Path: blob/master/Natural Language Processing using Python/Sentiment Analysis .ipynb
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Title: Sentiment Analysis on Movie Reviews
1. Introduction
Explanation of sentiment analysis.
Importance of sentiment analysis in the movie industry.
Objective of the case study.
Data
Sample movie reviews data attached
3. Data Preprocessing
Cleaning the text data (removing special characters, punctuation
Tokenization: Breaking down the text into individual words or phrases.
Removing stop words: Commonly occurring words that carry little or no meaning.
Stemming or Lemmatization: Reducing words to their base or root form.
Vectorization: Converting text data into numerical format using techniques like Bag of Words, TF-IDF, or word embeddings.
4. Exploratory Data Analysis (EDA)
Distribution of sentiments (positive, neutral, negative) in the dataset.
Word cloud visualization of most frequent words in positive and negative reviews.
Analysis of review length distribution.
5. Model Building
Selection of machine learning or deep learning algorithms for sentiment analysis (e.g., Naive Bayes, Support Vector Machines, Recurrent Neural Networks).
Splitting the dataset into training and testing sets.
Training the models on the training data.
Evaluation of models using metrics such as accuracy, precision, recall, and F1-score.
6. Hyperparameter Tuning
Optimization of model performance by tuning hyperparameters.
Using techniques like GridSearchCV or RandomizedSearchCV for hyperparameter tuning.
7. Model Comparison and Selection
Comparison of performance metrics for different models.
Selection of the best-performing model based on evaluation metrics.