Implementation of Text Mining (baby steps)
STEP : Tokenizing Stemming Analyzing Result / Knowledge Install nltk : pip install nltk Install sklearn : pip install scikit-learn Open Visual Studio Code, then type this : import pandas as pd import nltk from nltk.sentiment.vader import SentimentIntensityAnalyzer from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from nltk.stem import WordNetLemmatizer # download nltk corpus (first time only) import nltk nltk. download ( "all" ) # Load the amazon review dataset df = pd. read_csv ( "https://raw.githubusercontent.com/pycaret/pycaret/master/datasets/amazon.csv" ) def preprocess_text ( text ): # Tokenize the text tokens = word_tokenize (text. lower ()) # Remove stop words filtered_tokens = [ token for token in tokens if token not in stopwords. words ( "english" ) ] # Lemmatize the tokens lemmatizer...