This undertaking analyzes ebook opinions utilizing sentiment evaluation and predicts rankings. By using pure language processing (NLP) and deep studying methods, it processes evaluate datasets to establish sentiment (optimistic, destructive, impartial) and predict rankings. This may profit industries like publishing, e-commerce, and advice programs. For extra info, please test the GitHub repository. The hyperlink is offered beneath.
Case: Book Review Analysis and Prediction Model
Normal Construction
The undertaking consists of the next key elements:
- Information Preprocessing: Reads and processes evaluate texts and metadata (rankings, summaries, useful votes). Textual content knowledge is cleaned, tokenized, and ready for deep studying fashions.
- Information Visualization: Makes use of graphical instruments to discover dataset traits, equivalent to score distributions and evaluate lengths.
- Mannequin Improvement: Applies NLP and deep studying (e.g., LSTM) for sentiment evaluation and score prediction.
- Mannequin Coaching and Analysis: Splits the dataset for coaching/testing and evaluates the mannequin’s accuracy and loss.
The undertaking is a helpful place to begin for researchers working with textual content knowledge and predictive fashions.
Programming Language:
Information Processing:
pandasfor knowledge manipulationjsonfor dealing with JSON knowledge
Visualization:
seaborn,matplotlib.pyplot
Machine Studying & Deep Studying:
scikit-learnfor dataset splittingtensorflow.kerasfor mannequin improvement (e.g.,Tokenizer,LSTM)
Textual content Processing: re for textual content cleansing
