Alexandra Balahur

Also published as: Alexandra Balahur-Dobrescu


2020

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Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation
Valentin Barriere | Alexandra Balahur
Proceedings of the 28th International Conference on Computational Linguistics

Tweets are specific text data when compared to general text. Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult to find huge annotated corpora for non-English languages. The recent rise of the transformer models in Natural Language Processing allows to achieve unparalleled performances in many tasks, but these models need a consequent quantity of text to adapt to the tweet domain. We propose the use of a multilingual transformer model, that we pre-train over English tweets on which we apply data-augmentation using automatic translation to adapt the model to non-English languages. Our experiments in French, Spanish, German and Italian suggest that the proposed technique is an efficient way to improve the results of the transformers over small corpora of tweets in a non-English language.

2019

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Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Roman Klinger | Veronique Hoste | Carlo Strapparava | Orphee De Clercq
Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2018

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Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Saif M. Mohammad | Veronique Hoste | Roman Klinger
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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IEST: WASSA-2018 Implicit Emotions Shared Task
Roman Klinger | Orphée De Clercq | Saif Mohammad | Alexandra Balahur
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Past shared tasks on emotions use data with both overt expressions of emotions (I am so happy to see you!) as well as subtle expressions where the emotions have to be inferred, for instance from event descriptions. Further, most datasets do not focus on the cause or the stimulus of the emotion. Here, for the first time, we propose a shared task where systems have to predict the emotions in a large automatically labeled dataset of tweets without access to words denoting emotions. Based on this intention, we call this the Implicit Emotion Shared Task (IEST) because the systems have to infer the emotion mostly from the context. Every tweet has an occurrence of an explicit emotion word that is masked. The tweets are collected in a manner such that they are likely to include a description of the cause of the emotion – the stimulus. Altogether, 30 teams submitted results which range from macro F1 scores of 21 % to 71 %. The baseline (Max-Ent bag of words and bigrams) obtains an F1 score of 60 % which was available to the participants during the development phase. A study with human annotators suggests that automatic methods outperform human predictions, possibly by honing into subtle textual clues not used by humans. Corpora, resources, and results are available at the shared task website at http://implicitemotions.wassa2018.com.

2017

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Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Saif M. Mohammad | Erik van der Goot
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Understanding human values and their emotional effect
Alexandra Balahur
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Emotions can be triggered by various factors. According to the Appraisal Theories (De Rivera, 1977; Frijda, 1986; Ortony et al., 1988; Johnson-Laird and Oatley, 1989) emotions are elicited and differentiated on the basis of the cognitive evaluation of the personal significance of a situation, object or event based on “appraisal criteria” (intrinsic characteristics of objects and events, significance of events to individual needs and goals, individual’s ability to cope with the consequences of the event, compatibility of event with social or personal standards, norms and values). These differences in values can trigger reactions such as anger, disgust (contempt), sadness, etc., because these behaviors are evaluated by the public as being incompatible with their social/personal standards, norms or values. Such arguments are frequently present both in mainstream media, as well as social media, building a society-wide view, attitude and emotional reaction towards refugees/immigrants. In this demo, I will talk about experiments to annotate and detect factual arguments that are linked to human needs/motivations from text and in consequence trigger emotion in the media audience and propose a new task for next year’s WASSA.

2016

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Detecting Implicit Expressions of Affect from Text using Semantic Knowledge on Common Concept Properties
Alexandra Balahur | Hristo Tanev
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Emotions are an important part of the human experience. They are responsible for the adaptation and integration in the environment, offering, most of the time together with the cognitive system, the appropriate responses to stimuli in the environment. As such, they are an important component in decision-making processes. In today’s society, the avalanche of stimuli present in the environment (physical or virtual) makes people more prone to respond to stronger affective stimuli (i.e., those that are related to their basic needs and motivations ― survival, food, shelter, etc.). In media reporting, this is translated in the use of arguments (factual data) that are known to trigger specific (strong, affective) behavioural reactions from the readers. This paper describes initial efforts to detect such arguments from text, based on the properties of concepts. The final system able to retrieve and label this type of data from the news in traditional and social platforms is intended to be integrated Europe Media Monitor family of applications to detect texts that trigger certain (especially negative) reactions from the public, with consequences on citizen safety and security.

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Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Erik van der Goot | Piek Vossen | Andres Montoyo
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Sentiment Analysis - What are we talking about?
Alexandra Balahur
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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OPAL at SemEval-2016 Task 4: the Challenge of Porting a Sentiment Analysis System to the “Real” World
Alexandra Balahur
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Erik van der Goot | Piek Vossen | Andres Montoyo
Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Proceedings of the First Workshop on Computing News Storylines
Tommaso Caselli | Marieke van Erp | Anne-Lyse Minard | Mark Finlayson | Ben Miller | Jordi Atserias | Alexandra Balahur | Piek Vossen
Proceedings of the First Workshop on Computing News Storylines

2014

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Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Erik van der Goot | Ralf Steinberger | Andres Montoyo
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Challenges in Creating a Multilingual Sentiment Analysis Application for Social Media Mining
Alexandra Balahur | Hristo Tanev | Erik van der Goot
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts
Alexandra Balahur | Marco Turchi | Ralf Steinberger | Jose-Manuel Perea-Ortega | Guillaume Jacquet | Dilek Küçük | Vanni Zavarella | Adil El Ghali
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents an evaluation of the use of machine translation to obtain and employ data for training multilingual sentiment classifiers. We show that the use of machine translated data obtained similar results as the use of native-speaker translations of the same data. Additionally, our evaluations pinpoint to the fact that the use of multilingual data, including that obtained through machine translation, leads to improved results in sentiment classification. Finally, we show that the performance of the sentiment classifiers built on machine translated data can be improved using original data from the target language and that even a small amount of such texts can lead to significant growth in the classification performance.

2013

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Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Erik van der Goot | Andres Montoyo
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Sentiment Analysis in Social Media Texts
Alexandra Balahur
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Detecting Event-Related Links and Sentiments from Social Media Texts
Alexandra Balahur | Hristo Tanev
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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Improving Sentiment Analysis in Twitter Using Multilingual Machine Translated Data
Alexandra Balahur | Marco Turchi
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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OPTWIMA: Comparing Knowledge-rich and Knowledge-poor Approaches for Sentiment Analysis in Short Informal Texts
Alexandra Balahur
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

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Extending the EmotiNet Knowledge Base to Improve the Automatic Detection of Implicitly Expressed Emotions from Text
Alexandra Balahur | Jesús M. Hermida
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Sentiment analysis is one of the recent, highly dynamic fields in Natural Language Processing. Although much research has been performed in this area, most existing approaches are based on word-level analysis of texts and are mostly able to detect only explicit expressions of sentiment. However, in many cases, emotions are not expressed by using words with an affective meaning (e.g. happy), but by describing real-life situations, which readers (based on their commonsense knowledge) detect as being related to a specific emotion. Given the challenges of detecting emotions from contexts in which no lexical clue is present, in this article we present a comparative analysis between the performance of well-established methods for emotion detection (supervised and lexical knowledge-based) and a method we extend, which is based on commonsense knowledge stored in the EmotiNet knowledge base. Our extensive comparative evaluations show that, in the context of this task, the approach based on EmotiNet is the most appropriate.

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Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis
Alexandra Balahur | Andres Montoyo | Patricio Martinez Barco | Ester Boldrini
Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis

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Multilingual Sentiment Analysis using Machine Translation?
Alexandra Balahur | Marco Turchi
Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis

2011

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Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)
Alexandra Balahur | Ester Boldrini | Andres Montoyo | Patricio Martinez-Barco
Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)

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Detecting Implicit Expressions of Sentiment in Text Based on Commonsense Knowledge
Alexandra Balahur | Jesús M. Hermida | Andrés Montoyo
Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)

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Towards a Unified Approach for Opinion Question Answering and Summarization
Elena Lloret | Alexandra Balahur | Manuel Palomar | Andrés Montoyo
Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)

2010

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EmotiBlog: A Finer-Grained and More Precise Learning of Subjectivity Expression Models
Ester Boldrini | Alexandra Balahur | Patricio Martínez-Barco | Andrés Montoyo
Proceedings of the Fourth Linguistic Annotation Workshop

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A survey on the role of negation in sentiment analysis
Michael Wiegand | Alexandra Balahur | Benjamin Roth | Dietrich Klakow | Andrés Montoyo
Proceedings of the Workshop on Negation and Speculation in Natural Language Processing

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Going Beyond Traditional QA Systems: Challenges and Keys in Opinion Question Answering
Alexandra Balahur | Ester Boldrini | Andrés Montoyo | Patricio Martínez-Barco
Coling 2010: Posters

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OpAL: Applying Opinion Mining Techniques for the Disambiguation of Sentiment Ambiguous Adjectives in SemEval-2 Task 18
Alexandra Balahur | Andrés Montoyo
Proceedings of the 5th International Workshop on Semantic Evaluation

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Sentiment Analysis in the News
Alexandra Balahur | Ralf Steinberger | Mijail Kabadjov | Vanni Zavarella | Erik van der Goot | Matina Halkia | Bruno Pouliquen | Jenya Belyaeva
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Recent years have brought a significant growth in the volume of research in sentiment analysis, mostly on highly subjective text types (movie or product reviews). The main difference these texts have with news articles is that their target is clearly defined and unique across the text. Following different annotation efforts and the analysis of the issues encountered, we realised that news opinion mining is different from that of other text types. We identified three subtasks that need to be addressed: definition of the target; separation of the good and bad news content from the good and bad sentiment expressed on the target; and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. Furthermore, we distinguish three different possible views on newspaper articles ― author, reader and text, which have to be addressed differently at the time of analysing sentiment. Given these definitions, we present work on mining opinions about entities in English language news, in which we apply these concepts. Results showed that this idea is more appropriate in the context of news opinion mining and that the approaches taking this into consideration produce a better performance.

2009

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A Comparative Study of Open Domain and Opinion Question Answering Systems for Factual and Opinionated Queries
Alexandra Balahur | Ester Boldrini | Andrés Montoyo | Patricio Martínez-Barco
Proceedings of the International Conference RANLP-2009

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Summarizing Opinions in Blog Threads
Alexandra Balahur | Mijail Kabadjov | Josef Steinberger | Ralf Steinberger | Andrés Montoyo
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

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Summarizing Threads in Blogs Using Opinion Polarity
Alexandra Balahur | Elena Lloret | Ester Boldrini | Andrés Montoyo | Manuel Palomar | Patricio Martínez-Barco
Proceedings of the Workshop on Events in Emerging Text Types

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Opinion and Generic Question Answering Systems: a Performance Analysis
Alexandra Balahur | Ester Boldrini | Andrés Montoyo | Patricio Martínez-Barco
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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Towards Building a Competitive Opinion Summarization System: Challenges and Keys
Elena Lloret | Alexandra Balahur | Manuel Palomar | Andrés Montoyo
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium

2008

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Named Entity Relation Mining using Wikipedia
Adrian Iftene | Alexandra Balahur-Dobrescu
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Discovering relations among Named Entities (NEs) from large corpora is both a challenging, as well as useful task in the domain of Natural Language Processing, with applications in Information Retrieval (IR), Summarization (SUM), Question Answering (QA) and Textual Entailment (TE). The work we present resulted from the attempt to solve practical issues we were confronted with while building systems for the tasks of Textual Entailment Recognition and Question Answering, respectively. The approach consists in applying grammar induced extraction patterns on a large corpus - Wikipedia - for the extraction of relations between a given Named Entity and other Named Entities. The results obtained are high in precision, determining a reliable and useful application of the built resource.

2007

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Hypothesis Transformation and Semantic Variability Rules Used in Recognizing Textual Entailment
Adrian Iftene | Alexandra Balahur-Dobrescu
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing