A Compression-Based Method for Detecting Anomalies in Textual Data

Torre-Abaitua, Gonzalo de la; Lago-Fernández, Luis F.; Arroyo, David

doi:10.3390/e23050618

Cited by 9 publications

(3 citation statements)

References 40 publications

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“…Text mining is applied in social media through different techniques, such as sentiment analysis, topic modeling, named entity recognition (NER), user profiling and behavior analysis, anomaly and event detection, and social network analysis (SNA) [46][47][48][49][50][51].…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

Nikolaidou,

Kopsacheilis,

Gavanas

et al. 2024

Sustainability

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For over three decades, the European Union’s (EU) transport policy has aimed at fostering environmental sustainability and energy efficiency. Since 2015, European policymakers have focused more on three key sustainable development goals: decarbonizing the transport system, promoting low-emission mobility solutions, and transitioning to renewable and alternative fuels. To effectively communicate priorities and engage stakeholders, EU policymakers regularly use social media platforms like Twitter (now known as X). This active discourse involves policymakers, industrial stakeholders, the media, and the public, offering insights into the role of transport policy in addressing climate change and energy transition challenges. The current research endeavors to track and analyze the evolution of user-generated content related to climate change, energy transition, and smart mobility on Twitter from 2011 to 2021. This research uses text-mining and social network analysis techniques to quantitatively and qualitatively assess the dynamics of relevant EU policies and their effects. The study’s findings can be used to establish a robust monitoring and evaluation framework at the EU and national levels. This framework will assess the effectiveness of communicating strategic priorities for sustainable transport development. It also holds potential for application in other sectors, broadening its impact.

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Section: Literature Reviewmentioning

confidence: 99%

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

Nikolaidou,

Kopsacheilis,

Gavanas

et al. 2024

Sustainability

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“…Following the confirmation of the reliability of the coded dataset, the study advanced to the third phase of the methodology: textual analysis (TA). TA incorporates data mining techniques to extract insights from complex datasets, aiming to identify patterns, trends, or anomalies within the textual data (De la Torre-Abaitua et al, 2021). These processes of knowledge discovery are essential for companies looking to refine their marketing strategies or analyze data from diverse analytical perspectives.…”

Section: Table 2 Correlation Between Krippendorff's Alpha Value and S...mentioning

confidence: 99%

Assessing the Value of Black Friday Promotions: An Analysis of Instagram Users’ Sentiments and Behavioral Responses

BALCIOĞLU

2024

EKEV Akademi Dergisi

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Black Friday, as a significant global retail phenomenon, provides substantial insights into consumer behavior and the effectiveness of marketing strategies. This study explores into the dynamics of consumer engagement by analyzing user-generated content (UGC) on Instagram, focusing on the 2021 Black Friday promotions by key technology companies in Turkey. Utilizing an advanced three-step text mining methodology, the research commences with Latent Dirichlet Allocation (LDA) for organizing data into distinct thematic clusters pertinent to Black Friday promotions. This is followed by a sentiment analysis, executed using Python, to evaluate the emotional nuances of the UGC in relation to these themes and the corresponding company promotions. The concluding phase involves an exhaustive textual analysis (TA) to extract actionable insights, which are instrumental in refining promotional strategies and deepening the comprehension of consumer interactions on social media platforms. The results reveal a predominantly positive reception of exclusive promotions and smartphone deals, highlighting their effectiveness as strategic elements in social media marketing. In contrast, themes linked to perceived fraud, negative feedback, misinformation, and customer service issues elicited adverse reactions from consumers. These contrasting responses emphasize the imperative for brands to develop transparent, authentic marketing communications and robust customer support systems. The study not only offers strategic recommendations for brands aiming to enhance their social media campaigns but also contributes a theoretical framework for future research in digital consumer behavior, especially in the context of significant promotional events like Black Friday.

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“…Much like the example, this paper defines novelty as the reviews that have sentiment opposite to their corresponding star ratings. Anomaly detection is an eminently researched topic in various domains [ 7 ], but there is an inadequate study on outlier detection using sentiment analysis of a dataset. It is classified predominantly into supervised and unsupervised learning.…”

Section: Introductionmentioning

confidence: 99%

Statistics-Based Outlier Detection and Correction Method for Amazon Customer Reviews

Chatterjee

Zhou

Abusorrah

et al. 2021

Entropy

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People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.

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A Compression-Based Method for Detecting Anomalies in Textual Data

Cited by 9 publications

References 40 publications

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

Assessing the Value of Black Friday Promotions: An Analysis of Instagram Users’ Sentiments and Behavioral Responses

Statistics-Based Outlier Detection and Correction Method for Amazon Customer Reviews

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