scholarly journals A Hybrid Approach of Opinion Mining and Comparative Linguistic Analysis of Restaurant Reviews

2021 ◽  
Author(s):  
Salim Sazzed ◽  
Keyword(s):  
Opinion Mining ◽  
Hybrid Approach ◽  
Linguistic Analysis ◽  
Restaurant Reviews

A New Semantic Relations-Based Hybrid Approach for Implicit Aspect Identification in Sentiment Analysis

2020 ◽  
Vol 19 (03) ◽  
pp. 2050019
Author(s):  
Hajar El Hannach ◽  
Mohammed Benkhalifa
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Model ◽  
Opinion Mining ◽  
Short Term Memory ◽  
Hybrid Approach ◽  
Active Phase ◽  
Semantic Relations ◽  
Term Extraction ◽  
Supervised Classifiers ◽  
Restaurant Reviews

Within the next few years, sentiment analysis or opinion mining is set to become an important component of real-world applications for product manufacturers, e-commerce companies, and potential customers. Sentiment analysis deals with the computational assessment of people’s opinions apparent or hidden within the text according to three levels: document, sentence and aspect levels. The aspect-level is increasingly becoming an active phase of sentiment analysis. At this level, the aim is to determine the hidden target of opinion represented in datasets, known as aspect term identification. This paper proposes an original hybrid model combining semantic relations and frequency-based approach with supervised classifiers for implicit aspect identification (IAI). The proposed approach is directed towards improving the F1-performances for traditional supervised classifiers commonly used in this field based on eager and lazy learning, and deep learning technique using long short-term memory whit attention mechanism applied for IAI. Particularly, this work addresses aspect term extraction and aggregation, the two sub-tasks of IAI, involving adjectives and verbs. The effects of this approach are empirically examined on multiple datasets of electronic products and restaurant reviews with multiple aspect granularity levels. Comparing this method with similar approaches clearly shows the benefits of this method: (i) the use of an appropriately selected WordNet semantic relations of adjectives and verbs that significantly helps classifiers for IAI. (ii) Using the hybrid model helps classifiers better handle these selected WordNet semantic relations and therefore deal better with IAI.

scholarly journals Speech for Content Creation

2011 ◽  
Vol 3 (2) ◽  
pp. 35-49
Author(s):  
Joseph Polifroni ◽  
Imre Kiss ◽  
Stephanie Seneff
Keyword(s):  
Language Processing ◽  
Speech Processing ◽  
Opinion Mining ◽  
Prototype System ◽  
Dialogue Systems ◽  
Ancillary Data ◽  
Spoken Dialogue Systems ◽  
Content Creation ◽  
Sentiment Detection ◽  
Restaurant Reviews

This paper proposes a paradigm for using speech to interact with computers, one that complements and extends traditional spoken dialogue systems: speech for content creation. The literature in automatic speech recognition (ASR), natural language processing (NLP), sentiment detection, and opinion mining is surveyed to argue that the time has come to use mobile devices to create content on-the-fly. Recent work in user modelling and recommender systems is examined to support the claim that using speech in this way can result in a useful interface to uniquely personalizable data. A data collection effort recently undertaken to help build a prototype system for spoken restaurant reviews is discussed. This vision critically depends on mobile technology, for enabling the creation of the content and for providing ancillary data to make its processing more relevant to individual users. This type of system can be of use where only limited speech processing is possible.

scholarly journals Design of Hybrid Method for Automatic Sentiment Classification System

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3616-3620
Keyword(s):  
Machine Learning ◽  
Hybrid Method ◽  
Classification System ◽  
Scientific Community ◽  
Opinion Mining ◽  
Hybrid Approach ◽  
Learning Approach ◽  
Business World ◽  
Major Aspect ◽  
Machine Learning Approach

The Developing enthusiasm for the field of opinion mining and its applications in various regions of information and also, sociology has activated numerous researchers to investigate the field The chance to catch the opinion of the overall public about get-togethers, political developments, organization systems, advertising efforts, and item inclinations has raised expanding enthusiasm of both scientific community (as a result of the energizing open difficulties) and the business world (due to the wonderful advantages for promoting and money related market expectation). Today, sentiment analysis investigation has its applications in a few unique situations. There are a decent number of organizations, both huge and little scale, that focuses on opinions and sentiments as a major aspect of their central goal. This work introduces hybrid approach that includes lexicon based approach and machine learning approach for extracting aspects and sentiments

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.

scholarly journals An Automated Real-Time System for Opinion Mining using a Hybrid Approach

2016 ◽  
Vol 8 (7) ◽  
pp. 55-64 ◽  
Author(s):  
Indrajit Mukherjee ◽  
◽  
Jasni M Zain ◽  
P. K. Mahanti
Keyword(s):  
Real Time ◽  
Opinion Mining ◽  
Hybrid Approach ◽  
Time System ◽  
Real Time System

scholarly journals Opinion mining for national security: techniques, domain applications, challenges and research opportunities

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Noor Afiza Mat Razali ◽  
Nur Atiqah Malizan ◽  
Nor Asiakin Hasbullah ◽  
Muslihah Wook ◽  
Norulzahrah Mohd Zainuddin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Theoretical Analysis ◽  
National Security ◽  
Language Processing ◽  
Opinion Mining ◽  
Hybrid Approach ◽  
Hybrid Approaches ◽  
Computing Machinery ◽  
Significant Research ◽  
Research Opportunity

Abstract Background Opinion mining, or sentiment analysis, is a field in Natural Language Processing (NLP). It extracts people’s thoughts, including assessments, attitudes, and emotions toward individuals, topics, and events. The task is technically challenging but incredibly useful. With the explosive growth of the digital platform in cyberspace, such as blogs and social networks, individuals and organisations are increasingly utilising public opinion for their decision-making. In recent years, significant research concerning mining people’s sentiments based on text in cyberspace using opinion mining has been explored. Researchers have applied numerous opinions mining techniques, including machine learning and lexicon-based approach to analyse and classify people’s sentiments based on a text and discuss the existing gap. Thus, it creates a research opportunity for other researchers to investigate and propose improved methods and new domain applications to fill the gap. Methods In this paper, a structured literature review has been done by considering 122 articles to examine all relevant research accomplished in the field of opinion mining application and the suggested Kansei approach to solve the challenges that occur in mining sentiments based on text in cyberspace. Five different platforms database were systematically searched between 2015 and 2021: ACM (Association for Computing Machinery), IEEE (Advancing Technology for Humanity), SCIENCE DIRECT, SpringerLink, and SCOPUS. Results This study analyses various techniques of opinion mining as well as the Kansei approach that will help to enhance techniques in mining people’s sentiment and emotion in cyberspace. Most of the study addressed methods including machine learning, lexicon-based approach, hybrid approach, and Kansei approach in mining the sentiment and emotion based on text. The possible societal impacts of the current opinion mining technique, including machine learning and the Kansei approach, along with major trends and challenges, are highlighted. Conclusion Various applications of opinion mining techniques in mining people’s sentiment and emotion according to the objective of the research, used method, dataset, summarized in this study. This study serves as a theoretical analysis of the opinion mining method complemented by the Kansei approach in classifying people’s sentiments based on text in cyberspace. Kansei approach can measure people’s impressions using artefacts based on senses including sight, feeling and cognition reported precise results for the assessment of human emotion. Therefore, this research suggests that the Kansei approach should be a complementary factor including in the development of a dictionary focusing on emotion in the national security domain. Also, this theoretical analysis will act as a reference to researchers regarding the Kansei approach as one of the techniques to improve hybrid approaches in opinion mining.

A Hybrid Recommender System for Rating Prediction of Arabic Reviews

2020 ◽  
pp. 2050010
Author(s):  
Fouzi Harrag ◽  
Abdulmalik Salman Al-Salman ◽  
Alaa Alquahtani
Keyword(s):  
Sentiment Analysis ◽  
Recommender Systems ◽  
Recommender System ◽  
Opinion Mining ◽  
Good Accuracy ◽  
Hybrid Approach ◽  
Data Sparsity ◽  
Hybrid Recommender System ◽  
Rating Prediction ◽  
Hybrid Recommender

Recommender systems nowadays are playing an important role in the delivery of services and information to users. Sentiment analysis (also known as opinion mining) is the process of determining the attitude of textual opinions, whether they are positive, negative or neutral. Data sparsity is representing a big issue for recommender systems because of the insufficiency of user rating or absence of data about users or items. This research proposed a hybrid approach combining sentiment analysis and recommender systems to tackle the problem of data sparsity problems by predicting the rating of products from users’ reviews using text mining and NLP techniques. This research focuses especially on Arabic reviews, where the model is evaluated using Opinion Corpus for Arabic (OCA) dataset. Our system was efficient, and it showed a good accuracy of nearly 85% in predicting the rating from reviews.

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