This repo contains Sentiment Analysis with direct data from dataset, Sentiment Analysis with direct data from dataset with cross validation and gridsearchcv
Sentiment Analysis with data from dataset after cleaning with cross validation and gridsearchcv is under working
Dataset: https://www.kaggle.com/datasets/thedevastator/amazon-product-reviews?select=train.csv
The Amazon Reviews Polarity Dataset discloses eighteen years of customers' ratings and reviews from Amazon.com, offering an unparalleled trove of insight and knowledge. Drawing from the immense pool of over 35 million customer reviews, this dataset presents a broad spectrum of customer opinions on products they have bought or used. This invaluable data is a gold mine for improving products and services as it contains comprehensive information regarding customers' experiences with a product including ratings, titles, and plaintext content. At the same time, this dataset contains both customer-specific data along with product information which encourages deep analytics that could lead to great advances in providing tailored solutions for customers. Has your product been favored by the majority? Are there any aspects that need extra care? Use Amazon Reviews Polarity to gain deeper insights into what your customers want - explore now!
Here, the model was trained on 10000 data from train.csv and 10000 data from test.csv. 2 vectorizers namely "CountVectorizer" and "TF-IDF Vectorizer" were used and Also 3 Machine Learning Algorithms namely Logistic Regression, Random Forest and SVM were used and a comparision of the 3 is performed. A Barplot was plotted with precision and accuracy of each algorithm for a better visual understanding of the performance of each algorithm and also a tabular represention of the final output is also shown.
once after getting the required output, the ouput of each model is optimised by cross validation and gridsearchcv.