Alexandra Balahur

Opinion Mining on Newspaper Quotations

Opinion mining is the task of extracting from a set of documents opinions expressed by a source on a specified target. This article presents a comparative study on the methods and resources that can be employed for mining opinions from quotations (reported speech) in newspaper articles. We show the difficulty of this task, motivated by the presence of different possible targets and the large variety of affect phenomena that quotes contain. We evaluate our approaches using annotated quotations extracted from news provided by the EMM news gathering engine. We conclude that a generic opinion mining system requires both the use of large lexicons, as well as specialised training and testing data.

Detecting implicit expressions of emotion in text: A comparative analysis

Sentiment analysis is one of the recent, highly dynamic fields in Natural Language Processing. 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 propose and extend, which is based on commonsense knowledge stored in the EmotiNet knowledge base. Our extensive evaluations show that, in the context of this task, the approach based on EmotiNet is the most appropriate.

Building and Exploiting EmotiNet, a Knowledge Base for Emotion Detection Based on the Appraisal Theory Model

The task of automatically detecting emotion in text is challenging. This is due to the fact that most of the times, textual expressions of affect are not direct-using emotion words-but result from the interpretation and assessment of the meaning of the concepts and interaction of concepts described in the text. This paper presents the core of EmotiNet, a new knowledge base (KB) for representing and storing affective reaction to real-life contexts, and the methodology employed in designing, populating, and evaluating it. The basis of the design process is given by a set of self-reported affective situations in the International Survey on Emotion Antecedents and Reactions (ISEAR) corpus. We cluster the examples and extract triples using Semantic Roles. We subsequently extend our model using other resources, such as VerbOcean, ConceptNet, and SentiWordNet, with the aim of generalizing the knowledge contained. Finally, we evaluate the approach using the representations of other examples in the ISEAR corpus. We conclude that EmotiNet, although limited by the domain and small quantity of knowledge it presently contains, represents a semantic resource appropriate for capturing and storing the structure and the semantics of real events and predicting the emotional responses triggered by chains of actions.

Preface: Computational approaches to subjectivity and sentiment analysis: Present and envisaged methods and applications

Recent years have witnessed a surge of interest in computational methods for affect, ranging from opinion mining, to subjectivity detection, to sentiment and emotion analysis. This article presents a brief overview of the latest trends in the field and describes the manner in which the articles contained in the special issue contribute to the advancement of the area. Finally, we comment on the current challenges and envisaged developments of the subjectivity and sentiment analysis fields, as well as their application to other Natural Language Processing tasks and related domains.

A feature dependent method for opinion mining and classification

Mining the web for customer opinion on different products is both a useful, as well as challenging task. Previous approaches to customer review classification included document level, sentence and clause level sentiment analysis and feature based opinion summarization. In this paper, we present a feature driven opinion summarization method, where the term ldquodrivenrdquo is employed to describe the concept-to-detail (product class to product-specific characteristics) approach we took. For each product class we first automatically extract general features (characteristics describing any product, such as price, size, design), for each product we then extract specific features (as picture resolution in the case of a digital camera) and feature attributes (adjectives grading the characteristics, as for example high or low for price, small or big for size and modern or faddy for design). Further on, we assign a polarity (positive or negative) to each of the feature attributes using a previously annotated corpus and Support Vector Machines Sequential Minimal Optimization machine learning with the Normalized Google Distance. We show how the method presented is employed to build a feature-driven opinion summarization system that is presently working in English and Spanish. In order to detect the product category, we use a modified system for person names classification. The raw review text is split into sentences and depending on the product class detected, only the phrases containing the specific product features are selected for further processing. The phrases extracted undergo a process of anaphora resolution, Named Entity Recognition and syntactic parsing. Applying syntactic dependency and part of speech patterns, we extract pairs containing the feature and the polarity of the feature attribute the customer associates to the feature in the review. Eventually, we statistically summarize the polarity of the opinions different customers expressed about the product on the web as percentages of positive and negative opinions about each of the product features. We show the results and improvements over baseline, together with a discussion on the strong and weak points of the method and the directions for future work.

Determining the Polarity and Source of Opinions Expressed in Political Debates

In this paper we investigate different approaches we developed in order to classify opinion and discover opinion sources from text, using affect, opinion and attitude lexicon. We apply these approaches on the discussion topics contained in a corpus of American Congressional speech data. We propose three approaches to classifying opinion at the speech segment level, firstly using similarity measures to the affect, opinion and attitude lexicon, secondly dependency analysis and thirdly SVM machine learning. Further, we study the impact of taking into consideration the source of opinion and the consistency in the opinion expressed, and propose three methods to classify opinion at the speaker intervention level, showing improvements over the classification of individual text segments. Finally, we propose a method to identify the party the opinion belongs to, through the identification of specific affective and non-affective lexicon used in the argumentations. We present the results obtained when evaluating the different methods we developed, together with a discussion on the issues encountered and some possible solutions. We conclude that, even at a more general level, our approach performs better than trained classifiers on specific data.

Towards Building a Competitive Opinion Summarization System: Challenges and Keys

This paper presents an overview of our participation in the TAC 2008 Opinion Pilot Summarization task, as well as the proposed and evaluated post-competition improvements. We first describe our opinion summarization system and the results obtained. Further on, we identify the systems weak points and suggest several improvements, focused both on information content, as well as linguistic and readability aspects. We obtain encouraging results, especially as far as F-measure is concerned, outperforming the competition results by approximately 80%.

EmotiNet: a knowledge base for emotion detection in text built on the appraisal theories

The automatic detection of emotions is a difficult task in Artificial Intelligence. In the field of Natural Language Processing, the challenge of automatically detecting emotion from text has been tackled from many perspectives. Nonetheless, the majority of the approaches contemplated only the word level. Due to the fact that emotion is most of the times not expressed through specific words, but by evoking situations that have a commonsense affective meaning, the performance of existing systems is low. This article presents the EmotiNet knowledge base - a resource for the detection of emotion from text based on commonsense knowledge on concepts, their interaction and their affective consequence. The core of the resource is built from a set of self-reported affective situations and extended with external sources of commonsense knowledge on emotion-triggering concepts. The results of the preliminary evaluations show that the approach is appropriate for capturing and storing the structure and the semantics of real situations and predict the emotional responses triggered by actions presented in text.

Opinion and Generic Question Answering Systems: a Performance Analysis

The importance of the new textual genres such as blogs or forum entries is growing in parallel with the evolution of the Social Web. This paper presents two corpora of blog posts in English and in Spanish, annotated according to the EmotiBlog annotation scheme. Furthermore, we created 20 factual and opinionated questions for each language and also the Gold Standard for their answers in the corpus. The purpose of our work is to study the challenges involved in a mixed fact and opinion question answering setting by comparing the performance of two Question Answering (QA) systems as far as mixed opinion and factual setting is concerned. The first one is open domain, while the second one is opinion-oriented. We evaluate separately the two systems in both languages and propose possible solutions to improve QA systems that have to process mixed questions.

Multilingual Feature-Driven Opinion Extraction and Summarization from Customer Reviews

This paper presents a feature-driven opinion summarization method for customer reviews on the web based on identifying general features (characteristics) describing any product, product specific features and feature attributes (adjectives grading the characteristics). Feature attributes are assigned a polarity using on the one hand a previously annotated corpus and on the other hand by applying Support Vector Machines Sequential Minimal Optimization[1] machine learning with the Normalized Google Distance[2]. Reviews are statistically summarized around product features using the polarity of the feature attributes they are described by.