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19 results on '"text reviews"'

1. Exploring the advertising elements of electronic word-of-mouth in social media: an example of game reviews.

Author

Mayopu, Richard G., Wang, Yi-Yun, and Chen, Long-Sheng

Subjects
LATENT semantic analysis, NATURAL language processing, CONSUMER behavior, ADVERTISING effectiveness, WORD of mouth advertising
Abstract

The influence of social communities has quietly surpassed traditional media. Electronic word-of-mouth (eWOM) is far greater than in other forms of traditional advertising. More and more enterprises hire key opinion leaders (KOLs) to write product-related comments, hoping to influence the purchasing behavior of other users in the community. In fact, the power of text reviews on social media is more powerful than traditional advertising models. For in-app advertising, it is one of the important issues to understand the focus of ad viewers to improve the effectiveness of advertising and then enhance the click-through rate (CTR) of in-App ads. However, relatively few studies focus on studying what elements should be contained in a successful commercial review on social media. Consequently, this study will treat social media reviews as a kind of new advertising modes and attempt to find the contained elements of ads in these text comments by using natural language processing (NLP), latent semantics analysis (LSA), and matrix diagram techniques. The discovered elements of positive comments (commercial reviews) will be compared to those in negative reviews (true authentic voices of customers). Based on the results, we can provide advertising companies with suggestions when hiring KOLs to write recommendation reviews. [ABSTRACT FROM AUTHOR]

Published
2024
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2. Advancing Semantic Classification: A Comprehensive Examination of Machine Learning Techniques in Analyzing Russian-Language Patient Reviews.

Author

Kalabikhina, Irina, Moshkin, Vadim, Kolotusha, Anton, Kashin, Maksim, Klimenko, German, and Kazbekova, Zarina

Subjects
*MACHINE learning, *ARTIFICIAL neural networks, *CLASSIFICATION algorithms, *NEUROLINGUISTICS, *QUALITY of service, *PHYSICIANS, *TEXT messages, *NATURAL language processing
Abstract

Currently, direct surveys are used less and less to assess satisfaction with the quality of user services. One of the most effective methods to solve this problem is to extract user attitudes from social media texts using natural language text mining. This approach helps to obtain more objective results by increasing the representativeness and independence of the sample of service consumers being studied. The purpose of this article is to improve existing methods and test a method for classifying Russian-language text reviews of patients about the work of medical institutions and doctors, extracted from social media resources. The authors developed a hybrid method for classifying text reviews about the work of medical institutions and tested machine learning methods using various neural network architectures (GRU, LSTM, CNN) to achieve this goal. More than 60,000 reviews posted by patients on the two most popular doctor review sites in Russia were analysed. Main results: (1) the developed classification algorithm is highly efficient—the best result was shown by the GRU-based architecture (val_accuracy = 0.9271); (2) the application of the method of searching for named entities to text messages after their division made it possible to increase the classification efficiency for each of the classifiers based on the use of artificial neural networks. This study has scientific novelty and practical significance in the field of social and demographic research. To improve the quality of classification, in the future, it is planned to expand the semantic division of the review by object of appeal and sentiment and take into account the resulting fragments separately from each other. [ABSTRACT FROM AUTHOR]

Published
2024
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3. Factoring textual reviews into user preferences in multi-criteria based content boosted hybrid filtering (MCCBHF) recommendation system.

Author

Rajalakshmi, Sivanaiah, Sakaya Milton, R., and Mirnalinee, T.T.

Subjects
*RECOMMENDER systems, *SENTIMENT analysis, *USER-generated content, *SATISFACTION, *FILTERS & filtration, *METADATA, *CONSUMERS
Abstract

Recommendation systems help customers to find interesting and valuable resources in the internet services. Their priority is to create and examine users' individual profiles, which contain their preferences, and then update their profile content with additional features to finally increase the users' satisfaction. Specific characteristics or descriptions and reviews of the items to recommend also play a significant part in identifying the preferences. However, inferring the user's interest from his activities is a challenging task. Hence it is crucial to identify the interests of the user without the intervention of the user. This work elucidates the effectiveness of textual content together with metadata and explicit ratings in boosting collaborative techniques. In order to infer user's preferences, metadata content information is boosted with user-features and item-features extracted from the text reviews using sentiment analysis by Vader lexicon-based approach. Before doing sentiment analysis, ironic and sarcastic reviews are removed for better performance since those reviews inverse the polarity of sentiments. Amazon product dataset is used for the analysis. From the text reviews, we identified the reasons that would have led the user to the overall rating given by him, referred to as features of interest (FoI). FoI are formulated as multi-criteria and the ratings for multiple criteria are computed from the single rating given by the user. Multi-Criteria-based Content Boosted Hybrid Filtering techniques (MCCBHF) are devised to analyze the user preferences from their review texts and the ratings. This technique is used to enhance various collaborative filtering methods and the enhanced proposed MCKNN, MCEMF, MCTFM, MCFM techniques provide better personalized product recommendations to users. In the proposed MCCBHF algorithms, MCFM yields better results with the least RMSE value of 1.03 when compared to other algorithms. [ABSTRACT FROM AUTHOR]

Published
2023
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4. A New Multi-Attribute Decision Making Method for Overvalued Star Ratings Adjustment and Its Application in New Energy Vehicle Selection.

Author

Yu, Sumin, Zhang, Xiaoting, Du, Zhijiao, and Chen, Yanyan

Subjects
*ELECTRIC vehicles, *DECISION making, *CONSUMER preferences, *CARBON offsetting, *ENERGY security
Abstract

Under the global consensus of carbon peaking and carbon neutrality, new energy vehicles have gradually become mainstream, driven by the dual crises regarding the atmospheric environment and energy security. When choosing new energy vehicles, consumers prefer to browse the post-purchase reviews and star ratings of various new energy vehicles on platforms. However, it is easy for consumers to become lost in the high-star text reviews and mismatched reviews. To solve the above two issues, this study selected nine new energy vehicles and used a multi-attribute decision making method to rank the vehicles. We first designed adjustment rules based on star ratings and text reviews to cope with the issue of high star ratings but negative text reviews. Secondly, we classified consumers and recommended the optimal alternative for each type of consumer to deal with the issue of mismatched demands between review writers and viewers. Finally, this study compared the ranking results with the sales charts of the past year to verify the feasibility of the proposed method initially. The feasibility and stability of the proposed method were further verified through comparative and sensitivity analyses. [ABSTRACT FROM AUTHOR]

Published
2023
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5. Advancing Semantic Classification: A Comprehensive Examination of Machine Learning Techniques in Analyzing Russian-Language Patient Reviews

Author

Irina Kalabikhina, Vadim Moshkin, Anton Kolotusha, Maksim Kashin, German Klimenko, and Zarina Kazbekova

Subjects
machine learning, patient reviews, neural networks, online reviews, review classification, text reviews, Mathematics, QA1-939
Abstract

Currently, direct surveys are used less and less to assess satisfaction with the quality of user services. One of the most effective methods to solve this problem is to extract user attitudes from social media texts using natural language text mining. This approach helps to obtain more objective results by increasing the representativeness and independence of the sample of service consumers being studied. The purpose of this article is to improve existing methods and test a method for classifying Russian-language text reviews of patients about the work of medical institutions and doctors, extracted from social media resources. The authors developed a hybrid method for classifying text reviews about the work of medical institutions and tested machine learning methods using various neural network architectures (GRU, LSTM, CNN) to achieve this goal. More than 60,000 reviews posted by patients on the two most popular doctor review sites in Russia were analysed. Main results: (1) the developed classification algorithm is highly efficient—the best result was shown by the GRU-based architecture (val_accuracy = 0.9271); (2) the application of the method of searching for named entities to text messages after their division made it possible to increase the classification efficiency for each of the classifiers based on the use of artificial neural networks. This study has scientific novelty and practical significance in the field of social and demographic research. To improve the quality of classification, in the future, it is planned to expand the semantic division of the review by object of appeal and sentiment and take into account the resulting fragments separately from each other.

Published
2024
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6. Wine-experienscape and tourist satisfaction: through the lens of online reviews.

Author

Gunasekar, Sangeetha, Das, Payel, Dixit, Saurabh Kumar, Mandal, Santanu, and R Mehta, Simrran

Subjects
*SATISFACTION, *CONSUMERS' reviews, *CUSTOMER satisfaction, *TOURISTS, *WINE tourism
Abstract

This study examines the dimensions of wine-experienscape that influence customer satisfaction. Using the framework of experienscape, the dimensions for wine tourism was explored. The data was collected from TripAdvisor for Indian wineries. Results indicate that the Sensory dimension and Natural dimension of wine-experienscape increase the likelihood of customer satisfaction. The results are seen to vary with reviewer heterogeneity. The results have managerial and theoretical implications. [ABSTRACT FROM AUTHOR]

Published
2022
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7. Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated?

Author

Novoa-Paradela, David, Fontenla-Romero, Oscar, Guijarro-Berdiñas, Bertha, Novoa-Paradela, David, Fontenla-Romero, Oscar, and Guijarro-Berdiñas, Bertha

Abstract

[Abstract]: In the current landscape, user opinions exert an unprecedented influence on the trajectory of companies. In the field of online review platforms, these opinions, transmitted through text reviews and numerical ratings, significantly shape the credibility of products and services. For this reason, detecting inappropriate reviews becomes crucial. This paper addresses the problem of automatic anomalous review detection using a novel approach based on Anomaly Detection in the field of Natural Language Processing (NLP). Unlike other NLP tasks, anomaly detection in texts is a relatively emerging area. In this paper, we present a pipeline for opinion filtering that poses the problem of discerning between normal opinions containing relevant information about an item and anomalous opinions with unrelated content. Its key functionalities include: Classifying the reviews, assigning normality scores, and generating explanations for each classification, indispensable for the human who normally moderates these platforms. To evaluate the model, several Amazon datasets were used to demonstrate that the performance obtained is robust, obtaining an average F1 score of 91.4 detecting anomalies in the most complex scenario. In addition, a comparative study of three explainability techniques was conducted with 241 participants to measure the impact on understanding the classifications of the model and to rank their perceived usefulness of explanations. As a result, we obtained a system with great potential to automate tasks related to online review platforms, offering insights into anomaly detection applications in textual data and showing the difficulties that arise when the task to be explained presents a subjectivity component.

Published
2024

9. A New Multi-Attribute Decision Making Method for Overvalued Star Ratings Adjustment and Its Application in New Energy Vehicle Selection

Author

Sumin Yu, Xiaoting Zhang, Zhijiao Du, and Yanyan Chen

Subjects
car services platform, new energy vehicles, multi-attribute decision making, star ratings, text reviews, consumer demands, Mathematics, QA1-939
Abstract

Under the global consensus of carbon peaking and carbon neutrality, new energy vehicles have gradually become mainstream, driven by the dual crises regarding the atmospheric environment and energy security. When choosing new energy vehicles, consumers prefer to browse the post-purchase reviews and star ratings of various new energy vehicles on platforms. However, it is easy for consumers to become lost in the high-star text reviews and mismatched reviews. To solve the above two issues, this study selected nine new energy vehicles and used a multi-attribute decision making method to rank the vehicles. We first designed adjustment rules based on star ratings and text reviews to cope with the issue of high star ratings but negative text reviews. Secondly, we classified consumers and recommended the optimal alternative for each type of consumer to deal with the issue of mismatched demands between review writers and viewers. Finally, this study compared the ranking results with the sales charts of the past year to verify the feasibility of the proposed method initially. The feasibility and stability of the proposed method were further verified through comparative and sensitivity analyses.

Published
2023
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10. A Deep Learning-Based Sentiment Analysis Approach for Online Product Ranking With Probabilistic Linguistic Term Sets

Author

Zixu Liu, Huchang Liao, Maolin Li, Qian Yang, and Fanlin Meng

Subjects
Probabilistic linguistic term set, Sentiment analysis, Strategy and Management, Text classification, Deep learning, Electrical and Electronic Engineering, Text reviews
Abstract

The probabilities linguistic term set (PLTS) is an efficient tool to represent sentimental intensities hidden in unstructured text reviews that are useful for multicriteria online product ranking. Traditional machine learning-based sentiment analysis methods adopted in existing studies to obtain PLTSs often result in unsatisfying prediction accuracy and, thus, inevitably affect product ranking results. To overcome this limitation, in this study, we propose a deep learning-based sentiment analysis approach to produce PLTSs from online product reviews to rank online products. A natural language processing-based method is first applied to extract product features and corresponding feature texts from online reviews. Then, state-of-the-art deep learning-based models are implemented to conduct the sentiment classification for online product/feature review texts. To ensure classification accuracy, we propose an experimental matching mechanism to identify the level of sentiment tendency for all rating labels of a review dataset and then match each label with the most appropriate linguistic term. The experimental results reveal that our matching mechanism can benefit the training of a text classification model to identify sentiment tendencies from review texts with high prediction accuracy and with the help of the trained classification model, our approach can predict sentimental intensities of the extracted features' texts in the form of PLTSs with competitive accuracy. A case study of applying PLTSs output from our approach to an online product decision-making problem is also provided to validate the applicability of our approach.

Published
2023
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11. O Impacto de Reviews em Vídeo versus Texto na Intenção de Compra do Conumidor.

Author

Albano, P., Guerreiro, J., and Rita, P.

Abstract

Copyright of CISTI (Iberian Conference on Information Systems & Technologies / Conferência Ibérica de Sistemas e Tecnologias de Informação) Proceedings is the property of Conferencia Iberica de Sistemas Tecnologia de Informacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2019

12. Text-Based User-kNN: Measuring User Similarity Based on Text Reviews

Author

Terzi, Maria, Rowe, Matthew, Ferrario, Maria-Angela, Whittle, Jon, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Dimitrova, Vania, editor, Kuflik, Tsvi, editor, Chin, David, editor, Ricci, Francesco, editor, Dolog, Peter, editor, and Houben, Geert-Jan, editor

Published
2014
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13. Case Study: Deployment of AI Assistant for Commercial Work

Author

Chew, Kean Ho and Chat GPT3

Subjects
Case Study, Text Reviews, Assisted Generative AI, Vector Graphics, Commerical Applications
Abstract

The development of AI has reached a level of sophistication where it can generate new content based on internalized datasets, including both text and images, across different AI products. As of 2023, AI assistant is being used for testing and path-finding purposes and has garnered significant attention from various industries worldwide. At ZORALab, the author tested and deployed several AI assistant products, including Chat GPT-3 and DALL·E 2 from OpenAI and StableDiffusion from Stability AI, specifically for commercial use. The work covered in this paper includes successful context and content reviews, as well as the creation of logo vector graphics using these AI assisting tools. This case study demonstrates the potential for AI for improving commercial work processes. This paper provides a summarized review of applied AI products offered by OpenAI and Stability AI covering their purpose, roles, capabilities, and outputs. The author then identifies commercial requirements at ZORALab that are applicable for deployment, outlines the reasoning behind these decisions, and assesses the risks associated with implementation. A work process is developed as a result of this analysis. The paper concludes with the author's analytic findings highlighting the potential for AI to improve commercial work processes and outcomes. WARNING: Paper contains censored explicit images generated by AI, intentionally and unintentionally due to testing. 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UBI\"; Kurzgesagt – In a Nutshell via YouTube; Accessed on March 14, 2023; Available at: https://youtu.be/kl39KHS07Xc","JAKE WATSON; 2023; \"Lawyer Explains Stable Diffusion Lawsuit (Major Implications!)\"; Corridor Digital via Corridor Crew in YouTube; Accessed on March 16, 2023; Available at: https://youtu.be/gv9cdTh8cUo","CHEW KEAN HO; 2022; \"Supply Chain Threats in Software Industry Caused by Geo Political Influences and Business Model Changes\"; 1st Issue; DOI: 10.5281/zenodo.6815013; CERN EU, Open AIRE, and European Commission via Zenodo.org; Accessed on March 16, 2023; Available at: https://doi.org/10.5281/zenodo.6815013"]}

Published
2023
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14. O impacto de reviews em formatos de vídeo VS.: texto na decisão de compra do consumidor

Author

Albano, Pedro Alexandre Pina Caniço, Guerreiro, João, and Rita, Paulo

Subjects
Video review, Rede social, Electronic word-of-mouth, Comércio eletrónico, Vídeo digital, Online reviews, Consumidor, Marca, Tomada de decisão, Text reviews, Video reviews, Ciências Sociais::Economia e Gestão [Domínio/Área Científica]
Abstract

A presente dissertação de mestrado pretende avaliar o contributo de online video reviews em comparação com as text reviews tradicionais no que toca à tomada de decisão de compra dos consumidores. O objectivo principal desta dissertação visa responder ao impacto que este tipo de reviews tem, com vista a aferir conclusões sobre se a disponibilização desta ferramenta nos canais de comunicação online por parte das marcas poderia vir a ter sucesso ou não, com base nos resultados apresentados. Neste sentido, analisam-se questões como os tipos de consumidores, os tipos de reviews, as intenções de compra, os sentimentos relativos a determinadas reviews, e de que forma é que a conjugação de os diferentes cenários de resposta poderão chegar a uma conclusão comum: se de facto as video reviews têm um impacto diferencial na tomada de decisão de compra comparativamente com text reviews. Para chegar a esta conclusão, foi adotado um método quantitativo para o desenvolvimento da investigação, através de um inquérito por questionário de respostas fechadas. A amostra obteve um total de 153 respostas. Os resultados finais concluem video reviews têm mais impacto na decisão de compra dos consumidores do que text reviews, tanto ao nível do tipo de consumidor, como ao nível do tipo de produto e de review, sendo a qualidade da review assume um papel preponderante no desenvolvimento da perceção do consumidor quanto à qualidade do produto, tendo consequentemente maior impacto na sua decisão de compra. This dissertation intends to evaluate the contribution of online video reviews in comparison with the traditional text reviews in what concerns the decision of purchase by consumers. The main objective of this study is to evaluate the impact that this type of reviews has, in order to reach conclusions on whether the availability of this tool in the online communication channels by the brands could be successful or not, based on the results presented. In this sense, we analyze issues such as types of consumers, types of reviews, purchase intention, feelings about particular products, and how the combination of different response scenarios may lead to a common conclusion: If in fact video reviews have a differential impact on the purchase decision compared to text reviews. To reach this conclusion, a quantitative method for the development of the research was adopted through a survey of closed answers. The sample had a total of 153 responses. The final results conclude, in a precise way, that video reviews have more impact on consumers' buying decisions than text reviews, both in terms of consumer type, product type and review level, and the quality of the review assumes a preponderant role in the development of consumer perception of product quality, thus having a greater impact on their purchasing decision.

Published
2017

16. CBRecSys 2016. New Trends on Content-Based Recommender Systems:Proceedings of the 3rd Workshop on New Trends on Content-Based Recommender Systems co-located with 10th ACM Conference on Recommender Systems (RecSys 2016)

Author

Bogers, Toine, Lops, Pasquale, Koolen, Marijn, Musto, Cataldo, and Semeraro, Giovanni

Subjects
text reviews, content-based recommendation, implicit feedback, recommender systems, semantics, user-generated content, context
Abstract

While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2016 workshop provides a dedicated venue for papers dedicated to all aspects of content-based recommendation.

Published
2016

17. CBRecSys 2015. New Trends on Content-Based Recommender Systems:Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender Systems co-located with 9th ACM Conference on Recommender Systems (RecSys 2015)

Author

Bogers, Toine and Koolen, Marijn

Subjects
text reviews, content-based recommendation, implicit feedback, recommender systems, semantics, user-generated content, context
Abstract

While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2015 workshop aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation.

Published
2015

18. Workshop proceedings:CBRecSys 2014. Workshop on New Trends in Content-based Recommender Systems

Author

Bogers, Toine, Koolen, Marijn, and Cantádor, Ivan

Subjects
text reviews, content-based recommendation, implicit feedback, recommender systems, semantics, user-generated content, context
Abstract

While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. In recent years, competitions like the Netflix Prize, CAMRA, and the Yahoo! Music KDD Cup 2011 have spurred on advances in collaborative filtering and how to utilize ratings and usage data. However, there are many domains where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we do not know if and how these data sources should be combined to provided the best recommendation performance.The CBRecSys 2014 workshop aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation. We issued a Call for Papers asking for submissions of novel research papers (both long and short) addressing recommendation in do- mains where textual content is abundant (e.g., books, news, scientific articles, jobs, educational resources, Web pages, etc.) as well as dedicated comparisons of content-based techniques with collaborative filtering in different domains. Other relevant topics included opinion mining for text/book recommendation, semantic recommendation, content-based recommendation to allevi- ate cold-start problems, as well as serendipity, diversity and cross-domain recommendation.

Published
2014

19. Workshop on new trends in content-based recommender systems

Author

Pasquale Lops, Cataldo Musto, Marijn Koolen, Toine Bogers, and Giovanni Semeraro

Subjects
text reviews, Information retrieval, content-based recommendation, Computer science, User-generated content, Context (language use), Recommender system, Semantics, Usage data, context, Metadata, World Wide Web, Web page, Collaborative filtering, implicit feedback, recommender systems, semantics, user-generated content
Abstract

While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2014 workshop aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation.

Published
2014
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