sentiment analysis
Introduction Build an end-to-end sentiment classification system from scratch. The system accepts a movie review as input and classifies it as either positive or negative. There are three main steps:
Split the data into train and test sets, cleaning dataset, tokenize, stem words, create bag-of-words features, etc. Models - Create and experiment with different models: Naïve Bayes classification algorithm, Linear Model algorithm, SVM algorithm, etc Evaluation - Compare the performances of the models and outline steps to make the chosen model do better.
sentiment_analysis_test.py - Main code for sentiment analysis testing. trainingDataSet.py- for training the model
Python 3.6+ nltk sklearn
Data files for the sentiment analysis project are included under data/imdb-reviews. These are movie reviews from the website imdb.com, each labeled as either 'positive', if the reviewer enjoyed the film, or 'negative' otherwise.
Some of the NLP libraries require additional data for performing tasks like stopwords, PoS tagging, lemmatization, etc. Specifically, nltk will throw an error if the required data is not installed. You can use the following Python statement (in Linux terminal or in a code editor) to open the NLTK downloader and select the desired package(s) to install:
nltk.download("all")
fill free to improve this code