A Novel Framework for Aspect Knowledgebase Generated Automatically from Social Media Using Pattern Rules

Tran, Tuan Anh; Duangsuwan, Jarunee; Wettayaprasit, Wiphada

doi:10.7494/csci.2021.22.4.4028

Cited by 4 publications

(6 citation statements)

References 34 publications

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“…Table 9 provides a detailed comparison of the performance for each dataset used in this study against the study that attained the highest performance in the past. While Tubishat, Idris & Abushariah (2021) achieved the highest values since publication in 2021, the subsequent study by Tran, Duangsuwan & Wettayaprasit (2021) reported lower results. An important aspect to highlight here is that Tubishat, Idris & Abushariah (2021) employed additional tasks and resources, such as the product manual and optimization techniques, to enhance feature extraction performance.…”

Section: Resultsmentioning

confidence: 87%

“…In this study, the F-measure performance for opinion targets showed significant improvement. In another recent study, Tran, Duangsuwan & Wettayaprasit (2021) utilized two different types of datasets comprising product reviews for electronic and computer products. They proposed an aspect of knowledge-based generation using patterns (AKGPR) and further trimmed the extensive list of extracted features using keywords, Word2Vec, and a similarity threshold.…”

Section: Related Workmentioning

confidence: 99%

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Feature extraction from customer reviews using enhanced rules

Santhiran,

Varathan,

Chiam

2024

PeerJ Computer Science

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Opinion mining is gaining significant research interest, as it directly and indirectly provides a better avenue for understanding customers, their sentiments toward a service or product, and their purchasing decisions. However, extracting every opinion feature from unstructured customer review documents is challenging, especially since these reviews are often written in native languages and contain grammatical and spelling errors. Moreover, existing pattern rules frequently exclude features and opinion words that are not strictly nouns or adjectives. Thus, selecting suitable features when analyzing customer reviews is the key to uncovering their actual expectations. This study aims to enhance the performance of explicit feature extraction from product review documents. To achieve this, an approach that employs sequential pattern rules is proposed to identify and extract features with associated opinions. The improved pattern rules total 41, including 16 new rules introduced in this study and 25 existing pattern rules from previous research. An average calculated from the testing results of five datasets showed that the incorporation of this study’s 16 new rules significantly improved feature extraction precision by 6%, recall by 6% and F-measure value by 5% compared to the contemporary approach. The new set of rules has proven to be effective in extracting features that were previously overlooked, thus achieving its objective of addressing gaps in existing rules. Therefore, this study has successfully enhanced feature extraction results, yielding an average precision of 0.91, an average recall value of 0.88, and an average F-measure of 0.89.

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Section: Resultsmentioning

confidence: 87%

Section: Related Workmentioning

confidence: 99%

Feature extraction from customer reviews using enhanced rules

Santhiran,

Varathan,

Chiam

2024

PeerJ Computer Science

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“…Sentiment analysis, also known as opinion mining, is a technique used to determine opinions, emotions, subjectivity, and attitudes expressed in natural language text [20][21][22]. In the context of stress analysis, sentiment analysis is used to understand the emotions and sentiments in stress-related discussions.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…A post with a negative score is considered a stress post. The average polarity score is mapped to the stress level scales [26], including very low stress (0-20), low stress (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40), moderate stress (41-60), high stress (61-80), and very high stress (81-100). It is worth noting that the average polarity score has an interval of [-1.0, 1.0].…”

Section: Sentiment Analysismentioning

confidence: 99%

Exploring Mental Stress Expressions in Online Communities: A Subreddit Analysis

Tuan,

Nghia,

et al. 2024

J. Hum. Earth Future

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Objectives: This study aims to comprehensively explore trends, sentiments, and visualization of mental stress expressions in online communities, focusing on discussions within subreddits on the social media platform Reddit. Methods/Analysis: Advanced text analysis and statistical techniques are employed to achieve the study’s objectives. The research utilizes natural language processing (NLP) methods, sentiment analysis, and topic modeling to unravel the intricate layers of mental stress expressions found in posts across diverse subreddits. Additionally, engagement metrics, such as Redditors’ scores and the number of comments, are analyzed to discern distinctive information and patterns of interest. Findings: The research sheds light on prevalent trends, sentiments, and themes related to mental stress in online conversations before and after January 2020. The findings provide valuable insights into patterns of exciting topics, shared experiences of stress, coping mechanisms, and the significant role of virtual communities in offering support and understanding. Novelty/Improvement: The novelty lies in applying advanced text analysis techniques, including sentiment analysis with the majority voting method combining different machine learning techniques and topic modeling with semantic networks, to gain a deeper understanding of the dynamics of mental stress expressions in online communities. The research explores current patterns and distinguishes itself by examining temporal variations in stress-related posts and their correlation with engagement metrics, offering an innovative perspective on mental health discussions in the digital age. Doi: 10.28991/HEF-2024-05-02-01 Full Text: PDF

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“…The most common way to evaluate customer satisfaction with a product or service is by analyzing their comments and opinions. This provides insight into their sentiments and feelings [11]- [14]. By analyzing these reviews and comments, we can better understand which convey important feedback and which are unim-Bulletin of Electr Eng & Inf ISSN: 2302-9285 ❒ 2775 portant.…”

Section: Introductionmentioning

confidence: 99%

Towards visual sentiment summary to understand customers’ satisfaction

Thanh Loan,

Tuan

2024

Bulletin EEI

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Due to the COVID-19 pandemic, the shopping behavior of customers has been significantly affected and is being shifted towards online shopping. Understanding the customers’ opinions, attitudes, and emotions in feedback and comments plays an essential role in making decisions for organizations and individuals (e.g., companies and customers). In this study, we propose sentiment summaries from the customer knowledgebase (SSoCK) framework that analyses customer feedback and improve a mechanism for sentiment summarization by using text analysis including sentiment analysis. In the experiments, various domains from customer reviews (e.g., computer and Canon) are used to conduct. The results show that the proposed SSoCK framework has the high performance of sentiment classification in terms of its accuracy when compared to the other approaches. Moreover, the proposed framework generates various kinds of sentiment summaries that can support managers/potential customers understand trending/interesting aspects of the product with customer satisfaction and can be easily updated with new reviews within the same domain without storing any original data.

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A Novel Framework for Aspect Knowledgebase Generated Automatically from Social Media Using Pattern Rules

Cited by 4 publications

References 34 publications

Feature extraction from customer reviews using enhanced rules

Feature extraction from customer reviews using enhanced rules

Exploring Mental Stress Expressions in Online Communities: A Subreddit Analysis

Towards visual sentiment summary to understand customers’ satisfaction

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