In the modern digital era, vast amounts of user-generated content such as online reviews are continuously produced across various platforms. Extracting meaningful insights from this unstructured textual data is a challenging and time-consuming task. This paper presents an IMDB Reviews Sentiment Analyzer that utilizes Natural Language Processing (NLP) techniques to classify user reviews into positive, negative, or neutral sentiments.

The system integrates sentiment analysis, text summarization, visualization, and data storage into a unified platform. TextBlob is used for sentiment classification, while transformer-based models are employed for summarization. The system is implemented using Python and Streamlit, providing an interactive user interface. Experimental results demonstrate that the system effectively analyzes textual data and provides meaningful insights through visual representations such as word clouds and sentiment distribution charts.

Keywords—Sentiment Analysis, Natural Language Processing, TextBlob, Streamlit, IMDB Reviews, Text Summarization, Machine Learning