To know the best among the five Naive Bayes algorithms in analyzing the sentiment analysis
Gaussian, Categorical, Complement etc
The basics of NLP such as tokenizing and lemmatizing using the NLTK library
The use of both TFIDF, CountVectorizer, Pipeline
Exercise Statement
Provided a dataset of users' reviews of negative and positive classier, the model should be able to predict if a word or phrase is negative or positive.
Prerequisites
nltk, sklearn
Data source/summary:
The dataset contains a csv file consisting of amazon_cell reviews, yelp user reviews, and imdb review, and the typical responses they have
Suggest/Propose Solutions
I have the solution and will be happy to create a pull request to include the exercise statement/solution]
Learning Goals
To know the best among the five Naive Bayes algorithms in analyzing the sentiment analysis Gaussian, Categorical, Complement etc
The basics of NLP such as tokenizing and lemmatizing using the NLTK library
The use of both TFIDF, CountVectorizer, Pipeline
Exercise Statement Provided a dataset of users' reviews of negative and positive classier, the model should be able to predict if a word or phrase is negative or positive.
Prerequisites nltk, sklearn
Data source/summary: The dataset contains a csv file consisting of amazon_cell reviews, yelp user reviews, and imdb review, and the typical responses they have
Suggest/Propose Solutions I have the solution and will be happy to create a pull request to include the exercise statement/solution]