Project Title: Tripadvisor Hotel Review Sentiment Analysis using LSTM Neural Network
Project Description: In this project, I utilized the TripAdvisor Hotel Review dataset from Kaggle to perform sentiment analysis on hotel reviews. The main objective was to build a predictive model using LSTM (Long Short-Term Memory) neural networks to classify hotel reviews as positive or negative based on their textual content.
Steps Performed:
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Data Collection: I obtained the TripAdvisor Hotel Review dataset from Kaggle, which includes hotel reviews, ratings, and other relevant information.
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Data Cleaning: Preprocessing and cleaning the raw data were essential to remove any inconsistencies, noise, or irrelevant information. I handled missing values, removed special characters, and standardized text to make it ready for analysis.
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Data Visualization: I conducted exploratory data analysis to gain insights into the dataset. Utilizing data visualization libraries, I visualized the distribution of review ratings, the most common positive and negative words, and any other patterns present in the data.
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Text Preprocessing: I performed various text preprocessing techniques such as tokenization, stop-word removal, lowercasing, and stemming/lemmatization to prepare the text data for modeling.
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Natural Language Toolkit (NLTK): I leveraged the NLTK library in Python to further process the textual data. This involved tasks like sentiment analysis, part-of-speech tagging, and word frequency analysis.
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LSTM Neural Network: LSTM is a type of recurrent neural network well-suited for sequential data like text. I designed and trained an LSTM model using TensorFlow/Keras to classify hotel reviews as positive or negative based on their sentiment.
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Data Modeling: I split the dataset into training and testing sets to evaluate the performance of the LSTM model. I tuned hyperparameters to optimize the model's accuracy and prevent overfitting.
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Evaluation: I assessed the model's performance using metrics like accuracy, precision, recall, and F1-score. Additionally, I visualized the model's performance through a confusion matrix to understand false positives and false negatives.
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Prediction: After successfully training and evaluating the LSTM model, I used it to predict the sentiment of unseen hotel reviews.
Conclusion: The project aimed to build a robust sentiment analysis model to classify hotel reviews as positive or negative. By leveraging the TripAdvisor Hotel Review dataset, performing data cleaning, visualization, and implementing an LSTM neural network, I achieved an accurate and efficient prediction system. The model's insights could be beneficial for hotel management to understand customer sentiments and make data-driven decisions to enhance guest experiences.