# TwIdw--A Novel Method for Feature Extraction from Unstructured Texts
## 2 Related Works
The performance measures of the proposed techniques were compared with the standard Tfldf technique. The results showed that the proposed techniques are comparable with Tfldf, and morphological analysis can improve the baseline Tfldf technique. The study suggests that the precision for fake news and recall for real news can be statistically significantly improved. In NLP classification tasks, machine learning and deep learning techniques have been extensively employed. Machine learning algorithms, such as support vector machines (SVM), decision trees, and random forests, have been utilized to build models that can classify text into predefined categories. In recent years, deep learning models have emerged as powerful tools for NLP classification tasks. Deep neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models, have shown remarkable performance and allowing for more accurate and robust classification. [PERSON] et al. (2018) compare multiple machine learning and deep learning models to identify suicidal thoughts on Twitter with the goal of achieving high accuracy to recognize early indications and prevent suicide attempts. The study used text pre-processing and feature extraction techniques, trained several models, and experimented on a dataset of 49,178 instances. The findings show that the random forest model achieves the highest classification score among machine learning algorithms with an accuracy of 93% and f1-score of 0.92. However, training deep learning classifiers with word embedding increases the performance of ML models, with the BiLSTM model achieving an accuracy of 93.6% and f1-score of 0.93. This means that the BiLSTM model is performing better than the random forest model. [PERSON] et al. (2018) proposed a model for detecting fake news using feature extraction and machine learning algorithms. The sorted news samples are sent to long short-term memory and classical machine learning algorithms for detection. The model performs better than benchmark models in detecting fake news. In the FakeNewsNet database, the proposed model had an AUC of 69%, and in the Liar database, the proposed model had an accuracy of 70%. [PERSON] and [PERSON] [24] focus on utilizing various machine learning models to detect fake news in COVID-19-related tweets. Gradient Boosting Classifier, Logistic Regression, random forest Classifier, and Decision Tree Classification models were employed to classify tweets as either \"Fake News\" or \"Not Fake News\" in relation to COVID-19. Among these models, Logistic Regression emerged as the best performer, exhibiting the highest f1-score of 93%. In conclusion, one of the most crucial issues in NLP research, due to its significant impact on society, is fake news detection. Various techniques have been proposed to address this problem, including classical machine learning methods and deep learning models. These methods typically rely on feature extraction and representation techniques. The methods in the analyzed literature have shown promising results, with some models achieving high accuracy and AUC scores in detecting fake news in various datasets. However, there is still room for improvement, and future research should focus on developing more effective feature extraction techniques and exploring new deep learning architectures to further enhance the performance of these models. Feature extraction plays a vital role in machine learning models, enabling them to understand and analyze unstructured texts. Developing new and innovative feature extraction techniques can lead to improved accuracy in various NLP tasks, such as sentiment analysis, text classification, named entity recognition, and fake news detection. By capturing more relevant and informative features from unstructured texts, models can make more accurate predictions and classifications.