Leveraging Lexicon-Based and Sentiment Analysis Techniques for Online Reputation Generation

doi:10.22266/ijies2021.1231.25

Cited by 5 publications

(7 citation statements)

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“…In a unique reputation generation system, Twitter content was evaluated to determine credible reputation scores for products. Integrating sentiment orientation, user credibility, and tweet credibility, the system's computed values closely aligned with ground truth scores from various platforms, suggesting practical applications for consumers and businesses in decision-making processes on e-commerce platforms [145].…”

Section: Marketing and Brand Managementmentioning

confidence: 67%

“…To responsibly deploy sentiment analysis models, approaches and frameworks must address ethical issues including algorithm bias, privacy, and consumer data usage. This is stressed in studies like [145,149]. These studies contribute to sentiment analysis model ethics discussions.…”

Section: Ethical Considerationsmentioning

confidence: 99%

“…[65] mines Twitter data for sentiment analysis while [129] suggests using NLP to solve erroneous usergenerated reviews in e-commerce. Furthermore, [145] proposes a Twitter reputation generating method that handles multiple sources. These studies demonstrate that sentiment analysis is complex and requires advanced methods to handle varied data sources.…”

Section: Handling Diverse Data Sourcesmentioning

confidence: 99%

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Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2024

Preprint

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In recent years, e-commerce platforms have replaced conventional marketplaces. People are rapidly adopting internet shopping due to the convenience of making purchases from the comfort of their homes. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer reviews, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges by reviewing 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the majority of existing research relies on Amazon user reviews. Additionally, recent research has encouraged the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates increasing interest of researchers and continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.

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Section: Marketing and Brand Managementmentioning

confidence: 67%

Section: Ethical Considerationsmentioning

confidence: 99%

Section: Handling Diverse Data Sourcesmentioning

confidence: 99%

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Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2024

Preprint

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“…Boumhidi and Nfaoui [9] made use of a lexicon for classifying tweets at entity-level into three classes. They also made use of part-of-speech of words for this task.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The set of polarity levels include the conflict polarity level in addition to basic three polarity levelsneutral, positive, and negative. The lexicon-based approach is mostly used for polarity related subtasks [9]. The lexicon created in [10] was used for sentiment classification [11] lexicon-based approach is suited if domain specific lexicon is available.…”

Section: Introductionmentioning

confidence: 99%

Polarity Detection and Feature-Based Summarization for Aspect Based Sentiment Analysis Dataset

2022

IJIES

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Aspect Based Sentiment Analysis (ABSA) aims to perceive the opinion of the majority of reviewers for each feature (aspect category) of the product. We target both Aspect Term Polarity (ATP) and Aspect Category Polarity (ACP) subtasks of ABSA. We retrieve different word vector-based representations of sentence and augment them with n-gram and sentiment features using novel algorithm. We employ supervised machine learning approaches and propose the ensemble of Logistic Regression, k-Nearest Neighbor, and Support Vector Machine models for these subtasks. We experiment with four major domains from Hindi ABSA Dataset and report increase in performance accuracy by 16% for ATP subtask and 2% to 13% across domains for ACP subtask over results from researchers who released this dataset. Secondly, we also propose a novel WFBOS method to compute the Summary Score [range: -100 to 100] of each category from a particular domain. We make use of frequency of positive, neutral, negative, and conflict sentiment, of each aspect category from among the reviews of a particular domain in Hindi ABSA Dataset to do this computation.

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Cross-Platform Reputation Generation System Based on Aspect-Based Sentiment Analysis

2022

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The active growth of Internet-based applications such as social networks and e-commerce websites leads people to generate a tremendous amount of opinions and reviews about products and services. Thus, it becomes very crucial to automatically process them. Over the last ten years, many systems have been proposed to generate and visualize reputation by mining textual and numerical reviews. However, they have neglected the fact that online reviews could be posted by malicious users that intend to affect the reputation of the target product. Besides, these systems provide an overall reputation value toward the entity and disregard generating reputation scores toward each aspect of the product. Therefore, we developed a system that incorporates spam filtering, review popularity, review posting time, and aspect-based sentiment analysis to generate accurate and reliable reputation values. The proposed model computes numerical reputation values for an entity and its aspects based on opinions collected from various platforms. Our proposed system also offers an advanced visualization tool that displays detailed information about its output. Experiment results conducted on multiple datasets collected from various platforms (Twitter, Facebook, Amazon . . . ) show the efficacy of the proposed system compared with state-of-the-art reputation generation systems.

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Leveraging Lexicon-Based and Sentiment Analysis Techniques for Online Reputation Generation

Cited by 5 publications

References 20 publications

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Polarity Detection and Feature-Based Summarization for Aspect Based Sentiment Analysis Dataset

Cross-Platform Reputation Generation System Based on Aspect-Based Sentiment Analysis

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