Feature based opinion mining: A survey

Ganeshbhai, Solanki Yogesh; Shah, Bhumika

doi:10.1109/iadcc.2015.7154839

Cited by 16 publications

(5 citation statements)

References 13 publications

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“…It is also widely researched in data mining, recommendation systems, expert systems, and information retrieval technologies [2]. It has spread from data science to business intelligence [19]. There are mainly three methods or levels of research on sentiment analysis [2].…”

Section: Sentiment Analysismentioning

confidence: 99%

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A sentiment analysis model of Agritech startup on Facebook comments using naive Bayes classifier

Kewsuwun

Kajornkasirat

2022

IJECE

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<span>Facebook page is a tool able to generate perceptions and acceptance, and support people and investors in making business decisions. Moreover, Facebook page plays a part in engaging people in the form of a community. People share experiences and opinions toward products, services, and trends in particular periods on the Facebook page community. Regarding sentiment analysis on Facebook pages, most education and other general topics in English have only been analyzed in English. However, sentiment analysis regarding Agritech startups topics in Thai language has not been done yet. This study analyzes opinions and categorizes positive and negative comments by using naive Bayes classifier to examine the sentiments and attitudes of people and investors. The results could possibly reflect the perception rate of Agritech startups in Thailand and could be applied to explain attentiveness and assess people’s engagement opinions. Furthermore, it could be applied in studying consumer behavior, marketing analysis, spread of information, and attitudes. The study's model is generic and could be applied in other contexts to provide insightful suggestions.</span>

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Section: Sentiment Analysismentioning

confidence: 99%

“…Realizing the importance of opinion, targets also help us understand the sentiment analysis problem better. Another challenge is to classify opinion into two types: regular opinion and comparative opinion [19].…”

Section: Sentiment Analysismentioning

confidence: 99%

A sentiment analysis model of Agritech startup on Facebook comments using naive Bayes classifier

Kewsuwun

Kajornkasirat

2022

IJECE

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“…In this paper, our concern is aspect-based sentiment analysis, which is one of the levels of sentiment classification. It comprises aspect or feature extraction, sentiment polarity prediction and classification, and sentiment aggregation [8,9]. Aspects extraction in sentiment analysis is now becoming an active area of research as it is the most vital task in the aspect-based recognition [4,5].…”

Section: Background and Related Workmentioning

confidence: 99%

Aspect-based sentiment analysis using smart government review data

Alqaryouti

Siyam

Monem

et al. 2020

ACI

101

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Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.

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“…It is a growing research area which mainly focus on knowledge discovery and information retrieval from text using Natural Language Processing (NLP) techniques (Liu, 2012;Ganeshbhai and Shah, 2015;Jamaluddin, 2008). The task of sentiment analysis is to determine the polarity of the opinion in a given text.…”

Section: Introductionmentioning

confidence: 99%

The Generation of Malay Lexicon

Anbananthen¹,

Selvaraju²,

Krishnan³

2017

American Journal of Applied Sciences

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Abstract:The most important element in analyzing sentiment in text is to assign polarity to the opinion words. Polarity means the positive, negative or neutral state of the opinion words. They are many methods or ways in determining the polarity of an opinion words. One of the methods is using lexicon-based method. Lexicons are digital library of opinion words together with the polarity of the words. Basically, there are 3 methods in developing lexicon-based approach which is manual, dictionary-based and corpus-based. For Malay language there is no available sentiment lexicon and also very limited sources. Thus, in this study we present the automation lexicon generation for Malay language using the dictionary approach. The detail description of the automation lexicon generation for Malay language is discussed in this study.

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Feature based opinion mining: A survey

Cited by 16 publications

References 13 publications

A sentiment analysis model of Agritech startup on Facebook comments using naive Bayes classifier

A sentiment analysis model of Agritech startup on Facebook comments using naive Bayes classifier

Aspect-based sentiment analysis using smart government review data

The Generation of Malay Lexicon

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