Aspect based opinion mining on restaurant reviews

Perera, I. K. C. U.; Caldera, H. A.

doi:10.1109/ciapp.2017.8167276

Cited by 22 publications

(5 citation statements)

References 3 publications

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“…Step 3 -sentiment analysis Sentiment analysis, also known as opinion mining, is a method used to understand the sentiment of social media users. The sentiment analysis shows if the respondent has a positive, negative or neutral opinion about a certain argument (Dub e et al, 2018;Perera and Caldera, 2017). It allows businesses to collect, monitor and analyse user-generated data of their corporate social media sites and of their competitors.…”

Section: 3mentioning

confidence: 99%

“…A sentiment analysis was carried out to assess social media users' perception and possible polarization of opinions (Ravaglia et al, 2016). Because tweets' content is often informal, poorly structured in terms of syntax and short to respect length limits set by Twitter rules, the literature identifies the lexical approach as the most suitable to implement sentiment analysis (Boiy and Moens, 2009;Mostafa, 2017;Perera and Caldera, 2017).…”

Section: Sentiment Analysis Applicationmentioning

confidence: 99%

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Social media exploration for understanding food product attributes perception: the case of coffee and health with Twitter data

Samoggia

Riedel

Ruggeri

2020

BFJ

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PurposeFood companies and consumers are increasingly interested in healthy food and beverages. Coffee is one of the most commonly consumed beverages worldwide. There is increasing consensus that coffee consumption can have beneficial effects on human body. This paper aims at exploring Twitter messages' content and sentiment towards health attributes of coffee.Design/methodology/approachThe research adopted a utilitarian and hedonic consumer behaviour perspective to analyse online community messages. A sample of 13,000 tweets, from around 4,800 users, that mentions keywords coffee and health was collected on a daily basis for a month in mid-2017. The tweets were categorized with a term frequency analysis, keyword-in-context analysis and sentiment analysis.FindingsResults showed that the majority of tweets are neutral or slightly positive towards coffee’s effects on health. Media and consumers are dynamic Twitter users. Findings support that coffee consumption brings favourable emotions, wellness, energy, positive state of mind and an enjoyable and trendy lifestyle. Many tweets have a positive perception of coffee health benefits, especially relating to mental and physical well-being.Research limitations/implicationsThe high number of users and tweets analysed compensates the limited amount of time of data collection, Twitter messages' restricted number of characters and quantitative software analysis limitations.Practical implicationsThe research provides valuable suggestions for food and beverage industry managers.Originality/valueThis work adds value to the literature by expanding scholars' research on food product attributes perception analysis by using social media as a source of information. Moreover, it provides valuable information on marketable coffee attributes.

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Section: 3mentioning

confidence: 99%

Section: Sentiment Analysis Applicationmentioning

confidence: 99%

Social media exploration for understanding food product attributes perception: the case of coffee and health with Twitter data

Samoggia

Riedel

Ruggeri

2020

BFJ

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“…Studies like [4] and [5] classified public opinions into negative and positive, based on Amazon reviews and Twitter comments, respectively. Sentiment analysis conducted by [6] and [7] studied restaurant and hotel reviews to generate personalized review recommendations. [8] analyzed product reviews to acquire valuable information for marketing analysis.…”

Section: Related Work a Opinion Mining In Social Media 1) Overviewmentioning

confidence: 99%

Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data

Amangeldi,

Usmanova,

Shamoi

2024

IEEE Access

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Social media is now the predominant source of information due to the availability of immediate public response. As a result, social media data has become a valuable resource for comprehending public sentiments.Studies have shown that it can amplify ideas and influence public sentiments. This study analyzes the public perception of climate change and the environment over a decade from 2014 to 2023. Using the Pointwise Mutual Information (PMI) algorithm, we identify sentiment and explore prevailing emotions expressed within environmental tweets across various social media platforms, namely Twitter, Reddit, and YouTube. Accuracy on a human-annotated dataset was 0.65, higher than Vader's score but lower than that of an expert rater (0.90). Our findings suggest that negative environmental tweets are far more common than positive or neutral ones. Climate change, air quality, emissions, plastic, and recycling are the most discussed topics on all social media platforms, highlighting its huge global concern. The most common emotions in environmental tweets are fear, trust, and anticipation, demonstrating the wide and complex nature of public reactions. By identifying patterns and trends in opinions related to the environment, we hope to provide insights that can help raise awareness regarding environmental issues, inform the development of interventions, and adapt further actions to meet environmental challenges.

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“…To address this, ABSA, which can identify the sentiments of each aspect of online reviews, was proposed (Mowlaei et al, 2020; Nazir et al, 2020). Perera and Caldera (2017) conducted a study to validate the ABSA method using online reviews of restaurants. They identified the primary aspects of restaurants, such as location, food, service, timing, and staff.…”

Section: Related Workmentioning

confidence: 99%

AXCF: Aspect‐based collaborative filtering for explainable recommendations

Kim,

Li,

Jang

et al. 2024

Expert Systems

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With the rapid growth of the e‐commerce market facilitated, users are often overwhelmed by the excessive online information, making item selection challenging. While recommendation services have significantly enhanced user experience and sales, these traditional models often overlook the complexity of user‐item interactions and user preferences based on various item aspects. The proposed AXCF framework innovatively combines graph‐based collaborative filtering (CF), which captures high‐order connectivity between users and items, with aspect‐based sentiment analysis (ABSA) to extract detailed user preferences from online reviews. This approach addresses the limitations of linear relationships in traditional CF models by incorporating deep neural networks and introduces a method to overcome the cold‐start problem using online reviews as auxiliary information. By focusing on main aspects such as food, ambiance, and service derived from restaurant reviews, AXCF provides personalized recommendations with improved accuracy and explanatory power, demonstrating its superiority over existing models through experimental results. This study not only presents a significant advancement in recommender systems but also highlights the importance of high‐order connectivity and aspect‐based preferences in understanding and catering to user needs in the e‐commerce platform.

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Aspect based opinion mining on restaurant reviews

Cited by 22 publications

References 3 publications

Social media exploration for understanding food product attributes perception: the case of coffee and health with Twitter data

Social media exploration for understanding food product attributes perception: the case of coffee and health with Twitter data

Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data

AXCF: Aspect‐based collaborative filtering for explainable recommendations

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