基于在线民宿 UGC 数据的意见挖掘项目，包含数据挖掘和NLP 相关的处理，负责数据采集、主题抽取、情感分析等任务。目的是克服用户打分和评论不一致，实时对在线民宿的满意度评测，包含在线评论采集和情感可视化分析。搭建了百度地图POI查询入口，可以进行自动化的批量查询 POI 信息的功能；构建了基于在线民宿语料的 LDA 自动主题聚类模型，利用主题中心词能找出对应的主题属性字典；以用户打分作为标注，然后 litNlp 自带的字符级 TextCNN 进行情感分析，将情感分类概率分布作为情感趋势，最后通过 POI 热力图的方式对不同地域的民宿满意度进行展示。软件版本请见链接。
An interactive dashboard built using Tableau, visualizing British Airways customer review data. This project analyzes customer sentiment, satisfaction levels, and other key metrics to provide insights into passenger experiences and identify improvement areas for British Airways.
Power BI Visualization
Application of Latent Dirchlet Allocation, Text mining and classical statistic machine learning techniques to understand the customer satisfaction of different topics using Ryanair Reviews
The repository consists of Data Analysis using Tableau to provide the descriptive analysis of British Airways Customer Ratings.
This Power BI dashboard provides an in-depth analysis of call center performance, customer interactions, and sentiment trends. It helps businesses monitor key call metrics, understand customer satisfaction, and optimize support channels.
This project aims to leverage EDA techniques to analyze loan application data and identify predictors of loan default. By systematically exploring missing data, outliers, imbalance, and relationships between variables, the analysis aims to uncover insights into default risk.
Excel-based analysis of call center performance metrics, focusing on operational efficiency and customer satisfaction.
Sentiment analysis of Flipkart reviews. Predict ratings. Dublin Business School research.
A comprehensive analysis of customer satisfaction trends, this study delves into feedback and service quality data to enhance the passenger experience with British Airways.
Build a statistical model based customer satisfaction dataset from Qualtrics survey
This project analyzes airline passenger satisfaction using data mining on a dataset of 129,880 customers. It employs Gaussian Naive Bayes and Logistic Regression models, with Logistic Regression slightly outperforming. Key features include travel type and customer loyalty. Evaluation metrics include accuracy and ROC-AUC scores.
This repository contains a PowerBI dashboard to analyze an airline satisfaction survey.
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