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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align=\"center\"\u003eSentiment Analysis Using Recurrent Neural Networks\u003c/h1\u003e\n\u003ch2 align=\"center\"\u003eIntroduction\u003c/h2\u003e\n\n\u003cimg  align=\"right\" height=\"290\" src=\"https://www.aimtechnologies.co/wp-content/uploads/2023/08/Arabic-Sentiment-Analysis-2.jpg\" /\u003e\n\n- 👨‍💻This project implements a sentiment analysis model for movie reviews using RNNs built with Python's libraries.\n\n-  🔭 Sentiment analysis, also known as opinion mining, is a subfield of Natural Language Processing (NLP) that aims to automatically detect the emotional tone of a piece of text.\n\n- ⚡This project demonstrates proficiency in deep learning, data manipulation, text preprocessing, NLP, model building, evaluation and handling sequential data.\n\n\n\u003ch2 align=\"center\"\u003eRequirements\u003c/h2\u003e\n\n- Python 3.x\n- pandas\n- numpy\n- Regular expressions (re)\n- NLTK\n- Scikit-learn\n- Tensorflow\n- Scipy\n\n\n  \n\u003ch2 align=\"center\"\u003eText Preprocessing\u003c/h2\u003e\n\n- After loading the data, perform preprocessing techniques like HTML tag removal, punctuation removal, lowercasing, stop word removal using libraries nltk, re and spacy.\n\n- Make Tokens for the preprocessed text using Tokenizer. The Tokenizer class from Keras tokenizes the text, converting words into sequences of integer indices.\n\n- Sequences are padded with zeros to ensure a fixed length for all reviews using pad_sequences.\n\n- Sentiment labels (\"positive\" and \"negative\") are converted to numerical labels (0 and 1) using LabelEncoder.\n\n\u003ch2 align=\"center\"\u003eModel Building and Training \u003c/h2\u003e\n\n- Complex Bidirectional LSTM-GRU has been used in model building. This is a more complex model with multiple stacked Bidirectional LSTM and GRU layers with dropout regularization. Total trainable parameters were 37379651.\n\n- The data is split into training and testing sets using train_test_split.\n- Each model is compiled with the Adam optimizer, binary cross-entropy loss function, and accuracy metric.\n- Models are trained for a specified number of epochs and batch size.\n- Training and validation accuracy and loss are monitored during training.\n  \n\u003ch2 align=\"center\"\u003eWorking\u003c/h2\u003e\n\n- The code demonstrates how to use the trained model to predict the sentiment of new movie reviews.\n\n- Text pre-processing steps are applied to the new review text given as input.\n- The preprocessed text is converted to a sequence of integers using the fitted tokenizer.\n- The sequence is padded to match the maximum review length.\n- The model predicts the sentiment probability (positive or negative) for the review.\n- Based on the predicted probability, the review is classified as \"flop movie\", \"average movie\", \"good movie\", or \"blockbuster movie\".\n- Overall this this code provides a basic framework for sentiment analysis using RNNs. You can experiment with different RNN architectures, hyperparameters.\n\n\u003ch2 align=\"center\"\u003eUse Cases\u003c/h2\u003e\n\n- Analyze customer reviews and social media posts to understand customer satisfaction with products, services, and brand interactions.\n- Identify spam emails by analyzing the language and sentiment used in the message content.\n- Track brand sentiment across social media platforms to identify potential issues or crises early on.\n- Analyze employee satisfaction surveys and feedback forms to identify potential morale issues or areas for improvement in the workplace.\n- Analyze student feedback on online courses to understand their learning experience and identify areas for improvement in course content and delivery.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabeed04%2Fsentiment-analysis-using-recurrent-neural-networks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabeed04%2Fsentiment-analysis-using-recurrent-neural-networks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabeed04%2Fsentiment-analysis-using-recurrent-neural-networks/lists"}