A Novel Aspect Based Framework for Tourism Sector with Improvised Aspect and Opinion Mining Algorithm

Bhatnagar, Vishal; Goyal, Mahima; Hussain, Mohammad Anayat

doi:10.4018/ijrsda.2018040106

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Even though neural networks seem to be taking over most of the NLP tasks, some of the more recent ABSA studies (Asghar et al , 2017; Bhatnagar et al , 2018; Chinsha and Joseph, 2015; Cruz et al , 2013; Gupta et al , 2019; Huang et al , 2012; Marrese-Taylor et al , 2014; Samha et al , 2014; Singh et al , 2013; Yang and Cardie, 2013) show that approaches based on NLP, rules and dictionaries can still be very effective.…”

Section: Related Workmentioning

confidence: 99%

Aspect-based sentiment analysis of reviews in the domain of higher education

Nikolić

Grljević

Kovačević

2020

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Purpose Student recruitment and retention are important issues for all higher education institutions. Constant monitoring of student satisfaction levels is therefore crucial. Traditionally, students voice their opinions through official surveys organized by the universities. In addition to that, nowadays, social media and review websites such as “Rate my professors” are rich sources of opinions that should not be ignored. Automated mining of students’ opinions can be realized via aspect-based sentiment analysis (ABSA). ABSA s is a sub-discipline of natural language processing (NLP) that focusses on the identification of sentiments (negative, neutral, positive) and aspects (sentiment targets) in a sentence. The purpose of this paper is to introduce a system for ABSA of free text reviews expressed in student opinion surveys in the Serbian language. Sentiment analysis was carried out at the finest level of text granularity – the level of sentence segment (phrase and clause). Design/methodology/approach The presented system relies on NLP techniques, machine learning models, rules and dictionaries. The corpora collected and annotated for system development and evaluation comprise students’ reviews of teaching staff at the Faculty of Technical Sciences, University of Novi Sad, Serbia, and a corpus of publicly available reviews from the Serbian equivalent of the “Rate my professors” website. Findings The research results indicate that positive sentiment can successfully be identified with the F-measure of 0.83, while negative sentiment can be detected with the F-measure of 0.94. While the F-measure for the aspect’s range is between 0.49 and 0.89, depending on their frequency in the corpus. Furthermore, the authors have concluded that the quality of ABSA depends on the source of the reviews (official students’ surveys vs review websites). Practical implications The system for ABSA presented in this paper could improve the quality of service provided by the Serbian higher education institutions through a more effective search and summary of students’ opinions. For example, a particular educational institution could very easily find out which aspects of their service the students are not satisfied with and to which aspects of their service more attention should be directed. Originality/value To the best of the authors’ knowledge, this is the first study of ABSA carried out at the level of sentence segment for the Serbian language. The methodology and findings presented in this paper provide a much-needed bases for further work on sentiment analysis for the Serbian language that is well under-resourced and under-researched in this area.

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Section: Related Workmentioning

confidence: 99%

Aspect-based sentiment analysis of reviews in the domain of higher education

Nikolić

Grljević

Kovačević

2020

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“…On the other hand, this method has satisfactory accuracy and is suitable for opinion mining in various fields. Some methods have been proposed to mine opinions in particular applications, environments or fields such as Smart City (Puri et al 2018;Mishra et al 2018), Tourism Industry (Bhatnagar et al 2018), Advertisement (Tudoran 2018;Dragoni 2018), Nutrition Industry (Mostafa 2018), Stock Investment (Jeong et al 2018), Economy, Commerce and Marketing (Karami et al 2018;Yun et al 2018;Rathan et al 2018;Narayan et al 2018), Energy (Nuortimo and Härkönen 2018) and Literature Review like Movie Review (Souza et al 2018). In contrast, this paper proposes a new method called OMLML that is usable in various applications and fields.…”

Section: Tablementioning

confidence: 99%

“…In contrast, this paper proposes a new method called OMLML that is usable in various applications and fields. In comparison with methods using just machine learning or lexicon to mine opinions such as Puri et al (2018), Bhatnagar et al (2018), Tudoran (2018), Mostafa (2018), Karami et al (2018), Yun et al (2018), Narayan et al (2018), Nuortimo and Härkönen (2018), Souza et al (2018), Rozi et al (2018), Akhmedova et al (2018), Solanki et al (2019) and Kang et al (2018), the method applied for users' opinions mining in this paper combines a machine learning-based method and a lexicon-based method in order to classify opinions and sentiments with more accuracy. In addition, in this paper to create a model of mining based on machine learning and to improve accuracy and performance of the opinion mining method, an improved neural-fuzzy network is proposed.…”

Section: Tablementioning

confidence: 99%

OMLML: a helpful opinion mining method based on lexicon and machine learning in social networks

Keyvanpour

Zandian

Heidarypanah

2020

Soc. Netw. Anal. Min.

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Identification of users' polarities and mining their opinions in various areas, especially social networks, has become one of the popular and useful research fields. Although opinion mining and analyzing methods based on machine learning or lexicon have been useful, high training cost based on time or memory used, lack of enriched and complete lexicons, high dimensions of feature space and ambiguity in positive or negative detection of some sentences in these methods are examples of their downsides. To cope with these problems, in this paper a helpful method based on lexicon and machine learning called OMLML is proposed by using social networks. The main superiority of the proposed method compared to other methods is addressing these challenges simultaneously. According to the proposed method, the polarity of the opinions toward a target word is first determined using a method based on lexicon and textual features of words and sentences. Next, having mapped feature space into a 3-D vector, opinions are analyzed and classified based on a new machine learning method. The results of quantitative and qualitative experiments show that mapping data into a new space decreases training cost and that the performance of the proposed method than is acceptable particularly from the perspective of accuracy, F-measure and runtime.

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