1,499 research outputs found
Identifying Purpose Behind Electoral Tweets
Tweets pertaining to a single event, such as a national election, can number
in the hundreds of millions. Automatically analyzing them is beneficial in many
downstream natural language applications such as question answering and
summarization. In this paper, we propose a new task: identifying the purpose
behind electoral tweets--why do people post election-oriented tweets? We show
that identifying purpose is correlated with the related phenomenon of sentiment
and emotion detection, but yet significantly different. Detecting purpose has a
number of applications including detecting the mood of the electorate,
estimating the popularity of policies, identifying key issues of contention,
and predicting the course of events. We create a large dataset of electoral
tweets and annotate a few thousand tweets for purpose. We develop a system that
automatically classifies electoral tweets as per their purpose, obtaining an
accuracy of 43.56% on an 11-class task and an accuracy of 73.91% on a 3-class
task (both accuracies well above the most-frequent-class baseline). Finally, we
show that resources developed for emotion detection are also helpful for
detecting purpose
Unleashing the Power of Hashtags in Tweet Analytics with Distributed Framework on Apache Storm
Twitter is a popular social network platform where users can interact and
post texts of up to 280 characters called tweets. Hashtags, hyperlinked words
in tweets, have increasingly become crucial for tweet retrieval and search.
Using hashtags for tweet topic classification is a challenging problem because
of context dependent among words, slangs, abbreviation and emoticons in a short
tweet along with evolving use of hashtags. Since Twitter generates millions of
tweets daily, tweet analytics is a fundamental problem of Big data stream that
often requires a real-time Distributed processing. This paper proposes a
distributed online approach to tweet topic classification with hashtags. Being
implemented on Apache Storm, a distributed real time framework, our approach
incrementally identifies and updates a set of strong predictors in the Na\"ive
Bayes model for classifying each incoming tweet instance. Preliminary
experiments show promising results with up to 97% accuracy and 37% increase in
throughput on eight processors.Comment: IEEE International Conference on Big Data 201
Doctor of Philosophy in Computer Science
dissertationOver the last decade, social media has emerged as a revolutionary platform for informal communication and social interactions among people. Publicly expressing thoughts, opinions, and feelings is one of the key characteristics of social media. In this dissertation, I present research on automatically acquiring knowledge from social media that can be used to recognize people's affective state (i.e., what someone feels at a given time) in text. This research addresses two types of affective knowledge: 1) hashtag indicators of emotion consisting of emotion hashtags and emotion hashtag patterns, and 2) affective understanding of similes (a form of figurative comparison). My research introduces a bootstrapped learning algorithm for learning hashtag in- dicators of emotions from tweets with respect to five emotion categories: Affection, Anger/Rage, Fear/Anxiety, Joy, and Sadness/Disappointment. With a few seed emotion hashtags per emotion category, the bootstrapping algorithm iteratively learns new hashtags and more generalized hashtag patterns by analyzing emotion in tweets that contain these indicators. Emotion phrases are also harvested from the learned indicators to train additional classifiers that use the surrounding word context of the phrases as features. This is the first work to learn hashtag indicators of emotions. My research also presents a supervised classification method for classifying affective polarity of similes in Twitter. Using lexical, semantic, and sentiment properties of different simile components as features, supervised classifiers are trained to classify a simile into a positive or negative affective polarity class. The property of comparison is also fundamental to the affective understanding of similes. My research introduces a novel framework for inferring implicit properties that 1) uses syntactic constructions, statistical association, dictionary definitions and word embedding vector similarity to generate and rank candidate properties, 2) re-ranks the top properties using influence from multiple simile components, and 3) aggregates the ranks of each property from different methods to create a final ranked list of properties. The inferred properties are used to derive additional features for the supervised classifiers to further improve affective polarity recognition. Experimental results show substantial improvements in affective understanding of similes over the use of existing sentiment resources
Irony and Sarcasm Detection in Twitter: The Role of Affective Content
Tesis por compendioSocial media platforms, like Twitter, offer a face-saving ability that allows users to express themselves employing figurative language devices such as irony to achieve different communication purposes. Dealing with such kind of content represents a big challenge for computational linguistics. Irony is closely associated with the indirect expression of feelings, emotions and evaluations. Interest in detecting the presence of irony in social media texts has grown significantly in the recent years.
In this thesis, we introduce the problem of detecting irony in social media under a computational linguistics perspective. We propose to address this task by focusing, in particular, on the role of affective information for detecting the presence of such figurative language device.
Attempting to take advantage of the subjective intrinsic value enclosed in ironic expressions, we present a novel model, called emotIDM, for detecting irony relying on a wide range of affective features. For characterising an ironic utterance, we used an extensive set of resources covering different facets of affect from sentiment to finer-grained emotions. Results show that emotIDM has a competitive performance across the experiments carried out, validating the effectiveness of the proposed approach.
Another objective of the thesis is to investigate the differences among tweets labeled with #irony and #sarcasm. Our aim is to contribute to the less investigated topic in computational linguistics on the separation between irony and sarcasm in social media, again, with a special focus on affective features. We also studied a less explored hashtag: #not. We find data-driven arguments on the differences among tweets containing these hashtags, suggesting that the above mentioned hashtags are used to refer different figurative language devices.
We identify promising features based on affect-related phenomena for discriminating among different kinds of figurative language devices. We also analyse the role of polarity reversal in tweets containing ironic hashtags, observing that the impact of such phenomenon varies.
In the case of tweets labeled with #sarcasm often there is a full reversal, whereas in the case of those tagged with #irony there is an attenuation of the polarity.
We analyse the impact of irony and sarcasm on sentiment analysis, observing a drop in the performance of NLP systems developed for this task when irony is present. Therefore, we explored the possible use of our findings in irony detection for the development of an irony-aware sentiment analysis system, assuming that the identification of ironic content could help to improve the correct identification of sentiment polarity. To this aim, we incorporated emotIDM into a pipeline for determining the polarity of a given Twitter message.
We compared our results with the state of the art determined by the "Semeval-2015 Task 11" shared task, demonstrating the relevance of considering affective information together with features alerting on the presence of irony for performing sentiment analysis of figurative language for this kind of social media texts. To summarize, we demonstrated the usefulness of exploiting different facets of affective information for dealing with the presence of irony in Twitter.Las plataformas de redes sociales, como Twitter, ofrecen a los usuarios la posibilidad de expresarse de forma libre y espontanea haciendo uso de diferentes recursos lingüÃsticos como la ironÃa para lograr diferentes propósitos de comunicación. Manejar ese tipo de contenido representa un gran reto para la lingüÃstica computacional. La ironÃa está estrechamente vinculada con la expresión indirecta de sentimientos, emociones y evaluaciones. El interés en detectar la presencia de ironÃa en textos de redes sociales ha aumentado significativamente en los últimos años.
En esta tesis, introducimos el problema de detección de ironÃa en redes sociales desde una perspectiva de la lingüÃstica computacional. Proponemos abordar dicha tarea enfocándonos, particularmente, en el rol de información relativa al afecto y las emociones para detectar la presencia de dicho recurso lingüÃstico. Con la intención de aprovechar el valor intrÃnseco de subjetividad contenido en las expresiones irónicas, presentamos un modelo para detectar la presencia de ironÃa denominado emotIDM, el cual está basado en una amplia variedad de rasgos afectivos. Para caracterizar instancias irónicas, utilizamos un amplio conjunto de recursos que cubren diferentes ámbitos afectivos: desde sentimientos (positivos o negativos) hasta emociones especÃficas definidas con una granularidad fina. Los resultados obtenidos muestran que emotIDM tiene un desempeño competitivo en los experimentos realizados, validando la efectividad del enfoque propuesto.
Otro objetivo de la tesis es investigar las diferencias entre tweets etiquetados con #irony y #sarcasm. Nuestra finalidad es contribuir a un tema menos investigado en lingüÃstica computacional: la separación entre el uso de ironÃa y sarcasmo en redes sociales, con especial énfasis en rasgos afectivos. Además, estudiamos un hashtag que ha sido menos analizado: #not. Nuestros resultados parecen evidenciar que existen diferencias entre los tweets que contienen dichos hashtags, sugiriendo que son utilizados para hacer referencia de diferentes recursos lingüÃsticos. Identificamos un conjunto de caracterÃsticas basadas en diferentes fenómenos afectivos que parecen ser útiles para discriminar entre diferentes tipos de recursos lingüÃsticos. Adicionalmente analizamos la reversión de polaridad en tweets que contienen hashtags irónicos, observamos que el impacto de dicho fenómeno es diferente en cada uno de ellos. En el caso de los tweets que están etiquetados con el hashtag #sarcasm, a menudo hay una reversión total, mientras que en el caso de los tweets etiquetados con el hashtag #irony se produce una atenuación de la polaridad.
Llevamos a cabo un estudio del impacto de la ironÃa y el sarcasmo en el análisis de sentimientos, observamos una disminución en el rendimiento de los sistemas de PLN desarrollados para dicha tarea cuando la ironÃa está presente. Por consiguiente, exploramos la posibilidad de utilizar nuestros resultados en detección de ironÃa para el desarrollo de un sistema de análisis de sentimientos que considere de la presencia de ironÃa, suponiendo que la detección de contenido irónico podrÃa ayudar a mejorar la correcta identificación del sentimiento expresado en un texto dado. Con este objetivo, incorporamos emotIDM como la primera fase en un sistema de análisis de sentimientos para determinar la polaridad de mensajes en Twitter. Comparamos nuestros resultados con el estado del arte establecido en la tarea de evaluación "Semeval-2015 Task 11", demostrando la importancia de utilizar información afectiva en conjunto con caracterÃsticas que alertan de la presencia de la ironÃa para desempeñar análisis de sentimientos en textos con lenguaje figurado que provienen de redes sociales. En resumen, demostramos la utilidad de aprovechar diferentes aspectos de información relativa al afecto y las emociones para tratar cuestiones relativas a la presencia de la ironÃLes plataformes de xarxes socials, com Twitter, oferixen als usuaris la possibilitat d'expressar-se de forma lliure i espontà nia fent ús de diferents recursos lingüÃstics com la ironia per aconseguir diferents propòsits de comunicació. Manejar aquest tipus de contingut representa un gran repte per a la lingüÃstica computacional. La ironia està estretament vinculada amb l'expressió indirecta de sentiments, emocions i avaluacions. L'interés a detectar la presència d'ironia en textos de xarxes socials ha augmentat significativament en els últims anys.
En aquesta tesi, introduïm el problema de detecció d'ironia en xarxes socials des de la perspectiva de la lingüÃstica computacional. Proposem abordar aquesta tasca enfocant-nos, particularment, en el rol d'informació relativa a l'afecte i les emocions per detectar la presència d'aquest recurs lingüÃstic. Amb la intenció d'aprofitar el valor intrÃnsec de subjectivitat contingut en les expressions iròniques, presentem un model per a detectar la presència d'ironia denominat emotIDM, el qual està basat en una à mplia varietat de trets afectius. Per caracteritzar instà ncies iròniques, utilitzà rem un ampli conjunt de recursos que cobrixen diferents à mbits afectius: des de sentiments (positius o negatius) fins emocions especÃfiques definides de forma molt detallada. Els resultats obtinguts mostres que emotIDM té un rendiment competitiu en els experiments realitzats, validant l'efectivitat de l'enfocament proposat.
Un altre objectiu de la tesi és investigar les diferències entre tweets etiquetats com a #irony i #sarcasm. La nostra finalitat és contribuir a un tema menys investigat en lingüÃstica computacional: la separació entre l'ús d'ironia i sarcasme en xarxes socials, amb especial èmfasi amb els trets afectius. A més, estudiem un hashtag que ha sigut menys estudiat: #not. Els nostres resultats pareixen evidenciar que existixen diferències entre els tweets que contenen els hashtags esmentats, cosa que suggerix que s'utilitzen per fer referència de diferents recursos lingüÃstics. Identifiquem un conjunt de caracterÃstiques basades en diferents fenòmens afectius que pareixen ser útils per a discriminar entre diferents tipus de recursos lingüÃstics. Addicionalment analitzem la reversió de polaritat en tweets que continguen hashtags irònics, observant que l'impacte del fenomen esmentat és diferent per a cadascun d'ells. En el cas dels tweet que estan etiquetats amb el hashtag #sarcasm, a sovint hi ha una reversió total, mentre que en el cas dels tweets etiquetats amb el hashtag #irony es produïx una atenuació de polaritat.
Duem a terme un estudi de l'impacte de la ironia i el sarcasme en l'anà lisi de sentiments, on observem una disminució en el rendiment dels sistemes de PLN desenvolupats per a aquestes tasques quan la ironia està present. Per consegüent, vam explorar la possibilitat d'utilitzar els nostres resultats en detecció d'ironia per a desenvolupar un sistema d'anà lisi de sentiments que considere la presència d'ironia, suposant que la detecció de contingut irònic podria ajudar a millorar la correcta identificació del sentiment expressat en un text donat. Amb aquest objectiu, incorporem emotIDM com la primera fase en un sistema d'anà lisi de sentiments per determinar la polaritat de missatges en Twitter. Hem comparat els nostres resultats amb l'estat de l'art establert en la tasca d'avaluació "Semeval-2015 Task 11", demostrant la importà ncia d'utilitzar informació afectiva en conjunt amb caracterÃstiques que alerten de la presència de la ironia per exercir anà lisi de sentiments en textos amb llenguatge figurat que provenen de xarxes socials. En resum, hem demostrat la utilitat d'aprofitar diferents aspectes d'informació relativa a l'afecte i les emocions per tractar qüestions relatives a la presència d'ironia en Twitter.Hernández Farias, DI. (2017). Irony and Sarcasm Detection in Twitter: The Role of Affective Content [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90544TESISCompendi
A novel Auto-ML Framework for Sarcasm Detection
Many domains have sarcasm or verbal irony presented in the text of reviews, tweets, comments, and dialog discussions. The purpose of this research is to classify sarcasm for multiple domains using the deep learning based AutoML framework. The proposed AutoML framework has five models in the model search pipeline, these five models are the combination of convolutional neural network (CNN), Long Short-Term Memory (LSTM), deep neural network (DNN), and Bidirectional Long Short-Term Memory (BiLSTM). The hybrid combination of CNN, LSTM, and DNN models are presented as CNN-LSTM-DNN, LSTM-DNN, BiLSTM-DNN, and CNN-BiLSTM-DNN. This work has proposed the algorithms that contrast polarities between terms and phrases, which are categorized into implicit and explicit incongruity categories. The incongruity and pragmatic features like punctuation, exclamation marks, and others integrated into the AutoML DeepConcat framework models. That integration was possible when the DeepConcat AutoML framework initiate a model search pipeline for five models to achieve better performance. Conceptually, DeepConcat means that model will integrate with generalized features. It was evident that the pretrain model BiLSTM achieved a better performance of 0.98 F1 when compared with the other five model performances. Similarly, the AutoML based BiLSTM-DNN model achieved the best performance of 0.98 F1, which is better than core approaches and existing state-of-the-art Tweeter tweet dataset, Amazon reviews, and dialog discussion comments. The proposed AutoML framework has compared performance metrics F1 and AUC and discovered that F1 is better than AUC. The integration of all feature categories achieved a better performance than the individual category of pragmatic and incongruity features. This research also evaluated the performance of the dropout layer hyperparameter and it achieved better performance than the fixed percentage like 10% of dropout parameter of the AutoML based Bayesian optimization. Proposed AutoML framework DeepConcat evaluated best pretrain models BiLSTM-DNN and CNN-CNN-DNN to transfer knowledge across domains like Amazon reviews and Dialog discussion comments (text) using the last strategy, full layer, and our fade-out freezing strategies. In the transfer learning fade-out strategy outperformed the existing state-of-the-art model BiLSTM-DNN, the performance is 0.98 F1 on tweets, 0.85 F1 on Amazon reviews, and 0.87 F1 on the dialog discussion SCV2-Gen dataset. Further, all strategies with various domains can be compared for the best model selection
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