EhabAshraf32 / Restaurent-Reviews-Using-NLP-and-Machine-Learning

Restaurent-Reviews-Using-NLP-and-Machine-Learning

Project Description:

I leveraged Natural Language Processing (NLP) and Machine Learning techniques to analyze user opinions in the context of restaurant reviews. The primary objective was to gain insights into customer sentiment and understand their preferences. By employing a combination of NLP methodologies and Machine Learning algorithms, I aimed to develop a model that could accurately classify reviews as positive or negative.

Dataset Description:

I utilized the Restaurant reviews dataset to accomplish this, which served as the foundation for training and evaluating the model. The dataset consisted of a vast collection of reviews from various restaurants, encompassing a wide range of customer experiences and sentiments.

I employed the NLTK (Natural Language Toolkit) library, a powerful tool in the field of NLP.

Preprocessing such as:

tokenization, stemming, and corpus management. The text data was further processed through techniques like word tokenization, conversion to lowercase, punctuation removal, and elimination of stop words. Additionally, I employed advanced methods such as Lemmatization and Stemming to enhance the quality and uniformity of the data.

Feature extraction:

CountVectorizer technique which transformed the textual data into numerical features, making it compatible with machine learning algorithms. This allowed me to effectively train the model on the preprocessed data.

Train the model:

To evaluate the model's performance, I used the naive_bayes algorithm, specifically the MultinomialNB variant, which is well-suited for text classification tasks. The accuracy_score metric was employed to measure the model's performance, providing insights into its effectiveness in predicting sentiment accurately.

By combining NLP methodologies, machine learning algorithms, and the Restaurant reviews dataset, I successfully developed a model capable of analyzing user sentiment and classifying reviews as positive or negative. This project not only showcased my expertise in NLP and Machine Learning but also demonstrated my ability to preprocess data, employ feature extraction techniques, and evaluate model performance effectively.