Sentiment Analysis and Classification: Extracting Insights from Textual Feedback using NLP
In this project, I developed a machine learning model to predict the sentiment (positive or negative) of a given list of reviews using NLP (Natural Language Processing) techniques. The training dataset consisted of labeled reviews from various sources such as Amazon, IMDb, and Yelp. (To handle unlabeled data, a scoring method like SentimentIntensityAnalyzer can be implemented.)
To prepare the data for modeling, I performed preprocessing tasks including data cleaning, stop word removal, eliminating unnecessary characters or symbols based on an extended collection, tokenization, and vectorization. Leveraging the power of XGBoost, I trained the model to accurately classify the sentiment of reviews by recognizing distinguishing features.
The model's performance was evaluated using the AUC (Area Under the Curve) score, providing insights into its ability to differentiate between positive and negative sentiment. This automated approach enables efficient sentiment analysis for customer reviews, social media monitoring, and more.
By harnessing the capabilities of NLP and machine learning, this project offers an efficient and accurate solution for automated sentiment analysis and classification of reviews, enabling quick and accurate analysis of large volumes of text data.