Explainable System for Suggestion Mining using attention

Laskari, Naveen Kumar; Sanampudi, Suresh Kumar

doi:10.1109/icaccs54159.2022.9785102

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…The study emphasizes the growing importance of suggestion mining in recommendation and decision-support systems, fueled by the ever-increasing volume of online reviews. It also acknowledges the diverse methods explored in past research, including linguistic techniques, machine learning, deep learning, and pre-trained neural networks, highlighting the success of LSTM networks and CNN models in specific contexts [36].…”

Section: Profound Advantages Of Suggestion Miningmentioning

confidence: 99%

“…Furthermore, this methodology yields valuable insights that can directly translate into product and service enhancements guided by customer sentiment analysis. As a consequence, Laskari and Sanampudi's research contributes significantly to the advancement of sentiment analysis by introducing a more nuanced and informative approach to suggestion mining [33].…”

Section: Profound Advantages Of Suggestion Miningmentioning

confidence: 99%

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Performance Analysis of Suggestion Mining for Mobile Apps

Mali

2024

Preprint

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Extracting actionable improvement suggestions from user reviews remains a critical challenge in mobile app development. We propose two novel Natural Language Processing (NLP) techniques specifically designed to bridge this gap. The first technique leverages a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model, while the second employs a lightweight DistilBERT model. Both are trained on domain-specific action verbs and phrases indicative of desired app changes within the mobile app domain. We evaluate the effectiveness of these techniques through a comprehensive case study analyzing a substantial dataset of 8,061 Mobile app reviews. Our findings demonstrate the efficacy of these techniques in capturing valuable user insights that can inform development efforts and ultimately enhance mobile app quality. This, in turn, has the potential to translate into a more user-centric experience, potentially leading to increased user satisfaction and reduced app churn. Further research could explore advanced analysis of extracted suggestions to prioritize user concerns and inform targeted development initiatives.

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Section: Profound Advantages Of Suggestion Miningmentioning

confidence: 99%

Section: Profound Advantages Of Suggestion Miningmentioning

confidence: 99%

Performance Analysis of Suggestion Mining for Mobile Apps

Mali

2024

Preprint

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“…Recently, Laskari et al [17] presented an explainable system to identify patterns and understand the significant components of suggestions. The experimental results on the standard datasets demonstrate that the attention model can overtake other models.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

Hadizadeh,

Nazari,

Mansoorizadeh

2024

JWE

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In the aftermath of the COVID-19 pandemic, using web platforms as a communication medium and decision-making tool in online commerce has become widely acknowledged. User-generated comments, reflecting positive and negative sentiments towards specific items, serve as invaluable indicators, offering recommendations for product and organizational improvements. Consequently, the extraction of suggestions from mined opinions can enhance the efficacy of companies and organizations in this domain. Prevailing research in suggestion mining predominantly employs rule-based methodologies and statistical classifiers, relying on manually identified features. However, a recent trend has emerged wherein researchers explore solutions grounded in deep learning tools and techniques. This study aims to employ information retrieval techniques for the automated identification of suggestions. To this end, various methodologies, including distance measurement approaches, multilayer perceptron neural networks, support vector machines, regression logistics, convolutional neural networks utilizing TF-IDF, Bag of Words (BOW), and Word2Vec vectors, along with keyword extraction, have been integrated. The proposed approach is assessed using the SemEval2019 dataset to extract suggestions from the textual content of online user reviews. The obtained results demonstrate a notable enhancement in the F1 score, reaching 0.76 compared to prior research. The experiments further suggest that information retrieval-based approaches exhibit promising potential for this specific task.

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“…Recently, Laskari et al [25] presented an explainable system to identify patterns and understand the significant components of suggestions. The experimental results on the standard datasets demonstrate that the attention model can overtake other models.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, online text is becoming a very popular source that can be benefited other users to help to decide correctly and to help organizations to utilize suggestions to improve their performance [1]. In other words, the comments may include helpful suggestions that can help companies better understand user needs and improve products [2,3]. To utilize comments on the web, opinion mining was introduced in 2002.…”

Section: Introductionmentioning

confidence: 99%

Automatic Suggestion Detection from Online User Reviews with Information Retrieval and Deep Learning

Hadizadeh,

Nazari,

Mansoorizadeh

2023

Preprint

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After the outbreak of COVID-19, the use of the web platform as a means of communication between users and decision-making in online shopping has been accepted in e-commerce. Comments of users express substantially positive and negative emotions about a particular item, which offers suggestions for the improvement of products and organizations. Therefore, mining opinions to extract suggestions can increase the utility of companies and organizations. A good deal of research on suggestion mining employs rule-based approaches and statistical classifiers using manually identified features, while recently, several researchers have considered solutions based on deep learning tools and techniques. The purpose of this study is to use information retrieval techniques for the automatic detection of suggestions. Therefore, distance measurement approaches, multilayer perceptron neural networks, support vector machines, regression logistics, convolutional neural networks with TF-IDF, BOW, and Word2Vec vectors, and keyword extraction were adopted. The proposed approach is presented on the SemEval2019 dataset for extracting suggestions from the text of online user reviews. The results show that the F1Score has improved, as compared to the previous work, and has reached 0.87. The experiments indicate that information retrieval-based approaches tend to be promising for this task.

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Explainable System for Suggestion Mining using attention

Cited by 4 publications

References 10 publications

Performance Analysis of Suggestion Mining for Mobile Apps

Performance Analysis of Suggestion Mining for Mobile Apps

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

Automatic Suggestion Detection from Online User Reviews with Information Retrieval and Deep Learning

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