Machine Learning Methods for Finding Textual Features of Depression from Publications

Depression is a common but serious mood disorder. In 2015, WHO reports about 322 million people were living with some form of depression, which is the leading cause of ill health and disability worldwide. In USA, there are approximately 14.8 million American adults (about 6.7% percent of the US population) affected by major depressive disorder. Most individuals with depression are not receiving adequate care because the symptoms are easily neglected and most people are not even aware of their mental health problems. Therefore, a depression prescreen system is greatly beneficial for people to understand their current mental health status at an early stage. Diagnosis of depressions, however, is always extremely challenging due to its complicated, many and various symptoms. Fortunately, publications have rich information about various depression symptoms. Text mining methods can discover the different depression symptoms from literature. In order to extract these depression symptoms from publications, machine learning approaches are proposed to overcome four main obstacles: (1) represent publications in a mathematical form; (2) get abstracts from publications; (3) remove the noisy publications to improve the data quality; (4) extract the textual symptoms from publications. For the first obstacle, we integrate Word2Vec with LDA by either representing publications with document-topic distance distributions or augmenting the word-to-topic and word-to-word vectors. For the second obstacle, we calculate a document vector and its paragraph vectors by aggregating word vectors from Word2Vec. Feature vectors are calculated by clustering word vectors. Selected paragraphs are decided by the similarity of their distances to feature vectors and the document vector to feature vectors. For the third obstacle, one class SVM model is trained by vectored publications, and outlier publications are excluded by distance measurements. For the fourth obstacle, we fully evaluate the possibility of a word as a symptom according to its frequency in entire publications, and local relationship with its surrounding words in a publication

Discourse change detection in diachronic text collections with synthetic datasets and neural networks

Discourse dynamics is one of the important fields in digital humanities research. Over time, the perspectives and concerns of society on particular topics or events might change. Based on the changing in popularity of a certain theme different patterns are formed, increasing or decreasing the prominence of the theme in news. Tracking these changes is a challenging task. In a large text collection discourse themes are intertwined and uncategorized, which makes it hard to analyse them manually. The thesis tackles a novel task of automatic extraction of discourse trends from large text corpora. The main motivation for this work lies in the need in digital humanities to track discourse dynamics in diachronic corpora. Machine learning is a potential method to automate this task by learning patterns from the data. However, in many real use-cases ground truth is not available and annotating discourses on a corpus-level is incredibly difficult and time-consuming. This study proposes a novel procedure to generate synthetic datasets for this task, a quantitative evaluation method and a set of benchmarking models. Large-scale experiments are run using these synthetic datasets. The thesis demonstrates that a neural network model trained on such datasets can obtain meaningful results when applied to a real dataset, without any adjustments of the model

Detecting and Monitoring Hate Speech in Twitter

Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the possible negative impact that these messages can have on individuals or on the society. In this paper, we present HaterNet, an intelligent system currently being used by the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that identifies and monitors the evolution of hate speech in Twitter. The contributions of this research are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification approaches based on different document representation strategies and text classification models. (4) The best approach consists of a combination of a LTSM+MLP neural network that takes as input the tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the literatureThe work by Quijano-Sanchez was supported by the Spanish Ministry of Science and Innovation grant FJCI-2016-28855. The research of Liberatore was supported by the Government of Spain, grant MTM2015-65803-R, and by the European Union’s Horizon 2020 Research and Innovation Programme, under the Marie Sklodowska-Curie grant agreement No. 691161 (GEOSAFE). All the financial support is gratefully acknowledge

Multilingual opinion mining

170 p.Cada día se genera gran cantidad de texto en diferentes medios online. Gran parte de ese texto contiene opiniones acerca de multitud de entidades, productos, servicios, etc. Dada la creciente necesidad de disponer de medios automatizados para analizar, procesar y explotar esa información, las técnicas de análisis de sentimiento han recibido gran cantidad de atención por parte de la industria y la comunidad científica durante la última década y media. No obstante, muchas de las técnicas empleadas suelen requerir de entrenamiento supervisado utilizando para ello ejemplos anotados manualmente, u otros recursos lingüísticos relacionados con un idioma o dominio de aplicación específicos. Esto limita la aplicación de este tipo de técnicas, ya que dicho recursos y ejemplos anotados no son sencillos de obtener. En esta tesis se explora una serie de métodos para realizar diversos análisis automáticos de texto en el marco del análisis de sentimiento, incluyendo la obtención automática de términos de un dominio, palabras que expresan opinión, polaridad del sentimiento de dichas palabras (positivas o negativas), etc. Finalmente se propone y se evalúa un método que combina representación continua de palabras (continuous word embeddings) y topic-modelling inspirado en la técnica de Latent Dirichlet Allocation (LDA), para obtener un sistema de análisis de sentimiento basado en aspectos (ABSA), que sólo necesita unas pocas palabras semilla para procesar textos de un idioma o dominio determinados. De este modo, la adaptación a otro idioma o dominio se reduce a la traducción de las palabras semilla correspondientes

A Machine Learning Approach to Predicting Alcohol Consumption in Adolescents From Historical Text Messaging Data

Techniques based on artificial neural networks represent the current state-of-the-art in machine learning due to the availability of improved hardware and large data sets. Here we employ doc2vec, an unsupervised neural network, to capture the semantic content of text messages sent by adolescents during high school, and encode this semantic content as numeric vectors. These vectors effectively condense the text message data into highly leverageable inputs to a logistic regression classifier in a matter of hours, as compared to the tedious and often quite lengthy task of manually coding data. Using our machine learning approach, we are able to train a logistic regression model to predict adolescents\u27 engagement in substance abuse during distinct life phases with accuracy ranging from 76.5% to 88.1%. We show the effects of grade level and text message aggregation strategy on the efficacy of document embedding generation with doc2vec. Additional examination of the vectorizations for specific terms extracted from the text message data adds quantitative depth to this analysis. We demonstrate the ability of the method used herein to overcome traditional natural language processing concerns related to unconventional orthography. These results suggest that the approach described in this thesis is a competitive and efficient alternative to existing methodologies for predicting substance abuse behaviors. This work reveals the potential for the application of machine learning-based manipulation of text messaging data to development of automatic intervention strategies against substance abuse and other adolescent challenges

Examining the Effect of Word Embeddings and Preprocessing Methods on Fake News Detection

The words people choose to use hold a lot of power, whether that be in spreading truth or deception. As listeners and readers, we do our best to understand how words are being used. There are many current methods in computer science literature attempting to embed words into numerical information for statistical analyses. Some of these embedding methods, such as Bag of Words, treat words as independent, while others, such as Word2Vec, attempt to gain information about the context of words. It is of interest to compare how well these various methods of translating text into numerical data work specifically with detecting fake news. The term “fake news” can be quite divisive, but we define it as news that is hyper-partisan, filled with untruths, and written to cause anger and outrage, as defined in Potthast & Kiesel (2018). We hypothesize a person’s word choice relates to the factualness of an article. In Chapter 5, we utilize this embedded information in several binary classification methods. We find that words are only marginally valuable in detecting fake news regardless of the embedding or classification method used. However, within natural language processing tasks, there are many preprocessing steps taken to get the text ready for analysis, which is explored in Chapter 6. The embedding methods are confounded with the preprocessing methods used. Preprocessing of text includes, but is not limited to, filtering out words that do not appear a minimum number of times, filtering out stop words, removing numbers, and translating all letters to lower case. We find filtering out stop words and removing words not appearing a minimum number of times have the most significant effect in combination with embedding and classification methods. Finally, in Chapter 7, we extend the classification to six categories ranging from true to pants-on-fire false and found these preprocessing methods are not as influential as they were with the binary outcome. Other predictors outside of the words and word embeddings themselves are necessary for improvement in the detection of fake news. Advisor: Kent Eskridg

Automatically generated summaries of sports videos based on semantic content

The sport has been a part of our lives since the beginning of times, whether we are spectators or participants. The diffusion and increase of multimedia platforms made the consumption of these contents available to everyone. Sports videos appeal to a large population all around the world and have become an important form of multimedia content that is streamed over the Internet and television networks. Moreover, sport content creators want to provide the users with relevant information such as live commentary, summarization of the games in form of text or video using automatic tools.As a result, MOG-Technologies wants to create a tool capable of summarizing football matches based on semantic content, and this problem was explored in the scope of this Dissertation. The main objective is to convert the television football commentator's speech into text taking advantage of Google's Speech-to-Text tool. Several machine learning models were then tested to classify sentences into important events. For the model training, a dataset was created, combining 43 games transcription from different television channels also from 72 games provided by Google Search timeline commentary, the combined dataset contains 3260 sentences. To validate the proposed solution the accuracy and f1 score were extracted for each machine learning model.The results show that the developed tool is capable of predicting events in live events, with low error rate. Also, combining multiple sources, not only the sport commentator speech, will help to increase the performance of the tool. It is important to notice that the dataset created during this Dissertation will allow MOG-Technologies to expand and perfect the concept discussed in this project