New method uses word subspaces and term-frequency to improve text classification.
problem Lack of semantic meaning in bag-of-words features.
method Proposes word subspaces and term-frequency weighted word subspaces for text classification.
result Improved text classification performance compared to state-of-the-art algorithms.
Much work has been done on feature selection. Existing methods are based on document frequency, such as Chi-Square Statistic, Information Gain etc. However, these methods have two shortcomings: one is that they are not reliable for low-frequency terms, and the other is that they only count whether one term occurs in a …
Fuzzy clustering reduces text data dimensions effectively.
problem Sparsity and high dimensionality in document-term frequency matrices.
method Fuzzy clustering applied as a new unsupervised feature transformation method.
result Fuzzy clustering outperforms PCA and SVD in reducing text data dimensions.
Study detects emerging trends in financial news articles about Microsoft.
problem Challenges in identifying trends in long-form financial news articles.
method Topic modeling and term frequency for keyword similarity analysis.
result Demonstrates the influence of the pandemic on Microsoft.
Detection of emerging topics are now receiving renewed interest motivated by the rapid growth of social networks. Conventional term-frequency-based approaches may not be appropriate in this context, because the information exchanged are not only texts but also images, URLs, and videos. We focus on the social aspects of…
A new term weighting scheme TF-IDFC-RF outperforms others in sentiment analysis.
problem Improving text classification in sentiment analysis.
method Proposes a novel supervised term weighting scheme TF-IDFC-RF and compares it with other schemes.
result TF-IDFC-RF outperforms all other schemes on two sentiment analysis datasets.
Archetypal analysis represents a set of observations as convex combinations of pure patterns, or archetypes. The original geometric formulation of finding archetypes by approximating the convex hull of the observations assumes them to be real valued. This, unfortunately, is not compatible with many practical situations…
This paper identifies duplicate questions on Quora using machine and deep learning models.
problem Detecting semantically identical questions on Quora to improve user experience.
method Applied machine learning and deep learning techniques on Quora's dataset.
result Xgboost model with character level term frequency and inverse term frequency achieved 85.82% accuracy.
Textual data predicts electricity consumption and weather.
problem Lack of textual data in time series prediction models.
method Used TF-IDF and neural word embeddings to predict time series from text.
result Textual data can predict time series with sufficient accuracy.
Automated theorem proving without imitation, using tf-idf for premise selection.
problem Automated theorem proving in large knowledge bases without human guidance.
method Exploration mechanism mixing tf-idf selected premises in a deep reinforcement learning context.
result The prover trained with this approach outperforms provers trained only on human proofs.
Deep autoencoders improve health tweet clustering.
problem Accurate clustering of health-related tweets.
method Deep convolutional autoencoders for learning compact tweet representations.
result Clustering performance significantly outperforms conventional methods.
Automated sentiment analysis and opinion mining is a complex process concerning the extraction of useful subjective information from text. The explosion of user generated content on the Web, especially the fact that millions of users, on a daily basis, express their opinions on products and services to blogs, wikis, so…
This work reduces the dimensionality of text data using SVD, improving performance and computational efficiency.
problem High-dimensional input spaces in text classification lead to excessive parameter count and computational infeasibility.
method Singular Value Decomposition (SVD) is applied to transform the input space into a lower-dimensional latent space.
result Neural networks trained on the lower-dimensional latent space achieve comparable or better performance with reduced computational complexity.
Improves topic modeling for multi-collection corpora.
problem Challenges in text mining from multi-collection corpora.
method Compound Latent Dirichlet Allocation (cLDA) model and MCMC methods.
result cLDA model identifies topic proportions across multiple collections.
Comparative study of neural networks for short-term FOREX forecasting.
problem Simulating expert judgment in foreign exchange market forecasting.
method Implemented and compared LSTM and ANN architectures for short-term FOREX forecasting.
result ANN custom architecture outperforms LSTM in prediction quality and resource efficiency.
Project analyzes drug reviews to predict ratings using machine learning.
problem Predicting drug ratings from text reviews.
method Implemented supervised machine learning algorithms with TFIDF and Count Vectors.
result Good results in predicting test data sets for popular conditions.
Models predict Alzheimer's Dementia from spontaneous speech with high accuracy.
problem Early diagnosis of Alzheimer's Dementia (AD) through spontaneous speech analysis.
method Compared natural language processing techniques including SVM, GBDT, CRFs, and Transformer-based models.
result Top models achieve 0.81-0.82 test set scores for AD vs controls and 4.58 RMSE for Mental Mini State Exam scores.
Novel spam filter improves e-mail classification accuracy.
problem Uneven class distribution, unequal error cost, frequent content change, personalized discrimination.
method TFDCR feature selection, incremental learning, dynamic feature update.
result TFDCR outperforms in feature selection, incremental model improves classification accuracy.
Comment classification on cookery channels using BERT and traditional models.
problem Volume of multilingual comments, variable lengths, slang, symbols, and abbreviations make comment classification challenging.
method Evaluated traditional machine learning models (Naive Bayes, KNN, SVM, Random Forest, Decision Trees) and BERT-based models (BERT, DISTILBERT, XLM) for multilingual comment classification.
result XLM was the top-performing BERT model with an accuracy of 67.31, while Random Forest with Term Frequency Vectorizer was the best traditional model with 63.59 accuracy.
GANs visualize malware behavior for proactive protection.
problem Malware authors' advantage in testing and augmenting malicious code.
method GAN trained on distributed image representation of malware behaviors.
result Generated synthetic malware for adversarial training of anti-malware models.