Select 2 hotels that are located closely geographically and have at least 300 varied reviews/ratings on TripAdvisor. Build naïve Bayes and decision tree models using the data for the first hotel to predict the hotel rating. Calculate precision, recall and the F1 score for each model and identify the better performing model. 15 pts. 3. Evaluate the knowledge transferability of the better performing model using the data for the second hotel. 10 pts. Submit the python notebooks (.ipynb) file and a PDF showing the answers.
Montclair-State-University-Info368/Assignment-6
1. Select 6-8 products/brands that directly compete with each other. Download 300-500 tweets related to each product/brand. Clean the data. Build a dendrogram showing the lexical similarity among the brands. Identify the top 10 words associated with each brand (after cleaning the data). 15 pts. 2. Select 2 hotels that are located closely geographically and have at least 300 varied reviews/ratings on TripAdvisor. Build naïve Bayes and decision tree models using the data for the first hotel to predict the hotel rating. Calculate precision, recall and the F1 score for each model and identify the better performing model. 15 pts. 3. Evaluate the knowledge transferability of the better performing model using the data for the second hotel. 10 pts. Submit the python notebooks (.ipynb) file and a PDF showing the answers.
Jupyter NotebookMIT