Decoding customer experiences in rail transport service: application of hybrid sentiment analysis

Mishra, Dibya Nandan; Panda, Rajeev Kumar

doi:10.1007/s12469-021-00289-7

Cited by 8 publications

(5 citation statements)

References 47 publications

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“…Social-media analytics has been used in rail services for disruption management ( 15, 24, 25, 27, 28, 57 ), communication strategy ( 58 ), demand estimation ( 59 ), service-quality analysis ( 16–19, 36–38, 60, 61 ), and user opinion capturing ( 62 , 63 ) purposes. In line with the purpose and scope of this research, we will focus on the papers examining service quality and user opinion.…”

Section: Social-media Analytics In Rail Service Qualitymentioning

confidence: 99%

“…Tweets were then assigned to a five-dimensional SERVQUAL scale (reliability, assurance, tangibles, empathy, responsiveness), and passenger satisfaction levels on regional and national routes were determined. Mishra and Panda ( 19 ) used the RAILQUAL scale for tweet classification and service-quality analysis. In the study, more than 50,000 tweets were collected and analyzed.…”

Section: Social-media Analytics In Rail Service Qualitymentioning

confidence: 99%

“…In addition to structured surveys, voluntary messages on social media reflecting user experiences contain important insights into the quality of rail services and offer significant improvement opportunities by understanding the passenger’s needs and opinions ( 14 , 15 ). Therefore, social-media analysis and user-generated content have been used in a few studies to measure the quality of rail services ( 16 – 19 ). Unlike traditional methods, these studies are based on user-generated content gathered from various social-media platforms and on measuring rail service quality by means of various machine-learning algorithms.…”

mentioning

confidence: 99%

“…This paper differs from the previous ones in several respects. First, the literature mainly focuses on urban light rail and subway systems ( 16 , 18 , 36 , 37 ), and the national rail system has only been addressed in a few studies ( 17 , 19 , 38 ). The time interval in these studies ranges from 20 days to 1 year, but the temporal variations in passenger demands have not been considered at the national level.…”

mentioning

confidence: 99%

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Service Quality in Rail Systems: Listen to the Voice of Social Media

Güner,

Taşkın,

Cebeci

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Service quality is essential to increase and maintain user loyalty to the railway system. In the literature, surveys have been used to measure user satisfaction, and mathematical methods have been applied to quantify the survey results. In recent years, user-generated content, including comments and complaints shared via social media, has been used to measure the quality of rail services. This content may provide important insights into the quality of the service provided with its dynamic structure. In this study, a SERVQUAL-based social-media analytics approach is used to measure railway service quality, placing special emphasis on the temporal variations in a national rail system. Topic modeling was used to assign each content item to the relevant service dimension and sentiment analysis was applied to measure the level of satisfaction. Importance–performance analysis was employed at the final stage to generate policy suggestions. Gathering more than 2.3 million social-media messages posted from 2011 to 2021, we examined the temporal evolution of service quality of the Turkish rail system. The results reveal the most and least important services and the satisfaction level of each dimension. The differences between the priorities of conventional and high-speed rail passengers are defined, and policy recommendations are presented.

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Section: Social-media Analytics In Rail Service Qualitymentioning

confidence: 99%

Section: Social-media Analytics In Rail Service Qualitymentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Service Quality in Rail Systems: Listen to the Voice of Social Media

Güner,

Taşkın,

Cebeci

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…The number of words used in these studies is quite distinct for each study, ranging from one single word [37] to 500 words [19], [25]. The most popular travel-related words in these studies are associated with the identification of transport modes [15], [21], [26], [40], type of public transport [12], public transport companies [44]- [46], and public transit riders' satisfaction [14], [17], [21]. Some of these dictionaries were validated with the help of transportation experts [47].…”

Section: Literature Reviewmentioning

confidence: 99%

Leveraging Social Media as a Source of Mobility Intelligence: An NLP-Based Approach

Fontes,

Murços,

Carneiro

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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This work presents a deep learning framework for analyzing urban mobility by extracting knowledge from messages collected from Twitter. The framework, which is designed to handle largescale data and adapt automatically to new contexts, comprises three main modules: data collection and system configuration, data analytics, and aggregation and visualization. The text data is pre-processed using NLP techniques to remove informal words, slang, and misspellings. A pre-trained, unsupervised word embedding model, BERT, is used to classify travel-related tweets using a unigram approach with three dictionaries of travel-related target words: small, medium, and big. Public opinion is evaluated using VADER to classify travel-related tweets according to their sentiments. The mobility of three major cities was assessed: London, Melbourne, and New York. The framework demonstrates consistently high average performance, with a Precision of 0.80 for text classification and 0.77 for sentiment analysis. The framework can aggregate sparse information from social media and provide updated information in near real-time with high spatial resolution, enabling easy identification of traffic-related events. The framework is helpful for transportation decision-makers in operational control, tactical-strategic planning, and policy evaluation. For example, it can be used to improve the management of resources during traffic congestion or emergencies.INDEX TERMS Social media, urban mobility, text classification, sentiment analysis, framework.

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