On Assessing the Sentiment of General Tweets

Abstract. With the explosion of publicly accessible social data, sentiment analysis has emerged as an important task with applications in e-commerce, politics, and social sciences. Hence, so far, researchers have largely focused on sentiment analysis of texts involving entities such as products, persons, institutions, and events. However, a significant amount of chatter on microblogging websites may not be directed at a particular entity. On Twitter, users share information on their general state of mind, details about how their day went, their plans for the next day, or just conversational chatter with other users. In this paper, we look into the problem of assessing the sentiment of publicly available general stream of tweets. Assessing the sentiment of such tweets helps us assess the overall sentiment being expressed in a geographic location or by a set of users (scoped through some means), which has applications in social sciences, psychology, and health sciences. The only prior effor

Semi-Supervised Learning For Identifying Opinions In Web Content

Thesis (Ph.D.) - Indiana University, Information Science, 2011Opinions published on the World Wide Web (Web) offer opportunities for detecting personal attitudes regarding topics, products, and services. The opinion detection literature indicates that both a large body of opinions and a wide variety of opinion features are essential for capturing subtle opinion information. Although a large amount of opinion-labeled data is preferable for opinion detection systems, opinion-labeled data is often limited, especially at sub-document levels, and manual annotation is tedious, expensive and error-prone. This shortage of opinion-labeled data is less challenging in some domains (e.g., movie reviews) than in others (e.g., blog posts). While a simple method for improving accuracy in challenging domains is to borrow opinion-labeled data from a non-target data domain, this approach often fails because of the domain transfer problem: Opinion detection strategies designed for one data domain generally do not perform well in another domain. However, while it is difficult to obtain opinion-labeled data, unlabeled user-generated opinion data are readily available. Semi-supervised learning (SSL) requires only limited labeled data to automatically label unlabeled data and has achieved promising results in various natural language processing (NLP) tasks, including traditional topic classification; but SSL has been applied in only a few opinion detection studies. This study investigates application of four different SSL algorithms in three types of Web content: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. SSL algorithms are also evaluated for their effectiveness in sparse data situations and domain adaptation. Research findings suggest that, when there is limited labeled data, SSL is a promising approach for opinion detection in Web content. Although the contributions of SSL varied across data domains, significant improvement was demonstrated for the most challenging data domain--the blogosphere--when a domain transfer-based SSL strategy was implemented

Cross-lingual sentiment classification using semi-supervised learning

Cross-lingual sentiment classification aims to utilize annotated sentiment resources in one language for text sentiment classification in another language. Automatic machine translation services are the most commonly used tools to directly project information from one language into another. However, different term distribution between translated and original documents, translation errors and different intrinsic structure of documents in various languages are the problems that lead to low performance in sentiment classification. Furthermore, due to the existence of different linguistic terms in different languages, translated documents cannot cover all vocabularies which exist in the original documents. The aim of this thesis is to propose an enhanced framework for cross-lingual sentiment classification to overcome all the aforementioned problems in order to improve the classification performance. Combination of active learning and semi-supervised learning in both single view and bi-view frameworks is proposed to incorporate unlabelled data from the target language in order to reduce term distribution divergence. Using bi-view documents can partially alleviate the negative effects of translation errors. Multi-view semisupervised learning is also used to overcome the problem of low term-coverage through employing multiple source languages. Features that are extracted from multiple source languages can cover more vocabularies from test data and consequently, more sentimental terms can be used in the classification process. Content similarities of labelled and unlabelled documents are used through graphbased semi-supervised learning approach to incorporate the structure of documents in the target language into the learning process. Performance evaluation performed on sentiment data sets in four different languages certifies the effectiveness of the proposed approaches in comparison to the well-known baseline classification methods. The experiments show that incorporation of unlabelled data from the target language can effectively improve the classification performance. Experimental results also show that using multiple source languages in the multi-view learning model outperforms other methods. The proposed framework is flexible enough to be applied on any new language, and therefore, it can be used to develop multilingual sentiment analysis systems

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

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