Sentiment Lexicon Adaptation with Context and Semantics for the Social Web

Sentiment analysis over social streams offers governments and organisations a fast and effective way to monitor the publics' feelings towards policies, brands, business, etc. General purpose sentiment lexicons have been used to compute sentiment from social streams, since they are simple and effective. They calculate the overall sentiment of texts by using a general collection of words, with predetermined sentiment orientation and strength. However, words' sentiment often vary with the contexts in which they appear, and new words might be encountered that are not covered by the lexicon, particularly in social media environments where content emerges and changes rapidly and constantly. In this paper, we propose a lexicon adaptation approach that uses contextual as well as semantic information extracted from DBPedia to update the words' weighted sentiment orientations and to add new words to the lexicon. We evaluate our approach on three different Twitter datasets, and show that enriching the lexicon with contextual and semantic information improves sentiment computation by 3.4% in average accuracy, and by 2.8% in average F1 measure

eSOLHotel: Building an Spanish opinion lexicon adapted to the tourism domain

Desde que la web 2.0 es el mayor contenedor de opiniones en todos los idiomas sobre distintos temas o asuntos, el estudio del Análisis de Sentimientos ha crecido exponencialmente. En este trabajo nos centramos en la clasificación de polaridad de opiniones en español y se presenta un nuevo recurso léxico adaptado al dominio turístico (eSOLHotel). Este nuevo lexicón usa el enfoque basado en corpus. Se han realizado varios experimentos usando una aproximación no supervisada para la clasificación de polaridad de las opiniones en la categoría de hoteles del corpus SFU. Los resultados obtenidos con el nuevo lexicón eSOLHotel superan los resultados obtenidos con otro lexicón de propósito general y nos animan a seguir trabajando en esta línea.Since Web 2.0 is the largest container for subjective expressions about different topics or issues expressed in all languages, the study of Sentiment Analysis has grown exponentially. In this work, we focus on Spanish polarity classification of hotel reviews and a new domain-dependent lexical resource (eSOLHotel) is presented. This new lexicon has been compiled following a corpus-based approach. We have carried out several experiments using an unsupervised approach for the polarity classification over the category of hotels from corpus SFU. The results obtained with the new lexicon eSOLHotel outperform the results with other general purpose lexicon.Esta investigación ha sido parcialmente financiada por el Fondo Europeo de Desarrollo Regional (FEDER), el proyecto ATTOS (TIN2012-38536-C03-0) del Gobierno de España y el proyecto AORESCU (P11-TIC-7684 MO) del gobierno autonómico de la Junta de Andalucía. Por último, el proyecto CEATIC (CEATIC-2013-01) de la Universidad de Jaén también ha financiado parcialmente este artículo

Idiom–based features in sentiment analysis: cutting the Gordian knot

In this paper we describe an automated approach to enriching sentiment analysis with idiom–based features. Specifically, we automated the development of the supporting lexico–semantic resources, which include (1) a set of rules used to identify idioms in text and (2) their sentiment polarity classifications. Our method demonstrates how idiom dictionaries, which are readily available general pedagogical resources, can be adapted into purpose–specific computational resources automatically. These resources were then used to replace the manually engineered counterparts in an existing system, which originally outperformed the baseline sentiment analysis approaches by 17 percentage points on average, taking the F–measure from 40s into 60s. The new fully automated approach outperformed the baselines by 8 percentage points on average taking the F–measure from 40s into 50s. Although the latter improvement is not as high as the one achieved with the manually engineered features, it has got the advantage of being more general in a sense that it can readily utilize an arbitrary list of idioms without the knowledge acquisition overhead previously associated with this task, thereby fully automating the original approach

The Today Tendency of Sentiment Classification

Sentiment classification has already been studied for many years because it has had many crucial contributions to many different fields in everyday life, such as in political activities, commodity production, and commercial activities. There have been many kinds of the sentiment analysis such as machine learning approaches, lexicon-based approaches, etc., for many years. The today tendency of the sentiment classification is as follows: (1) Processing many big data sets with shortening execution times (2) Having a high accuracy (3) Integrating flexibly and easily into many small machines or many different approaches. We will present each category in more details

Generación de recursos para Análisis de Opiniones en español

[ES] El Análisis de Sentimientos (AS) se refiere al tratamiento de la información subjetiva en los textos, sobretodo comentarios u opiniones personales. Una de las tareas básicas de AS es la clasificación de la polaridad de un texto determinado en un documento o frase, es decir, si la opinión expresada es positiva, negativa o neutra. Mucho se ha investigado en la clasificación de polaridad en documentos escritos en inglés. Sin embargo, actualmente cada vez más personas expresan comentarios u opiniones en su propio idioma. Para llevar a cabo esta labor es necesario el uso de los recursos lingüísticos (lexicones y corpora) que son escasos, cuando no inexistentes, en idiomas distintos al inglés. Por tales circunstancias, esta tesis tiene como objetivo la generación de nuevos recursos para el AS en español, tercer idioma con más relevancia en la web 2.0.[EN] Sentiment Analysis (SA) refers to the treatment of the subjective information in texts, product reviews, comments on blogs or personal opinions. One of the basic tasks in SA is classifying the polarity of a given text in a document, i.e., whether the opinion expressed is positive, negative, or neutral. Many studies have investigated the polarity classification in documents written in English. However, nowadays more and more people express their comments, opinions or points of view in their own language. For this reason, it is necessary to develop systems than can extract and analyze all this information in different languages. In this work we focus on polarity detection for Spanish reviews. We are mainly concerned with linguistic resources for Spanish sentiment analysis because, in addition to the lack of resources for this language in this area, it is currently the third most used language in the web 2.0.Tesis Univ. Jaén. Departamento de Informática- Leída el 28 de noviembre de 201

Unsupervised Aspect Discovery from Online Consumer Reviews

The success of on-line review websites has led to an overwhelming number of on-line consumer reviews. These reviews have become an important tool for consumers when making a decision to purchase a product. This growth has led to the need for applications that enable this information to be presented in a way that is meaningful. These applications often rely on domain specific semantic lexicons which are both expensive and time consuming to make. The following thesis proposes an unsupervised approach for product aspect discovery in on-line consumer reviews. We apply a two step hierarchical clustering process in which we first cluster based on the semantic similarity of the contexts of terms and then on the similarity of the hypernyms of the cluster members. The method also includes a process for assigning class labels to each of the clusters. Finally an experiment showing how the proposed methods can be used to measure aspect based sentiment is performed. The methods proposed in this thesis are evaluated on a set of 157,865 reviews from a major commercial website and found that the two-step clustering process increases cluster F-scores over a single round of clustering. Finally, the proposed methods are compared to a state of the art topic modelling approach by Titov and McDonald (2008)

On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training

Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind the social media texts or reviews, which has been a fundamental application to the real-world society. Since the early 2010s, ABSA has achieved extraordinarily high accuracy with various deep neural models. However, existing ABSA models with strong in-house performances may fail to generalize to some challenging cases where the contexts are variable, i.e., low robustness to real-world environments. In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training. First, we strengthen the current best-robust syntax-aware models by further incorporating the rich external syntactic dependencies and the labels with aspect simultaneously with a universal-syntax graph convolutional network. In the corpus perspective, we propose to automatically induce high-quality synthetic training data with various types, allowing models to learn sufficient inductive bias for better robustness. Last, we based on the rich pseudo data perform adversarial training to enhance the resistance to the context perturbation and meanwhile employ contrastive learning to reinforce the representations of instances with contrastive sentiments. Extensive robustness evaluations are conducted. The results demonstrate that our enhanced syntax-aware model achieves better robustness performances than all the state-of-the-art baselines. By additionally incorporating our synthetic corpus, the robust testing results are pushed with around 10% accuracy, which are then further improved by installing the advanced training strategies. In-depth analyses are presented for revealing the factors influencing the ABSA robustness.Comment: Accepted in ACM Transactions on Information System

From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

Sentiment analysis with limited training data

Sentiments are positive and negative emotions, evaluations and stances. This dissertation focuses on learning based systems for automatic analysis of sentiments and comparisons in natural language text. The proposed approach consists of three contributions: 1. Bag-of-opinions model: For predicting document-level polarity and intensity, we proposed the bag-of-opinions model by modeling each document as a bag of sentiments, which can explore the syntactic structures of sentiment-bearing phrases for improved rating prediction of online reviews. 2. Multi-experts model: Due to the sparsity of manually-labeled training data, we designed the multi-experts model for sentence-level analysis of sentiment polarity and intensity by fully exploiting any available sentiment indicators, such as phrase-level predictors and sentence similarity measures. 3. LSSVMrae model: To understand the sentiments regarding entities, we proposed LSSVMrae model for extracting sentiments and comparisons of entities at both sentence and subsentential level. Different granularity of analysis leads to different model complexity, the finer the more complex. All proposed models aim to minimize the use of hand-labeled data by maximizing the use of the freely available resources. These models explore also different feature representations to capture the compositional semantics inherent in sentiment-bearing expressions. Our experimental results on real-world data showed that all models significantly outperform the state-of-the-art methods on the respective tasks.Sentiments sind positive und negative Gefühle, Bewertungen und Einstellungen. Die Dissertation beschäftigt sich mit lernbasierten Systemen zur automatischen Analyse von Sentiments und Vergleichen in Texten in natürlicher Sprache. Die vorliegende Abeit leistet dazu drei Beiträge: 1. Bag-of-Opinions-Modell: Zur Vorhersage der Polarität und Intensität auf Dokumentenebene haben wir das Bag-of-Opinions-Modell vorgeschlagen, bei dem jedes Dokument als ein Beutel Sentiments dargestellt wird. Das Modell kann die syntaktischen Strukturen von subjektiven Ausdrücken untersuchen, um eine verbesserte Bewertungsvorhersage von Online-Rezensionen zu erzielen. 2. Multi-Experten-Modell: Wegen des Mangels an manuell annotierten Trainingsdaten haben wir das Multi-Experten-Modell entworfen, um die Sentimentpolarität und -intensität auf Satzebene zu analysieren. Das Modell kann alle möglichen Sentiment-Indikatoren verwenden, wie Prädiktoren auf Phrasenebene und Ähnlichkeitsmaße von Sätzen. 3. LSSVMrae-Modell: Um Sentiments von Entitäten zu verstehen, wir haben wir das LSSVMrae-Modell zur Extraktion von Sentiments und Vergleichen von Entitäten auf Satz- und Ausdrucksebene vorgeschlagen. Die unterschiedliche Granularität der Analyse führt zu unterschiedlicher Modellkomplexität; je feiner, desto komplexer. Alle vorgeschlagenen Modelle zielen darauf ab, möglichst wenige manuell annotierte Daten und möglichst viele frei verfügbare Ressourcen zu verwenden. Diese Modelle untersuchen auch verschiedene Merkmalsdarstellungen, um die Kompositionssemantik abzubilden, die subjektiven Ausdrücken inhärent ist. Die Ergebnisse unserer Experimente mit Realweltdaten haben gezeigt, dass alle Modelle für die jeweiligen Aufgaben deutlich bessere Leistungen erzielen als die modernsten Methoden