The goal of this project is to conduct sentiment analysis as it allows businesses to quickly understand the overall opinions of their customers.
https://www.kaggle.com/datasets/airbnb/seattle
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To identify accommodation attributes Airbnb guests use to rate their experience
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To extract sentiments from unstructured customer review texts.
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To build a word cloud with key word attributes customers use in their reviews.
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Business Understanding: Exploring the business reasons for our data mining effort and what the company hopes to gain from the project. This is done by attempting to understand the goals and requirements of the business, in this case the Airbnb.
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Data Understanding: A brief overview of the data which involves accessing the data and exploring it using tables and graphics
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Data Preparation: Merging data sets, selecting a sample subset of data, aggregating records, deriving new attributes, sorting the data for modelling, removing missing values,cleaning text, tokenizing, transforming it into a Document-Term Matrix, and finally splitting into training and test data sets are all tasks involved in cleaning and preparing data for further analysis.
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Exploratory Data Analysis: The goal of this procedure is to summarise the main characteristics of the dataset, which is often done visually. Word clouds and top grams are among the outputs of this procedure.
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Modelling: To further support and provide insight to our previously posed questions, models such as Naive Bayes and Artificial Neural Networks are built.
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Recommendation and Conclusion: Here, we interpret our project findings, offer opinions based on the findings, and propose a solution, a gap in the research that needs to be filled, and the next steps in research.
GNU General Public License v3.0
