Performance Evaluation of Learners for Analyzing the Hotel Customer Sentiments Based on Text Reviews

Sisodia, Dilip Singh; Nikhil, Saragadam; Kiran, G. S. S. Raj; Shrawgi, Hari

doi:10.1007/978-981-13-8253-6_20

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Customer reviews play a substantial role in a hotel's persona which directly affects its valuation (Sisodia et al 2020). This study findings can be used as insight into what are the things that generate a satisfying experience and strengthen brand positioning of the hotel, boost customer satisfaction and exploring consumer views with respect to pre-identified brands to establish a hotel positioning.…”

Section: Managerial Implicationsmentioning

confidence: 86%

Exploring Brand Positioning and Hotel Persona Trough Wom and Content by Text Analysis

Widarmanti

Ramantoko

2020

ijbe

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Section: Managerial Implicationsmentioning

confidence: 86%

Exploring Brand Positioning and Hotel Persona Trough Wom and Content by Text Analysis

Widarmanti

Ramantoko

2020

ijbe

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“…By utilizing machine learning techniques for sentiment analysis, this paper demonstrates the potential for automated analysis of social media data, which can provide valuable insights for businesses and organizations seeking to understand customer sentiment and improve their overall customer experience. Performance Evaluation of Learners for Analyzing the Hotel Customer Sentiments Based on Text Reviews [4,10]The study aims to tackle the challenge of analyzing the enormous amount of hotel reviews and opinions available on the internet. With the availability of large datasets containing text reviews, it has become possible to automate sentiment profiling and opinion mining.…”

Section: Literature Surveymentioning

confidence: 99%

Automatic Classification of Railway Complaints using Machine Learning

Sathivika Roy,

Vasukidevi,

Malleswari

et al. 2024

E3S Web Conf.

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People may now express their thoughts and ideas with a wider audience because of the popularity of social media sites like Twitter, Instagram, and Facebook. Businesses now utilise Twitter to reply to client comments, reviews, and grievances. Every day, millions of individuals discuss a wide range of issues on Twitter by sharing their ideas and interests. Sentiment analysis is a useful method for analysing such data, which involves identifying the sentiment of the source text and classifying it as positive, neutral, or negative. However, due to the vast amount of data, it can be challenging for businesses to address every customer’s question or complaint in a timely manner. Some issues may be urgent but delayed due to the volume of information. In order to prioritize emergency tweets, a system is proposed that utilizes machine learning algorithms such as Random Forest, Support Vector Machine, Logistic Regression, and Naïve Bayes to identify tweets based on their urgency. The proposed system gathers and preprocesses unstructured data, performs feature extraction, trains, assesses and compares multiple machine learning models to determine the best classifier with the highest accuracy, and uses vectorization via a pipeline to determine the sentiment of a new tweet provided as input.

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“…For example for the above-mentioned sentence, bi-gram would be 'one great', and 'great app'. N-gram features are reported to show better performance for review classification [55,56]. TF-IDF weighs down the most common words occurring in all text documents and gives importance to each word that appears in a subset of documents.…”

Section: Feature Selectionmentioning

confidence: 99%

GBSVM: Sentiment Classification from Unstructured Reviews Using Ensemble Classifier

et al. 2020

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User reviews on social networking platforms like Twitter, Facebook, and Google+, etc. have been gaining growing interest on account of their wide usage in sentiment analysis which serves as the feedback to both public and private companies, as well as, governments. The analysis of such reviews not only plays a noteworthy role to improve the quality of such services and products but helps to devise marketing and financial strategies to increase the profit for companies and customer satisfaction. Although many analysis models have been proposed, yet, there is still room for improving the processing, classification, and analysis of user reviews which can assist managers to interpret customers feedback and elevate the quality of products. This study first evaluates the performance of a few machine learning models which are among the most widely used models and then presents a voting classifier Gradient Boosted Support Vector Machine (GBSVM) which is constituted of gradient boosting and support vector machines. The proposed model has been evaluated on two different datasets with term frequency and three variants of term frequency-inverse document frequency including uni-, bi-, and tri-gram as features. The performance is compared with other state-of-the-art techniques which prove that GBSVM outperforms these models.

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Performance Evaluation of Learners for Analyzing the Hotel Customer Sentiments Based on Text Reviews

Cited by 5 publications

References 7 publications

Exploring Brand Positioning and Hotel Persona Trough Wom and Content by Text Analysis

Exploring Brand Positioning and Hotel Persona Trough Wom and Content by Text Analysis

Automatic Classification of Railway Complaints using Machine Learning

GBSVM: Sentiment Classification from Unstructured Reviews Using Ensemble Classifier

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