Social media as a main source of customer feedback

Hasson, Sharon Grubner; Piorkowski, John; McCulloh, Ian

doi:10.1145/3341161.3345642

Cited by 13 publications

(4 citation statements)

References 4 publications

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“…They further expound that it evaluates the customer's experience and is more likely to predict customer retention, loyalty, and product repurchase. The most common metrics for customer satisfaction are gathered via a traditional survey, but this generally involves added costs and requires active customer participation, which are areas of potential risk to a business (Hasson et al, 2019). However, with the emergence of social media platforms such as Facebook and Twitter, businesses can view customer satisfaction through their online written feedback organically.…”

Section: Customer Satisfaction Social Media and Feedbackingmentioning

confidence: 99%

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A Comparison of Sentiment Analysis Algorithms Using Twitter Feedback on a Local Bank: Developing Marketing Strategies for Philippine Banks

Ferrazzini,

Flores,

Joaquin

et al. 2024

International Conference on Innovative Practices in Management, Engineering &Amp; Social Sciences

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A breach in BDO's online security has escalated into clients losing their money, and BDO is faced with the need to provide assurance that strict cyber security protocols are being followed and that the risk of outsider hacking is negligible. Therefore, sentiment analysis is an appealing tool to facilitate the collection of feedback to improve banking processes. To perform the analysis, several Python Libraries and the Naïve-Bayes Algorithm were used. For data mining, Apify was used via its Twitter Scraper feature to gather data for the dataset used to train the algorithm. The individual accuracy of the program created was then compared to existing studies that used the Semantic Approach and J48 Algorithm to determine which algorithm is the most accurate and superior. Compared to J48 and Semantic Approach, J48 is the most accurate with Semantic Approach being the least accurate. The accuracy of the Naive Bayes algorithm is in the middle at 86.82%. While not as accurate as J48, this is compensated by its faster processing time and faster learning capabilities. From the results of the sentiment analysis, BDO must improve in various facets of their service such as cybersecurity, customer service, and privacy. The website and app must also be optimized for better online banking. For future research, it is recommended that the dataset is processed in other algorithms for a more accurate comparison. Frontend integration should also be considered for non-technology adept users and the code must also be optimized for efficient and more accurate data processing.

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Section: Customer Satisfaction Social Media and Feedbackingmentioning

confidence: 99%

“…This circumstance will inevitably cost the additional bank capital, which is potentially an expense they would rather minimize considering the losses from the hacking incident. (Hasson et al, 2019). This notion opens an avenue to consider automatic and automated methods of collecting and analyzing feedback.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Sentiment Analysis Algorithms Using Twitter Feedback on a Local Bank: Developing Marketing Strategies for Philippine Banks

Ferrazzini,

Flores,

Joaquin

et al. 2024

International Conference on Innovative Practices in Management, Engineering &Amp; Social Sciences

View full text Add to dashboard Cite

show abstract

“…Whether to create a new data product or, to re-engineer an existing one, harnessing a well-catalogued customer feedback platform empowers an innovator in terms of gaining customer insights (Hasson et al, 2019;Soroka et al, 2017;Wei et al, 2020), knowing competitor trend in the data-driven industry, and thus attaining competitive advantage over data rivals. This step is compatible with analytics-based data-driven innovation in a "lean startup" context that focuses on periodic refinement over time for continual innovation through minimizing the total time throughout the closed-loop process cycle.…”

Section: Step 7: Market Feedbackmentioning

confidence: 99%

Architecting and Developing Big Data-Driven Innovation (DDI) in the Digital Economy

Sultana

Akter

Kyriazis

et al. 2021

Journal of Global Information Management

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To revamp with new creative age characterized by ongoing digital transformation, more and more industries are capitalizing on digital innovation for their sustainable business growth. Drawing on a systematic literature review, thematic analysis, and using the theories of dynamic capabilities and market orientation, this research scrutinizes a systematic process for developing analytics-based data-driven innovation (DDI). Findings suggest a standardized seven-step process for DDI, including product conceptualization, data acquisition, data refinement, data storage and retrieval, distribution, presentation, and market feedback.

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“…By this way, the cache resources of overall system can be utilized more effectively. In [14], the authors have investigated the caching strategy and cooperative distance to optimize the throughput of the system and improve energy efficiency, where user similarity is calculated by the set of common neighbors among users. In [15], the different time spans have been divided by temporal score information.…”

Section: Related Workmentioning

confidence: 99%

Social‐Aware Caching Strategy Based on Joint Action Deep Reinforcement Learning

Jing

Song

et al. 2021

Wireless Communications and Mobile Computing

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Caching in device-to-device (D2D) networks is emerging a promising trend, which enables to reduce backhaul traffic. Moreover, social interaction among users influences the performance of overall system network. Therefore, it is crucial to consider social attributes in the D2D networks to develop a caching strategy to resolve the problem of unbalanced content distributed. In this paper, we consider two types of users according to their activeness, i.e., active users and inactive users. Inactive users assist active users cache contents during off-peak periods and provide the contents to the active users during peak periods to relieve the pressure of base station (BS). In addition, caching system model is divided into physical domain model and social domain model. In physical domain, the quality of communication links is judged by the delay between D2D users. In social domain, based on a real-world dataset, CiaoDVD, we calculate user similarity in three dimensions and obtain user trust by a trust topology to measure user relationships. Finally, in order to maximize the cache hit ratio, a joint action deep Q -networks (JADQN) framework is proposed to pair the active users with inactive users and distribute the contents to inactive users. Simulation results indicate that the proposed strategy improves the cache hit ratio by 42.9% and reduces the download delay by 48.8% compared with least frequency used (LFU) algorithm, which validates the effectiveness of our method.

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Social media as a main source of customer feedback

Cited by 13 publications

References 4 publications

A Comparison of Sentiment Analysis Algorithms Using Twitter Feedback on a Local Bank: Developing Marketing Strategies for Philippine Banks

A Comparison of Sentiment Analysis Algorithms Using Twitter Feedback on a Local Bank: Developing Marketing Strategies for Philippine Banks

Architecting and Developing Big Data-Driven Innovation (DDI) in the Digital Economy

Social‐Aware Caching Strategy Based on Joint Action Deep Reinforcement Learning

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