AspectFrameNet: a frameNet extension for analysis of sentiments around product aspects

Chatterji, Sanjay; Varshney, Nitish; Rahul, Ranjan Kumar

doi:10.1007/s11227-016-1808-6

Cited by 14 publications

(11 citation statements)

References 11 publications

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“…Classification is the second technique that uses a classifier to identify implicit aspects, for example, the Naïve Bayes (NB) classifier (Xu et al, 2020). The third one is CRF based on a sequential labeling technique (Chatterji et al, 2017). The last is deep learning techniques containing CNN (Feng et al, 2019) and LTSM (Ahmed et al, 2019).…”

Section: Review Methodologymentioning

confidence: 99%

Review of sentiment analysis: An emotional product development view

Yan

2022

Front. Eng. Manag.

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Conveying consumers’ specific emotions in new products, referred to as emotional product development or emotional design, is strategically crucial for manufacturers. Given that sentiment analysis (SA) can extract and analyze people’s opinions, sentiments, attitudes, and perceptions regarding different products/services, SA-based emotional design may provide manufacturers with real-time, direct, and rapid decision support. Despite its considerable advancements and numerous survey and review articles, SA is seldom considered in emotional design. This study is among the first efforts to conduct a thorough review of SA from the view of emotional design. The comprehensive review of aspect-level SA reveals the following: 1) All studies focus on extracting product features by mixing technical product features and consumers’ emotional perceptions. Consequently, such studies cannot capture the relationships between technical and emotional attributes and thus cannot convey specific emotions to the new products. 2) Most studies use the English language in SA, but other languages have recently received more interest in SA. Furthermore, after conceptualizing emotion as Kansei and introducing emotional product development and Kansei Engineering, a review of the data-driven emotional design is then conducted. A few efforts start to study emotional design with the help of SA. However, these studies only focus on either analyzing consumers’ preferences on product features or extracting emotional opinions from online reviews, thus cannot realize data-driven emotional product development. Finally, some research opportunities are provided. This study opens a broad door to aspect-level SA and its integration with emotional product development.

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Section: Review Methodologymentioning

confidence: 99%

Review of sentiment analysis: An emotional product development view

Yan

2022

Front. Eng. Manag.

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“…While aspect category detection can handle the implicit aspects and explicit aspects, it is much harder to extract implicit aspects with aspect term extraction. Sequential supervised models [23], [24] are better than language rule models to extract implicit aspects. These models' problem is that they need lots of labeled data, which is not easy to get for each area and domain separately.…”

Section: Background Of Studymentioning

confidence: 99%

A Similarity Score Model for Aspect Category Detection

Madhoushi¹,

Hamdan²,

Zainudin³

2021

IJACSA

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Aspect-based Sentiment Analysis (ABSA) aims to extract significant aspects of an item or product from reviews and predict the sentiment of each aspect. Previous similarity methods tend to extract aspect categories at the word level by combining Language Models (LM) in their models. A drawback for the LM model is its dependence on a large amount of labelled data for a specific domain to function well. This work proposes a mechanism to address labelled data dependency by a one-step approach experimenting to decide the best combinatory architectures of recurrent-based LM and the best semantic similarity measures for fostering a new aspect category detection model. The proposed model addresses drawbacks of previous aspect category detection models in an implicit manner. The datasets of this study, S1 and S2, are from standard SemEval online competition. The proposed model outperforms the previous baseline models in terms of the F1-score of aspect category detection. This study finds more relevant aspect categories by creating a more stable and robust model. The F1 score of our best model for aspect category detection is 79.03% in the restaurant domain for the S1 dataset. In dataset S2, the F1score is 72.65% in the laptop domain and 75.11% in the restaurant domain.

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“…In another development, [113,114] employs a CRF-based technique for both implicit and explicit aspect identification. The findings of both approaches are significantly impactful but suffer some drawbacks, in which the former is unable to recognize many aspect features and the latter is only limited to Basque and Catalan languages.…”

Section: Conditional Random Field (Crf)mentioning

confidence: 99%

A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

et al. 2020

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Aspect-based sentiment analysis (ABSA) is currently among the most vigorous areas in natural language processing (NLP). Individuals, private and government institutions are increasingly using media sources for decision making. In the last decade, aspect extraction has been the most essential phase of sentiment analysis (SA) to conduct an abridged sentiment classification. However, previous studies on sentiment analysis mostly focused on explicit aspects extraction with limited work on implicit aspects. To the best of our knowledge, this is the first systematic review that covers implicit, explicit, and the combination of both implicit and explicit aspect extractions. Therefore, this systematic review has been conducted to, 1) identify techniques used for extracting implicit, explicit, or both implicit and explicit aspects; 2) analyze the various evaluation metrics, data domains, and languages involved in the implicit and explicit aspect extraction in sentiment analysis from years 2008 to 2019; 3) identify the key challenges associated with the techniques based on the result of a comprehensive comparative analysis; and finally, 4) highlight the feasible opportunities for future research directions. This review can be used to assist novice and prominent researchers to understand the concept of both implicit and explicit aspect extractions in aspect-based sentiment analysis domain. INDEX TERMS aspect-based sentiment analysis, aspect extraction, explicit aspect, feature extraction, implicit aspect.

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AspectFrameNet: a frameNet extension for analysis of sentiments around product aspects

Cited by 14 publications

References 11 publications

Review of sentiment analysis: An emotional product development view

Review of sentiment analysis: An emotional product development view

A Similarity Score Model for Aspect Category Detection

A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

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