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Build an NLP model to perform sentiment analysis on a dataset of your choice (e.g., movie reviews). Submit your code AND a separate 1-page report on your approach and results.

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Answered 2 days After Sep 15, 2024

Solution

Bhaumik answered on Sep 18 2024
7 Votes
3
Sentiment Analysis of IMDB Movie Reviews Using NLP Techniques
Willard Widma
Avila University
CS661- AI & Machine Learning
Willard Widma
9/14/2024
    
Introduction
Sentiment analysis, at times called opinion mining, is a natural language processing (NLP) function in which the emotive content of a text body is extracted. It is frequently used to interpret viewpoints from user-generated material, such as social media postings, consumer reviews, and other types of content. The main objective of sentiment analysis is to categorize the text's polarity into positive, negative, and neutral groups. To categorize movie reviews as either good or negative, this paper focuses on using binary sentiment analysis to the IMDB Movie Reviews Dataset.
The TensorFlow framework is used to create a deep-learning model for this purpose. The model was created to use fully connected layers for classification after embedding layers to convert textual data into numerical representations. We assessed the model's performance using accuracy and loss criteria. 50,000 reviews total—25,000 for training and another 25,000 for testing—are included in the IMDB dataset. The binary classification labels on these reviews make them a perfect dataset for sentiment analysis.
Methodology
· Data Pre-processing
Text input must be transformed into a numerical representation before being fed into a neural network model. The text was initially tokenized using TensorFlow's Tokenizer function, which gives each word a distinct number. To minimize computing costs, the vocabulary is restricted to the top 10,000 most frequently occu
ing terms in the dataset.
Using the pad_sequences function, each review was padded to provide a consistent 500-word length. Because neural networks require fixed-size inputs,...
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