Sentiment Analysis in Customer Experience in Philippine Courier Delivery Services using VADER Algorithm Thru Chatbot Interviews

Mindoro, Jennalyn N.; Malbog, Mon Arjay F.; Nipas, Marte D.; Susa, Julie Ann B.; Acoba, Aimee G.; Gulmatico, Joshua S.

doi:10.1109/icpc2t53885.2022.9777007

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…These dual advantages enable both customers and courier service companies to promptly address concerns related to customer satisfaction, ultimately enhancing customer choices and the overall management of courier services by the companies [20]. Previous research has underscored sentiment analysis as the preeminent analytical technique for assessing opinions and reviews derived from social media [1], [21]. Sentiment analysis involves the meticulous examination of textual content to discern sentiment pertaining to a specific subject of interest [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring public sentiments towards courier services in Malaysia through Twitter: a sentiment analysis approach

Mohamed Ariff,

Raja Salim,

Azizan

et al. 2024

IJEECS

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This study delves into the sentiment analysis of courier services in Malaysia through the lens of tweets. With the escalating reliance on courier services for daily parcel deliveries, understanding public sentiment becomes imperative. Through a systematic methodology encompassing data gathering, preparation, alteration, analysis, modeling, and deployment, this study aims to furnish valuable insights and recommendations. A dynamic graphical representation of sentiment scores is presented for intuitive interpretation and informed decision-making. The study's significance lies in empowering Malaysians to make judicious choices regarding courier services, thereby fostering transparency in the industry. The study proposes several avenues for future enhancements, including the integration of autocorrect features, diversifying data sources and languages, incorporating deep learning techniques, and developing a user-friendly interface for enhanced visualization. This study serves as a guiding resource, illuminating sentiments surrounding courier services in Malaysia and paving the way for future advancements in sentiment analysis within this domain.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring public sentiments towards courier services in Malaysia through Twitter: a sentiment analysis approach

Mohamed Ariff,

Raja Salim,

Azizan

et al. 2024

IJEECS

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“…SVM and the Vader lexicon have been used in various domains to predict consumer response and product or service experiences [20]- [24]. Twitter has also been used as a platform for analyzing responses to climate change [25].…”

Section: Introductionmentioning

confidence: 99%

“…The Vader lexicon has proven to be an effective annotation tool, demonstrating high accuracy rates. In a study on predicting customer response from chatbot sources, Vader achieved an accuracy rate of 93.33% [24]. When combined with SVM, the accuracy of Vader increased to 80.3%, surpassing Naive Bayes, Random Forest, Neural Network, Decision Tree, and k-Nearest Neighbour [26].…”

Section: Introductionmentioning

confidence: 99%

Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation

Nurcahyawati¹,

Mustaffa

2023

J. Inf. Syst. Eng. Bus. Intell.

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Background: The concept of customer orientation, which is based on a set of fundamental beliefs that prioritize the interests of the customer, requires companies to detect these interests in order to maintain a high level of quality in their products or services. Furthermore, there are several indicators of customer orientation, and one of them is their opinion or taste, which provides valuable feedback for businesses. With the rapid development of social media, customers can express emotions, thoughts, and opinions about services or products that may not be easily conveyed in the real world. Objective: The objective of this study is to detect customer orientation towards product or service quality, as expressed in online or social media. Additionally, the study showcases the novelty and superiority of the annotation process used for detecting customer orientation classifications. Methods: This study employs a method to compare the classification performance of the Vader lexicon annotation process with manual annotation. To accomplish this, a dataset from the Amazon website will be analyzed and classified using the Support Vector Machine algorithm. The objective of this method is to determine the level of customer orientation present within the dataset. To evaluate the effectiveness of the Vader lexicon, the study will compare the results of manual and automatic data annotation. Results: The results showed that customer orientation towards product or service quality has a predominantly positive value, comprising up to 76% of the total responses analyzed. Conclusion: The findings demonstrate that using Vader in the annotation process results in superior accuracy values compared to manual annotation. Specifically, the accuracy value increased from 86% to 88.57%, indicating that Vader could be a reliable tool for annotating text. Therefore, future studies should consider using Vader as a classifier or integrating it into the annotation process to further enhance its performance. Keywords: Classification, Customer, Orientation, Text analysis, Vader lexicon,

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Sentiment Analysis in Customer Experience in Philippine Courier Delivery Services using VADER Algorithm Thru Chatbot Interviews

Cited by 2 publications

References 13 publications

Exploring public sentiments towards courier services in Malaysia through Twitter: a sentiment analysis approach

Exploring public sentiments towards courier services in Malaysia through Twitter: a sentiment analysis approach

Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation

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