OPINION MINING AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI

Name: OPINION MINING AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI
Brand: BALIGE PUBLISHING
SKU: 01920349

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1958732OPINION MINING AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUIhttps://www.gandhi.com.mx/opinion-mining-and-prediction-using-machine-learning-and-deep-learning-with-python-gui/phttps://gandhi.vtexassets.com/arquivos/ids/245007/1700338c-e5d0-4569-97ff-d2fb1547adaf.jpg?v=638333946470900000186186MXNBALIGE PUBLISHINGInStock/Ebooks/1920349OPINION MINING AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI186186https://www.gandhi.com.mx/opinion-mining-and-prediction-using-machine-learning-and-deep-learning-with-python-gui/phttps://gandhi.vtexassets.com/arquivos/ids/245007/1700338c-e5d0-4569-97ff-d2fb1547adaf.jpg?v=638333946470900000InStockMXN99999DIEbook20221230005536115_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1230005536115_Opinion mining (sometimes known as sentiment analysis or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.This dataset was created for the Paper From Group to Individual Labels using Deep Features, Kotzias et. al,. KDD 2015. It contains sentences labelled with a positive or negative sentiment. Score is either 1 (for positive) or 0 (for negative). The sentences come from three different websites/fields: imdb.com, amazon.com, and yelp.com.For each website, there exist 500 positive and 500 negative sentences. Those were selected randomly for larger datasets of reviews. Amazon: contains reviews and scores for products sold on amazon.com in the cell phones and accessories category, and is part of the dataset collected by McAuley and Leskovec. Scores are on an integer scale from 1 to 5. Reviews considered with a score of 4 and 5 to be positive, and scores of 1 and 2 to be negative. The data is randomly partitioned into two halves of 50, one for training and one for testing, with 35,000 documents in each set. IMDb: refers to the IMDb movie review sentiment dataset originally introduced by Maas et al. as a benchmark for sentiment analysis. This dataset contains a total of 100,000 movie reviews posted on imdb.com. There are 50,000 unlabeled reviews and the remaining 50,000 are divided into a set of 25,000 reviews for training and 25,000 reviews for testing. Each of the labeled reviews has a binary sentiment label, either positive or negative. Yelp: refers to the dataset from the Yelp dataset challenge from which we extracted the restaurant reviews. Scores are on an integer scale from 1 to 5. Reviews considered with scores 4 and 5 to be positive, and 1 and 2 to be negative. The data is randomly generated a 50-50 training and testing split, which led to approximately 300,000 documents for each set. Sentences: for each of the datasets above, labels are extracted and manually 1000 sentences are manually labeled from the test set, with 50 positive sentiment and 50 negative sentiment. These sentences are only used to evaluate our instance-level classifier for each dataset3. They are not used for model training, to maintain consistency with our overall goal of learning at a group level and predicting at the instance level.The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy.(*_*)1230005536115_In the context of sentiment analysis and opinion mining, this project began with dataset exploration. The dataset, comprising user reviews or social media posts, was examined to understand the sentiment labels distribution. This analysis provided insights into the prevalence of positive or negative opinions, laying the foundation for sentiment classification.To tackle sentiment classification, we employed a range of machine learning algorithms, including Support Vector, Logistic Regression, K-Nearest Neighbours Classiier, Decision Tree, Random Forest Classifier, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and Adaboost Classifiers. These algorithms were combined with different vectorization techniques such as Hashing Vectorizer, Count Vectorizer, and TF-IDF Vectorizer. By converting text data into numerical representations, these models were trained and evaluated to identify the most effective combination for sentiment classification.In addition to traditional machine learning algorithms, we explored the power of recurrent neural networks (RNNs) and their variant, Long Short-Term Memory (LSTM). LSTM is particularly adept at capturing contextual dependencies and handling sequential data. The text data was tokenized and padded to ensure consistent input length, allowing the LSTM model to learn from the sequential nature of the text. Performance metrics, including accuracy, were used to evaluate the models ability to classify sentiments accurately.Furthermore, we delved into Convolutional Neural Networks (CNNs), another deep learning model known for its ability to extract meaningful features. The text data was preprocessed and transformed into numerical representations suitable for CNN input. The architecture of the CNN model, consisting of embedding, convolutional, pooling, and dense layers, facilitated the extraction of relevant features and the classification of sentiments.Analyzing the results of our machine learning models, we gained insights into their effectiveness in sentiment classification. We observed the accuracy and performance of various algorithms and vectorization techniques, enabling us to identify the models that achieved the highest accuracy and overall performance. LSTM and CNN, being more advanced models, aimed to capture complex patterns and dependencies in the text data, potentially resulting in improved sentiment classification.Monitoring the training history and metrics of the LSTM and CNN models provided valuable insights. We examined the learning progress, convergence behavior, and generalization capabilities of the models. Through the evaluation of performance metrics and convergence trends, we gained an understanding of the models ability to learn from the data and make accurate predictions.Confusion matrices played a crucial role in assessing the models predictions. They provided a detailed analysis of the models classification performance, highlighting the distribution of correct and incorrect classifications for each sentiment category. This analysis allowed us to identify potential areas of improvement and fine-tune the models accordingly.In addition to confusion matrices, visualizations comparing the true values with the predicted values were employed to evaluate the models performance. These visualizations provided a comprehensive overview of the models classification accuracy and potential areas for improvement. They allowed us to assess the alignment between the models predictions and the actual sentiment labels, enabling a deeper understanding of the models strengths and weaknesses.Overall, the exploration of machine learning, LSTM, and CNN models for sentiment analysis and opinion mining aimed to develop effective tools for understanding public opinions. The results obtained from this project showcased the models performance, convergence behavior, and their ability to accurately classify sentiments. These insights can be leveraged by businesses and organizations to gain a deeper understanding of the sentiments expressed towards their products or services, enabling them to make informed decisions and adapt their strategies accordingly....1230005536115_BALIGE PUBLISHINGlibro_electonico_04e10a8b-3298-307f-837f-074785f3b471_1230005536115;1230005536115_1230005536115Rismon HasiholanInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/f2ca928b-958d-498f-bb70-f76c6615d8f6-epub-a8037e23-966e-4c3b-ad68-649d2865048f.epub2022-04-20T00:00:00+00:00BALIGE PUBLISHING