Recognition of Emotions in Czech Newspaper Headlines

With the growth of internet community, many different text-based documents are produced. Emotion detection and classification in text becomes very important in human-machine interaction or in human-to-human internet communication with this growth. This article refers to this issue in Czech texts. Headlines were extracted from Czech newspapers and Fear, Joy, Anger, Disgust, Sadness, and Surprise emotions are detected. In this work, several algorithms for learning were assessed and compared according to their accuracy of emotion detection and classification of news headlines. The best results were achieved using the SVM (Support Vector Machine) method with a linear kernel, where the presence of the dominant emotion or emotions was analyzed. For individual emotions the following results were obtained: Anger was detected in 87.3 %, Disgust 95.01%, Fear 81.32 %, Joy 71.6 %, Sadness 75.4 %, and Surprise 71.09 %

Intensional Learning to Efficiently Build up Automatically Annotated Emotion Corpora

Textual emotion detection has a high impact on business, society, politics or education with applications such as, detecting depression or personality traits, suicide prevention or identifying cases of cyber-bulling. Given this context, the objective of our research is to contribute to the improvement of emotion recognition task through an automatic technique focused on reducing both the time and cost needed to develop emotion corpora. Our proposal is to exploit a bootstrapping approach based on intensional learning for automatic annotations with two main steps: 1) an initial similarity-based categorization where a set of seed sentences is created and extended by distributional semantic similarity (word vectors or word embeddings); 2) train a supervised classifier on the initially categorized set. The technique proposed allows us an efficient annotation of a large amount of emotion data with standards of reliability according to the evaluation results.This research has been supported by the FPI grant (BES-2013-065950) and the research stay grants (EEBB-I-15-10108 and EEBB-I-16-11174) from the Spanish Ministry of Science and Innovation. It has also funded by the Spanish Government (DIGITY ref. TIN2015-65136-C02-2-R and RESCATA ref. TIN2015-65100-R), the Valencian Government (grant no. PROMETEOII/ 2014/001), the University of Alicante (ref. GRE16-01) and BBVA Foundation (Análisis de Sentimientos Aplicado a la Prevención del Suicidio en las Redes Sociales (ASAP) project)

Sentiment polarity shifters : creating lexical resources through manual annotation and bootstrapped machine learning

Alleviating pain is good and abandoning hope is bad. We instinctively understand how words like "alleviate" and "abandon" affect the polarity of a phrase, inverting or weakening it. When these words are content words, such as verbs, nouns and adjectives, we refer to them as polarity shifters. Shifters are a frequent occurrence in human language and an important part of successfully modeling negation in sentiment analysis; yet research on negation modeling has focussed almost exclusively on a small handful of closed class negation words, such as "not", "no" and "without. A major reason for this is that shifters are far more lexically diverse than negation words, but no resources exist to help identify them. We seek to remedy this lack of shifter resources. Our most central step towards this is the creation of a large lexicon of polarity shifters that covers verbs, nouns and adjectives. To reduce the prohibitive cost of such a large annotation task, we develop a bootstrapping approach that combines automatic classification with human verification. This ensures the high quality of our lexicon while reducing annotation cost by over 70%. In designing the bootstrap classifier we develop a variety of features which use both existing semantic resources and linguistically informed text patterns. In addition we investigate how knowledge about polarity shifters might be shared across different parts of speech, highlighting both the potential and limitations of such an approach. The applicability of our bootstrapping approach extends beyond the creation of a single resource. We show how it can further be used to introduce polarity shifter resources for other languages. Through the example case of German we show that all our features are transferable to other languages. Keeping in mind the requirements of under-resourced languages, we also explore how well a classifier would do when relying only on data- but not resource-driven features. We also introduce ways to use cross-lingual information, leveraging the shifter resources we previously created for other languages. Apart from the general question of which words can be polarity shifters, we also explore a number of other factors. One of these is the matter of shifting directions, which indicates whether a shifter affects positive polarities, negative polarities or whether it can shift in either direction. Using a supervised classifier we add shifting direction information to our bootstrapped lexicon. For other aspects of polarity shifting, manual annotation is preferable to automatic classification. Not every word that can cause polarity shifting does so for every of its word senses. As word sense disambiguation technology is not robust enough to allow the automatic handling of such nuances, we manually create a complete sense-level annotation of verbal polarity shifters. To verify the usefulness of the lexica which we create, we provide an extrinsic evaluation in which we apply them to a sentiment analysis task. In this task the different lexica are not only compared amongst each other, but also against a state-of-the-art compositional polarity neural network classifier that has been shown to be able to implicitly learn the negating effect of negation words from a training corpus. However, we find that the same is not true for the far more lexically diverse polarity shifters. Instead, the use of the explicit knowledge provided by our shifter lexica brings clear gains in performance.Deutsche Forschungsgesellschaf

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Análisis de sentimientos de reseñas para determinar la acogida de un producto utilizando técnicas de machine learning y data mining

Leer múltiples reseñas de productos puede resultar tedioso, y concluir si un producto ha gustado o no a sus consumidores es complicado, por lo que es necesario implementar una herramienta que analice todas las reseñas de un producto y determine su polaridad. Lo anterior con el fin de agilizar y mejorar la toma de decisiones sobre un producto por parte de los interesados, así como la relación cliente-empresa, evaluando las reseñas bajo un mismo críterio. Durante el desarrollo del proyecto se diseñó e implementó la estrategia utilizando técnicas de Machine learning y Data mining para solucionar el problema planteado. Como resultado se implemento un modelo por medio de un dataset, luego se aplicó web scrapping a la página web de Amazon, un reconocido E-commerce, con el fin de extraer las reseñas de un producto dado, se visualizaron las reseñas de este a través de librerías de Python para luego ser procesadas y así realizar un analisis de sentimientos. Lo anterior permitió concluir la polaridad de un producto dado haciendo uso de tecnicas de machine learning y data mining.Reading multiple product reviews can be tedious, and concluding whether or not consumers liked a product is complicated, so it is necessary to implement a tool that analyzes all reviews of a product and determines their polarity. The foregoing in order to streamline and improve decision-making about a product by the interested parties, as well as the client-company relationship, evaluating the reviews under the same criteria. During the development of the project, the strategy was developed and implemented using Machine learning and Data mining techniques to solve the problem posed. As a result, a model was implemented through a data set, then web scrapping was applied to the Amazon website, a recognized E-commerce, in order to extract the reviews of a given product, the reviews of this product were displayed. through Python libraries to later be processed and thus carry out a sentiment analysis. The above concluded the polarity of a given product making use of machine learning and data mining techniques