning ◽  
Analysis Approach ◽  
Learning Engagement ◽  
Dynamic Changes

Towards Semantic Aspect-Based Sentiment Analysis for Arabic Reviews

2020 ◽  
Vol 12 (4) ◽  
pp. 1-13
Author(s):  
Salima Behdenna ◽  
Fatiha Barigou ◽  
Ghalem Belalem
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Description Logics ◽  
Arabic Language ◽  
Analysis Approach ◽  
Subjective Information ◽  
Linguistic Rules ◽  
Semantic Aspect

Sentiment analysis is a text mining discipline that aims to identify and extract subjective information. This growing field results in the emergence of three levels of granularity (document, sentence, and aspect). However, both the document and sentence levels do not find what exactly the opinion holder likes and dislikes. Furthermore, most research in this field deals with English texts, and very limited researches are undertaken on Arabic language. In this paper, the authors propose a semantic aspect-based sentiment analysis approach for Arabic reviews. This approach utilizes the semantic of description logics and linguistic rules in the identification of opinion targets and their polarity.

scholarly journals User-Generated Tweets about Global Green Brands: A Sentiment Analysis Approach

2018 ◽  
Vol 30 (2) ◽  
pp. 125-145
Author(s):  
Saba Resnik ◽  
Mateja Kos Koklič
Keyword(s):  
Sentiment Analysis ◽  
Analysis Approach

Sentiment Analysis with Text Mining in Contexts of Big Data

2017 ◽  
Vol 13 (3) ◽  
pp. 47-67 ◽  
Author(s):  
Carina Sofia Andrade ◽  
Maribel Yasmina Santos
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Decision Making Process ◽  
Practical Applications ◽  
Information Models ◽  
Evolution Of Technology ◽  
The Common ◽  
Analyse Data

The evolution of technology, along with the common use of different devices connected to the Internet, provides a vast growth in the volume and variety of data that are daily generated at high velocity, phenomenon commonly denominated as Big Data. Related with this, several Text Mining techniques make possible the extraction of useful insights from that data, benefiting the decision-making process across multiple areas, using the information, models, patterns or tendencies that these techniques are able to identify. With Sentiment Analysis, it is possible to understand which sentiments and opinions are implicit in this data. This paper proposes an architecture for Sentiment Analysis that uses data from the Twitter, which is able to collect, store, process and analyse data on a real-time fashion. To demonstrate its utility, practical applications are developed using real world examples where Sentiment Analysis brings benefits when applied. With the presented demonstration case, it is possible to verify the role of each used technology and the techniques adopted for Sentiment Analysis.

scholarly journals A hybrid sentiment analysis approach using black widow optimization based feature selection

2021 ◽  
Author(s):  
Anand Joseph Daniel ◽  
◽  
M Janaki Meena ◽  
Keyword(s):  
Feature Selection ◽  
Sentiment Analysis ◽  
Performance Metrics ◽  
Computation Time ◽  
Online Reviews ◽  
Reduction Technique ◽  
Feature Reduction ◽  
Analysis Approach ◽  
Black Widow ◽  
Feature Selection Technique

With the massive development of Internet technologies and e-commerce technology, people rely on the product reviews provided by users through web. Sentiment analysis of online reviews has become a mainstream way for businesses on e-commerce platforms to satisfy the customers. This paper proposes a novel hybrid framework with Black Widow Optimization (BWO) based feature reduction technique which combines the merits of both machine learning and lexicon-based approaches to attain better scalability and accuracy. The scalability problem arises due to noisy, irrelevant and unique features present in the extracted features from proposed approach, which can be eliminated by adopting an effective feature reduction technique. In our proposed BWO approach, without changing the accuracy (90%), the feature-set size is reduced up to 43%. The proposed feature selection technique outperforms other commonly used Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) based feature selection techniques with reduced computation time of 21 sec. Moreover, our sentiment analysis approach is analyzed using performance metrics such as precision, recall, F-measure, and computation time. Many organizations can use these online reviews to make well-informed decisions towards the users’ interests and preferences to enhance customer satisfaction, product quality and to find the aspects to improve the products, thereby to generate more profits.

scholarly journals A Proposed Lexicon-Based Sentiment Analysis Approach for the Vernacular Algerian Arabic

2016 ◽  
Vol 110 (1) ◽  
pp. 55-70 ◽  
Author(s):  
M’hamed Mataoui ◽  
Omar Zelmati ◽  
Madiha Boumechache
Keyword(s):  
Sentiment Analysis ◽  
Analysis Approach
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