4 AI Engineers Available For July

4 AI Engineers Available For July

4 AI Engineers Available For July

Current Page

Sentiment Analysis of Movie Review Dataset

  • Modeling: TensorFlow, Keras, SimpleRNN

  • Data Processing and Management: Pandas, NumPy

  • Programming Language: Python

  • Web Deployment: Streamlit

This project focuses on analyzing sentiment in a dataset of 25,000 unique IMDB movie reviews, classifying them into Negative, Neutral, or Positive categories. The analysis involves deep understanding of the context, semantics, and sentiment expressed in the reviews. Advanced machine learning models including Simple RNN, GRU, and Bi-directional LSTM were developed and implemented to achieve this classification. By employing natural language processing techniques such as tokenization, lemmatization, stemming, punctuation removal, and stopwords elimination, the models reached an impressive overall accuracy of 92%. These results provide users with quick and reliable insights into the sentiment trends of movie reviews. The project not only showcased the effectiveness of different RNN architectures in handling text data but also highlighted their capabilities in sentiment analysis.

Keep reading

Finance

Finance AI Chatbot

Education Technology

AI-Powered Virtual Teaching Assistant

Legal Services

Streamlining Web Application Navigation with Conversational AI

Information Technology

Voice and Large Language Models (LLM)