# Current Approaches and Applications in Natural Language Processing
## 2 Review of Issue Contents
### Text Classification  
Text classification is still a major concern in NLP research. Several contributions are related to this topic. For example, ref. [1] predict whether a patient had been diagnosed with a mental disorder and, if so, the specific mental disorder type. LIWC, spaCy, fastText, and RobBERT were used to analyze Dutch psychiatric interview transcriptions. LIWC, in combination with the random forest classification algorithm, performed the best in predicting whether a person had a mental disorder or not. SpaCy, in combination with random forest, best predicted which particular mental disorder a patient had been diagnosed with. When studying the results obtained with RobBERT and fastText, it was found, by applying LIME analysis, that the difference between mental disorder and no disorder was more prevalent in the manner of speaking than in the topics or the semantic content. Again, classical ML techniques such as Random Forest are still very useful. Multimodal approaches are also present. In [2], a novel approach to fuse textual and visual features using a scaled dot-product attention mechanism is proposed. This is used in a multimodal classification system applied in fake news detection. The attention mechanism allows fine-grained combination of both visual embeddings and word embeddings taken from the image and text found in posts. The system achieves competitive results on the Weibo dataset. Another paper studies the automatic detection of misogyny in web content by building an annotated corpus from several sources and then training a system for classifying texts [3]. The system is based on BERT embeddings and a final linear regression classifier. The results are good, although not comparable to other systems. A major contribution is the way in which the corpus is generated, which can allow for augmenting training datasets on misogyny detection. Text classification can also be used to classify types of texts at high-level semantics. For instance, ref. [4] explores different machine learning and neural network techniques for the classification of strings as problems, non-problems, solutions, and non-solutions. The algorithm that provided the best results was a convolutional neural network. To addresses the detection of fake news, the authors of [5] present a solution based on three steps: stance detection, author credibility verification, and machine learning classification. Stance detection verifies the relevance between the title and paragraphs of a news item; if there is a match, the next module checks whether the author is authentic to determine whether the news item should be believed or not. Finally, machine learning algorithms are used to classify the news item. Text classification can also be applied to user profiling. A proposal for personality recognition relying on the dominance, influence, steadiness, and compliance (DISC) model together with a Bag-of-Words model of language is presented in [6]. Classical machine learning algorithms such as AdaBoost and Random Forests achieved good performance. Topic detection is still stimulating research. Ref. [7] applies BERT word embeddings and a classical clustering algorithm (spherical k-means) to assign documents to topics. The proposal encodes documents as a linear combination of word embeddings and word frequencies in the document. Topics have been previously identified using the spherical k-means algorithm over all word embeddings in the corpus. Finally, documents are associated with topics using cosine distance. This method outperforms other approaches such as PLSA (Probabilistic Latent Semantic Analysis) and do not need to fine-tune the deep learning model. In addition to systems and methods, this Special Issue includes some overviews. Related to this topic, ref. [8] provides a review on corpora related to deception detection on several approaches to the study of deception and on previous research into itslinguistic detection. Moreover, the author explores the linguistic cues of deception in the Spanish language.