Feedback Sentiment Research Articles

Data-Driven Insights from 10,000 Reviews: Fostering Sustainability through Rapid Adaptation to Guest Feedback

The present research aims to explore how customer satisfaction and discontent may influence the financial success of luxury hotels by analyzing more than 10,000 reviews from the Radisson Blu Hotel in Bucharest and financial data spanning a decade. Text mining and sentiment analysis were used in order to pinpoint crucial elements that could impact visitor experience and the way in which they might correlate with hotel revenues in the long run. Research has linked positive visitor evaluations to revenue growth, while negative feedback does not consistently lead to financial declines, suggesting that premium brands may be resilient to mild online criticism. Our research examines how customer feedback sentiment and business income are interconnected throughout time, emphasizing the significance of handling guest contentment in order to reduce resource spending and create a sustainable feedback loop between guests and businesses. Our analysis challenges the idea that negative evaluations always have a detrimental impact on financial performance and highlights the importance of long-term positive feedback in the hospitality sector. This study enhances comprehension of the influence of customer feedback on the luxury hotel business, providing valuable insights for service industry experts on utilizing reviews in the future in order to gain strategic benefits and also develop their businesses in a sustainable way.

Assessing supervisor versus trainee viewpoints of entrustment through cognitive and affective lenses: an artificial intelligence investigation of bias in feedback.

The entrustment framework redirects assessment from considering only trainees' competence to decision-making about their readiness to perform clinical tasks independently. Since trainees and supervisors both contribute to entrustment decisions, we examined the cognitive and affective factors that underly their negotiation of trust, and whether trainee demographic characteristics may bias them. Using a document analysis approach, we adapted large language models (LLMs) to examine feedback dialogs (N = 24,187, each with an associated entrustment rating) between medical student trainees and their clinical supervisors. We compared how trainees and supervisors differentially documented feedback dialogs about similar tasks by identifying qualitative themes and quantitatively assessing their correlation with entrustment ratings. Supervisors' themes predominantly reflected skills related to patient presentations, while trainees' themes were broader-including clinical performance and personal qualities. To examine affect, we trained an LLM to measure feedback sentiment. On average, trainees used more negative language (5.3% lower probability of positive sentiment, p < 0.05) compared to supervisors, while documenting higher entrustment ratings (+ 0.08 on a 1-4 scale, p < 0.05). We also found biases tied to demographic characteristics: trainees' documentation reflected more positive sentiment in the case of male trainees (+ 1.3%, p < 0.05) and of trainees underrepresented in medicine (UIM) (+ 1.3%, p < 0.05). Entrustment ratings did not appear to reflect these biases, neither when documented by trainee nor supervisor. As such, bias appeared to influence the emotive language trainees used to document entrustment more than the degree of entrustment they experienced. Mitigating these biases is nonetheless important because they may affect trainees' assimilation into their roles and formation of trusting relationships.

Student textual feedback sentiment analysis using machine learning techniques to improve the quality of education

Student textual feedback sentiment analysis using machine learning techniques to improve the quality of education

Event Impact Analysis Using Machine Learning

The "Event Impact Analysis using Machine Learning" project presents an innovative and comprehensive framework for evaluating the repercussions of significant events. This study presents a machine learning model designed to classify feedback into positive or negative sentiments based on relevant parameters, accompanied by comprehensive parameter visualizations. The aim is to create an effective system that accurately categorizes feedback sentiment while providing insightful graphical representations of key parameters. The model employs diverse machine learning algorithms, including advanced natural language processing techniques, to extract informative features from textual feedback. These features are then utilized to train a sentiment classification model capable of distinguishing between positive and negative sentiments. Key parameters contributing to sentiment classification are identified and visually presented through informative graphs and charts. These visualizations offer an intuitive understanding of the relationships between parameters and sentiment, enhancing the interpretability of the model's decision-making process. Real-world feedback datasets are utilized for model training and evaluation. Standard metrics such as accuracy, precision, recall, and F1-score are employed to assess the model's performance in sentiment classification. This study contributes to the field of sentiment analysis by combining accurate sentiment classification with parameter visualization. The resulting insights offer businesses a deeper understanding of customer feedback, aiding in improved decision-making and enhanced user experiences. Key Words: Event Analysis, Feedback Classification, Parameter Visualization, Natural Language Processing, Decision-Making.

How do ideas gain legitimacy in internal crowdsourcing idea development? Exploring the effects of feedback on idea selection

ABSTRACT Internal crowdsourcing offers new opportunities for ideas to gain legitimacy in idea development as not only managers but also experts are able to provide feedback to improve and subsequently accept ideas for further realisation. Given this far limited knowledge about how ideas gain legitimacy in idea development, this paper focuses on the influence of feedback, a main source of idea legitimacy in internal crowdsourcing. More specifically, this study aims to explore how feedback from different feedback providers (i.e., managers and experts) influences idea selection in internal crowdsourcing idea development. Based on text mining methods including sentiment, topic-model, and expert identification analyses, results reveal that feedback sentiment, feedback diversity and feedback amount play key roles in idea legitimisation. Specifically, these aspects of feedback influence idea acceptance in idea selection through the mediating role of idea revision in idea development and that this relationship is moderated by feedback providers. This study extends previous knowledge about the legitimisation process from the perspective of feedback in idea development by offering both a more comprehensive view, including different feedback aspects, and a more granular measurement of these, through the use of text analysis. Based on insights from the study, practical implications are presented for how to gain legitimacy from feedback for idea selection in internal crowdsourcing.

Functioning of an Android app “TNAU Paddy Expert System” and its User’s Feedback Sentiment Analysis

TNAU paddy expert system app is a system that links people and institutions to promote mutual learning and utilize agriculture-related technologies, knowledge and information. The present study expounded the functions of an android app “TNAU paddy expert system”, also it was intended to fi nd out the nature of feedbacks provided by the TNAU paddy expert system app users and to foster the application more for eff ective usage through the feedbacks provided by the users. The feedback and ratings were collected from the TNAU Paddy expert system app (both in English and Tamil version) page in the open-source platform viz., Google play store, a total of 63 reviews and 109 users’ ratings were collected from the application interface present in the Google play store to fi nd out the nature of feedback and the level of satisfaction of the users. Sentiment analysis was carried out with the help of ‘MS Excel 365’ and ‘azure machine learning’ softwares. Then the data was also subjected to factor analysis with the help of Statistical Package for Social Sciences (SPSS) 16.0, to fi nd out the major opinions that were expressed by the TNAU Paddy expert system app users. TNAU Paddy expert system app was the application of scientifi c research which imparts the scientifi cally proven paddy technologies and agricultural practices for the betterment of the farming community, that aids farmers to make right decisions at the needed time and through crop doctor component it helps them by protecting their crop and reducing the time and cost.

Enhancing text classification performance by preprocessing misspelled words in Indonesian language

Supervised learning using shallow machine learning methods is still a popular method in processing text, despite the rapidly advancing sector of unsupervised methodologies using deep learning. Supervised text classification for application user feedback sentiments in Indonesian Language is one of the applications which is quite popular in both the research community and industry. However, due to the nature of shallow machine learning approaches, various text preprocessing techniques are required to clean the input data. This research aims to implement and evaluate the role of Levenshtein distance algorithm in detecting and preprocessing misspelled words in Indonesian language, before the text data is then used to train a user feedback sentiment classification model using multinomial Naïve Bayes. This research experimented with various evaluation scenarios, and found that preprocessing misspelled words in Indonesian language using the Levenshtein distance algorithm could be useful and showed a promising 8.2% increase on the accuracy of the model’s ability to classify user feedback text according to their sentiments.

WITHDRAWN: Student feedback sentiment analysis: A review

To learn how the students are performing is an art, an art where all educationalist must know as the teaching change learning about the students also get difficult to learn about the student in this modern society an effective students feedback system is a must. Student's feedback is always an important part of knowing the condition of the classroom and performance of the Teacher, by learning and understanding the student condition teacher can have improved their teaching method. But taking feedback orally or through counselling sometimes lack proper feedback system from students. By using a student response system online or offline it much easier and can be analysed to improve the teaching method. Feedback using questionnaires and analysis of the data it's much faster and accurate. A technique like Sentiment Analysis can identify the students feeling in positive, negative, and neutral. This paper focuses on previous work done by many authors in the same field.

Adaptive cognitive intelligence in analyzing employee feedback using LSTM

Cognitive computing is the mirroring of human brain and this is made possible by using natural language processing, pattern recognition and data mining. By mirroring the human brain (Cognitive computing system), helps to solve some of the complicated problems without much of human supervision. In the fast-changing world, the major challenge every organization facing is difficulty in retaining its employees. Employees may leave an organization due to low salary, overwork, lack of opportunities and recognition, work culture, work-life imbalance etc. Better ways to retain employees is to understand their requirements and fulfill them. The proposed employee feedback sentiment analysis system collects the employee feedback reviews from open forums and perform sentiment analysis using Recurrent Neural Network – Long Short-term Memory (RNN-LSTM) algorithm. On performing Sentiment analysis, employee review comments are classified as Positive or Negative. A report is generated and sent to the HR of the organization as webapp or mobile app. The report has total number of positive and negative comments and positive and negative counts with respect to salary, work pressure etc. With the report, the organization can arrive at identifying social sentiments of their brand and may take corrective actions to retain employees which benefits both organization and employees. This paper also captures the performance of various models in training and predicting the employee feedback dataset and the models evaluated are Logistic Regression, Support Vector Machine, Random Forest Classifier, AdaBoost Classifier, Gradient Boosting Classifier, Decision Tree Classifier and Gaussian Naïve Bayes. The classification report and accuracy of each model is captured. The dataset size was gradually increased from 200 to 1000 and accuracy was predicted for each model. It was identified that the accuracy of machine learning algorithms was ranging between 66% to 85%. On training RNN-LSTM algorithm with dataset of size 30 k, the accuracy was 88%. It was identified that Deep learning algorithm RNN-LSTM performs better with huge dataset. Increasing dataset size still increase the performance of RNN-LSTM algorithm in training and prediction. Thus, the objective function of the proposed model to perform sentiment analysis on employee feedback review comments is achieved successfully.

A customer feedback sentiment dictionary: Towards automatic assessment of online reviews

This paper aims at creating a tool to automatically extract online customer reviews of hospitality businesses and to assign a reliable score to them, based on a specifically created sentiment dictionary for this purpose by means of a statistical learning method. The effect of the amount of available training data and their resulting dictionaries is investigated. As such, a practical approach for applying LASSO regression in the context of online hospitality reviews is presented resulting in a sentiment dictionary of 778 terms with their associated weights trained on 20 000 reviews. It is shown that the created dictionary is able to accurately predict online review scores set by consumers, therefore highlighting the practical relevance of the proposed approach.

Methods of Applying Machine Learning to Student Feedback Through Clustering and Sentiment Analysis

Machine learning is used to analyze student feedback in first-year engineering courses. This exploratory work builds on previous research at the University of Toronto, where a multi-year investigation used an online survey to collect quantitative and qualitative data from incoming first-year students. [1] (N ~1000)Sentiment analysis, a machine learning method, is used to investigate the relationship between hours of study outside of scheduled instructional hours and qualitative survey feedback sentiment. The results are visualized with chronological sentiment graphs, which contextualize the results in relation to key events during the school year.Large drops in sentiment were seen to occur during weeks with major assessments and deadlines. An inverse correlation between hours spent outside of class and feedback sentiment was also noticed

Exploring the Effects of Feedback Sentiment and Expertise in Internal Crowdsourcing for Ideas

With the increased usage of internal crowdsourcing for ideas, many firms are able to receive feedback from the voluntary collective wisdom with diverse expertise instead of a limited amount of specified experts, resulting in a large amount, diverse and complex input to ideas. Given the so far limited knowledge about it, firms face challenges to fruitfully manage feedback to ideas. Two aspects playing critical roles in the feedback on proposed ideas for innovation are arguably feedback sentiment and its source expertise. This paper aims to explore the roles of feedback sentiment and expertise in internal crowdsourcing for ideas. It does so through an empirical study drawing on data collected from a Swedish multi-national company using internal crowdsourcing for innovation ideas. The investigation explicitly captures the effects of feedback sentiment and source expertise in terms of: 1) positive and negative feedback sentiment; and 2) feedback sentiment in conjunction with expertise of feedback source. The ...