Spanish Tweets Sentiment Analysis of based on Supervised Learning Techniques

Trabajo de Fin de Grado en Ingeniería Informática / Ingeniería de Computadores, Facultad de Informática UCM, Departamento de Sistemas Informáticos y Computación, Curso 2020/2021Durante los últimos años, plataformas tales como Twitter o Facebook, han ganado fuerza y el número de personas que las usan ha crecido exponencialmente. En el año 2021 se envían de media alrededor de 500 millones de Tweets al día Twitter (2020). En este contexto, con tal cantidad de información, y gracias al aumento de la capacidad de procesamiento, muchas empresas, atraídas por las ventajas que conllevaba, han optado por minar información de estas redes sociales. Esto hace que el campo del Análisis de Sentimientos cobre gran importancia a la hora de obtener lo que la gente piensa acerca de algo. Este trabajo consiste en, dado un dataset de Tweets en castellano etiquetado con dos categorías (positiva y negativa) según el sentimiento del tweet, añadir una categoría más para ampliar la información que pudiera extraerse de los datos. En este paso de añadir la nueva categoría, se utiliza un etiquetador automático. Dado que los etiquetadores VADER y Textblob funcionan solo con textos en inglés, se hace una traducción con los traductores de Google y de Bing. Es decir, se prueban las cuatro combinaciones, y la mejor de estas es la que se usa finalmente para etiquetar esta nueva categoría. A continuación, se comprueba cual es el mejor número de palabras a la hora de crear la matriz TF-IDF, y a continuación se transforma el corpus a dicha matriz. Finalmente, se analiza una serie de clasificadores para obtener uno que sea capaz de predecir lo mejor posible la categoría a la que pertenece un tweet. Los resultados fueron los siguientes: la mejor combinación traductor-etiquetador fue el traductor de bing y el etiquetador VADER, obteniendo solo un 39,4% de tweets etiquetados como neutros, y un 67% de acierto con el dataset original. El mejor clasificador fue el Descenso del Gradiente Estocástico, el cual obtuvo una accuracy del 75,94%, una precision media del 76,53% y un recall del 74,5%.In the last few years, platforms like Twitter or Facebook have become more important and people using them has increased exponentially. In 2021 was sent an average of 500 million twetts every day Twitter (2020). In this context, with so much information and proccesing increased, many companies have started minning social media due to the many advantages it has. This has caused an increase on the importance of Sentiment Analysis when you want to know what people thinks about a topic. Using a dataset of spanish tweets with two labels(positive and negative), in this paperwork we are going to classify another label to extract more information from the data. To add this new label an authomatic labeling was used. Due to the fact that Valence Aware Dictionary y Sentiment Reasoner (VADER) and TextBlob labeling works only on English text, a previous translation was made using Google and Bing translators. In other words, four different combinations was tested and only the best one was chosen to add the new label. The next step was to find out the best number of words using Term Frecuency (TF)-Inverse Document Frecuency (IDF) matrix, consequently transforming the corpus using that matrix. Finally, several classifiers were tested to get the best one predicting the correct tweet labels. The results obtained were the following ones: The best translator-labeler combination was Bing and VADER respectively, labeling 39.4% of the original dataset as neutral and having 67% accuracy when excluding neutral tweets. The best classifier was Stochastic Gradient Descent with 75.94% accuray, 76.53% precision on average and 74.5% recall.Depto. de Sistemas Informáticos y ComputaciónFac. de InformáticaTRUEsubmitte

Increased exploratory activity in rats with deficient sensorimotor gating: a study of schizophrenia-relevant symptoms with genetically heterogeneous NIH-HS and Roman rat strains

Schizophrenia involves positive, negative and cognitive symptoms, as well as comorbidity with anxiety and obsessive-compulsive disorder. Prepulse inhibition (PPI) of the startle response is a measure of sensorimotor gating that is impaired in schizophrenia and animal models of the disease. Remarkably, impaired PPI has been related to other schizophrenia-like features in rodent models, such as cognitive deficits and hyperactivity. However, it remains to be investigated whether deficient PPI and increased exploratory activity are associated in genetically heterogeneous (outbred) naïve animals. This study was undertaken to evaluate the relationships among PPI and other schizophrenia-related symptoms, such as augmented exploratory activity, anxiety and compulsivity in the genetically heterogeneous (outbred) NIH-HS rat stock (HS) and in the genetically-selected inbred Roman High-Avoidance (RHA) and Low-avoidance (RLA) rats. Animals underwent the following tests: open-field (exploratory activity), elevated zero-maze (anxiety-like behavior), marble burying (compulsive-like behavior), and PPI. Three groups of HS rats were formed according to their PPI scores, i.e. Low-PPI, Medium-PPI and High-PPI. The HS Low-PPI group displayed higher exploratory activity in the open-field than the HS Medium-PPI and HS High-PPI groups. Likewise, compared with their RLA counterparts, RHA rats exhibited lower PPI and more intense exploratory activity in the open-field test. Correlational and factorial analyses of the whole HS sample and the RHA/RLA data globally corroborated the results of the PPI-stratified HS subgroups. These data suggest that such a consistent association between impaired PPI and increased exploratory activity in outbred HS and inbred RHA/RLA rats is a relevant parameter that must be taken into account when modeling clusters of schizophrenia-relevant symptoms

Volumetric brain differences between the Roman rat strains: Neonatal handling effects, sensorimotor gating and working memory

The present work was devoted to evaluate whether the differences between the Roman high-(RHA) and low-avoidance (RLA) rat strains in novelty-induced behavioural inhibition/disinhibition, sensorimotor gating (prepulse inhibition, PPI) and spatial learning/memory are paralleled by differences in the volume of relevant brain areas (measured through magnetic resonance image, MRI) related to these behavioural phenotypes. To that purpose, we conducted two experiments. Experiment 1 involved testing adult rats from both strains, either untreated (controls) or treated with neonatal handling (NH; administered during the first 21 days of life), in a novel object exploration test (NOE), in the elevated zero-maze test (ZM) of anxiety and in a PPI test, as well as measuring the volume of limbic and cortical brain regions (amygdala -Am-, hippocampus -Hc-, striatum -St-, medial prefrontal cortex -mPFc-, anterior cingulate cortex -ACC-, nucleus accumbens -NAc-, lateral ventricles -LV-). Experiment 2 consisted in submitting rats to NOE and PPI tests, and to several spatial learning/memory tasks using the Morris water maze. RHA rats show higher exploration of the novel object in the NOE test, lowered anxiety in the ZM and impaired PPI compared to RLA rats. RLAs display better spatial reference learning and memory. The results revealed that the RLA strain shows greater Hc, Am and mPFc volume than its RHA counterpart, whereas the latter presents dramatically enlarged lateral ventricles. NH treatment markedly enhanced NOE test exploration in RLA rats, improved PPI in RHA rats and impaired it in the RLA strain, and produced beneficial effects on spatial working memory mainly in RHA rats. NH treatment decreased Hc and Am volume in the RLA strain. The results are discussed in terms of the possible relationships of strain-related brain volumetric differences and the behavioral (anxiety-related and schizophrenia-relevant) traits differentiating both rat strains, and highlighting the novel findings that NH, an anxiety/stressreducing treatment, is for the first time shown to enduringly reduce Hc and Am volume in parallel to the decrease of anxiety and the impairment of sensorimotor gating in RLA rats

Three-Dimensional Bioprinting of Organoid-Based Scaffolds (OBST) for Long-Term Nanoparticle Toxicology Investigation

The toxicity of nanoparticles absorbed through contact or inhalation is one of the major concerns for public health. It is mandatory to continually evaluate the toxicity of nanomaterials. In vitro nanotoxicological studies are conventionally limited by the two dimensions. Although 3D bioprinting has been recently adopted for three-dimensional culture in the context of drug release and tissue regeneration, little is known regarding its use for nanotoxicology investigation. Therefore, aiming to simulate the exposure of lung cells to nanoparticles, we developed organoid-based scaffolds for long-term studies in immortalized cell lines. We printed the viscous cell-laden material via a customized 3D bioprinter and subsequently exposed the scaffold to either 40 nm latex-fluorescent or 11–14 nm silver nanoparticles. The number of cells significantly increased on the 14th day in the 3D environment, from 5 × 105 to 1.27 × 106, showing a 91% lipid peroxidation reduction over time and minimal cell death observed throughout 21 days. Administered fluorescent nanoparticles can diffuse throughout the 3D-printed scaffolds while this was not the case for the unprinted ones. A significant increment in cell viability from 3D vs. 2D cultures exposed to silver nanoparticles has been demonstrated. This shows toxicology responses that recapitulate in vivo experiments, such as inhaled silver nanoparticles. The results open a new perspective in 3D protocols for nanotoxicology investigation supporting 3Rs

Conservation of Phenotypes in the Roman High- and Low-Avoidance Rat Strains After Embryo Transfer

The Roman high- (RHA-I) and low-avoidance (RLA-I) rat strains are bi-directionally bred for their good versus non-acquisition of two-way active avoidance, respectively. They have recently been re-derived through embryo transfer (ET) to Sprague-Dawley females to generate specific pathogen free (SPF) RHA-I/RLA-I rats. Offspring were phenotyped at generations 1 (G1, born from Sprague-Dawley females), 3 and 5 (G3 and G5, born from RHA-I and RLA-I from G2-G4, respectively), and compared with generation 60 from our non-SPF colony. Phenotyping included two-way avoidance acquisition, context-conditioned fear, open-field behaviour, novelty-seeking, baseline startle, pre-pulse inhibition (PPI) and stress-induced increase in plasma corticosterone concentration. Post-ET between-strain differences in avoidance acquisition, context-conditioned freezing and novelty-induced self-grooming are conserved. Other behavioural traits (i.e. hole-board head-dipping, novel object exploration, open-field activity, startle, PPI) differentiate the strains at G3-G5 but not at G1, suggesting that the pre-/post-natal environment may have influenced these co-segregated traits at G1, though further selection pressure along the subsequent generations (G1-G5) rescues the typical strain-related differences