Amazon-Customer-Sentiment-Analysis-Using-Transformers

Data Preprocessing and Sentiment Analysis

Used Libraries for Data Preprocessing, Sentiment Analysis, and Machine Learning:

Utilized NLTK (Natural Language Toolkit) and Sklearn (Scikit-learn) libraries.

Employed these libraries for text preprocessing, sentiment analysis, and machine learning model development.

Performed Text Preprocessing and Sentiment Analysis:

Preprocessed text data by converting to lowercase, tokenizing, stemming, and removing stop words.

Utilized the VADER sentiment analysis tool to calculate sentiment scores for each review.

Defined sentiment labels (positive, negative, neutral) based on VADER scores.

Limited the dataset to the first 100,000 rows for analysis.

Trained Support Vector Machine (SVM) Classifier for Sentiment Prediction:

Split the dataset into training and testing sets for model evaluation.

Applied TF-IDF vectorization for feature extraction.

Trained an SVM classifier using the TF-IDF features.

Predicted sentiment labels using the trained SVM model.

Achieved accuracy measurement for the SVM classifier on the testing set.

Libraries Used:

NLTK (Natural Language Toolkit)

Pandas

Sklearn (Scikit-learn)