Sentiment analysis on twitter for the portuguese language

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.

Opinion Mining

In this thesis we study the field of opinion mining by giving a comprehensive review of the available research that has been done in this topic. Also using this available knowledge we present a case study of a multilevel opinion mining system for a student organization's sales management system. We describe the field of opinion mining by discussing its historical roots, its motivations and applications as well as the different scientific approaches that have been used to solve this challenging problem of mining opinions. To deal with this huge subfield of natural language processing, we first give an abstraction of the problem of opinion mining and describe the theoretical frameworks that are available for dealing with appraisal language. Then we discuss the relation between opinion mining and computational linguistics which is a crucial pre-processing step for the accuracy of the subsequent steps of opinion mining. The second part of our thesis deals with the semantics of opinions where we describe the different ways used to collect lists of opinion words as well as the methods and techniques available for extracting knowledge from opinions present in unstructured textual data. In the part about collecting lists of opinion words we describe manual, semi manual and automatic ways to do so and give a review of the available lists that are used as gold standards in opinion mining research. For the methods and techniques of opinion mining we divide the task into three levels that are the document, sentence and feature level. The techniques that are presented in the document and sentence level are divided into supervised and unsupervised approaches that are used to determine the subjectivity and polarity of texts and sentences at these levels of analysis. At the feature level we give a description of the techniques available for finding the opinion targets, the polarity of the opinions about these opinion targets and the opinion holders. Also at the feature level we discuss the various ways to summarize and visualize the results of this level of analysis. In the third part of our thesis we present a case study of a sales management system that uses free form text and that can benefit from an opinion mining system. Using the knowledge gathered in the review of this field we provide a theoretical multi level opinion mining system (MLOM) that can perform most of the tasks needed from an opinion mining system. Based on the previous research we give some hints that many of the laborious market research tasks that are done by the sales force, which uses this sales management system, can improve their insight about their partners and by that increase the quality of their sales services and their overall results.

Context-free and context-sensitive dynamics in recurrent neural networks

Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languages. The dynamics of such networks is usually based on damped oscillation around fixed points in state space and requires that the dynamical components are arranged in certain ways. It is shown that qualitatively similar dynamics with similar constraints hold for a(n)b(n)c(n), a context-sensitive language. The additional difficulty with a(n)b(n)c(n), compared with the context-free language a(n)b(n), consists of 'counting up' and 'counting down' letters simultaneously. The network solution is to oscillate in two principal dimensions, one for counting up and one for counting down. This study focuses on the dynamics employed by the sequential cascaded network, in contrast to the simple recurrent network, and the use of backpropagation through time. Found solutions generalize well beyond training data, however, learning is not reliable. The contribution of this study lies in demonstrating how the dynamics in recurrent neural networks that process context-free languages can also be employed in processing some context-sensitive languages (traditionally thought of as requiring additional computation resources). This continuity of mechanism between language classes contributes to our understanding of neural networks in modelling language learning and processing.

Metodi di previsione di indici di borsa con tecniche di text & opinion mining applicate a twitter

Il problema relativo alla predizione, la ricerca di pattern predittivi all‘interno dei dati, è stato studiato ampiamente. Molte metodologie robuste ed efficienti sono state sviluppate, procedimenti che si basano sull‘analisi di informazioni numeriche strutturate. Quella testuale, d‘altro canto, è una tipologia di informazione fortemente destrutturata. Quindi, una immediata conclusione, porterebbe a pensare che per l‘analisi predittiva su dati testuali sia necessario sviluppare metodi completamente diversi da quelli ben noti dalle tecniche di data mining. Un problema di predizione può essere risolto utilizzando invece gli stessi metodi : dati testuali e documenti possono essere trasformati in valori numerici, considerando per esempio l‘assenza o la presenza di termini, rendendo di fatto possibile una utilizzazione efficiente delle tecniche già sviluppate. Il text mining abilita la congiunzione di concetti da campi di applicazione estremamente eterogenei. Con l‘immensa quantità di dati testuali presenti, basti pensare, sul World Wide Web, ed in continua crescita a causa dell‘utilizzo pervasivo di smartphones e computers, i campi di applicazione delle analisi di tipo testuale divengono innumerevoli. L‘avvento e la diffusione dei social networks e della pratica di micro blogging abilita le persone alla condivisione di opinioni e stati d‘animo, creando un corpus testuale di dimensioni incalcolabili aggiornato giornalmente. Le nuove tecniche di Sentiment Analysis, o Opinion Mining, si occupano di analizzare lo stato emotivo o la tipologia di opinione espressa all‘interno di un documento testuale. Esse sono discipline attraverso le quali, per esempio, estrarre indicatori dello stato d‘animo di un individuo, oppure di un insieme di individui, creando una rappresentazione dello stato emotivo sociale. L‘andamento dello stato emotivo sociale può condizionare macroscopicamente l‘evolvere di eventi globali? Studi in campo di Economia e Finanza Comportamentale assicurano un legame fra stato emotivo, capacità nel prendere decisioni ed indicatori economici. Grazie alle tecniche disponibili ed alla mole di dati testuali continuamente aggiornati riguardanti lo stato d‘animo di milioni di individui diviene possibile analizzare tali correlazioni. In questo studio viene costruito un sistema per la previsione delle variazioni di indici di borsa, basandosi su dati testuali estratti dalla piattaforma di microblogging Twitter, sotto forma di tweets pubblici; tale sistema include tecniche di miglioramento della previsione basate sullo studio di similarità dei testi, categorizzandone il contributo effettivo alla previsione.

Evaluating the robustness of EmotiBlog for sentiment analysis and opinion mining

Preliminary research demonstrated the EmotiBlog annotated corpus relevance as a Machine Learning resource to detect subjective data. In this paper we compare EmotiBlog with the JRC Quotes corpus in order to check the robustness of its annotation. We concentrate on its coarse-grained labels and carry out a deep Machine Learning experimentation also with the inclusion of lexical resources. The results obtained show a similarity with the ones obtained with the JRC Quotes corpus demonstrating the EmotiBlog validity as a resource for the SA task.

EmotiBlog: towards a finer-grained sentiment analysis and its application to opinion mining

Comunicación presentada en las IV Jornadas TIMM, Torres (Jaén), 7-8 abril 2011.

Text mining na análise de sentimentos em contextos de big data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

Progettazione di un sistema di Social Intelligence e Sentiment Analysis per un'azienda del settore consumer goods

Progettazione di un sistema di Social Intelligence e Sentiment Analysis per un'azienda del settore consumer goods

Studio ed analisi di tecniche di opinion mining per contesti di promozione turistica su piattaforme di social media

Negli ultimi anni Internet ha cambiato le modalità di creazione e distribuzione delle informazioni turistiche. Un ruolo fondamentale viene ricoperto dalle piattaforme di social media, tecnologie che permettono ai consumatori di condividere le proprie esperienze ed opinioni. Diventa necessario, quindi, comprendere i cambiamenti in queste tecnologie e nel comportamento dei viaggiatori per poter applicare strategie di marketing di successo. In questo studio, utilizzando Opinion Finder, un software spesso impiegato nel campo dell'opinion mining, si esamineranno da un punto di vista qualitativo i post e commenti estratti da alcuni profili degli enti di promozione turistica nazionale in Europa, dividendo l'analisi per fattori che possono influenzare il sentimento degli utenti. Attraverso i risultati ottenuti, si può dimostrare che l'analisi delle opinioni e del sentimento si presenta come un ottimo strumento per evidenziare possibili fenomeni utili per la pianificazione di strategie di marketing per gli enti. Studi futuri potrebbero migliorare la valutazione di questi dati attraverso la creazione di un corpus di apprendimento per il software che contenga testi relativi al mondo del turismo e permettendo ad Opinion Finder di incrementare la validità della classificazione del sentimento, contestualizzando le espressioni in maniera corretta.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

Joint sentiment/topic model for sentiment analysis

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.

Sentiment analysis in Twitter

Gli ultimi anni hanno visto una crescita esponenziale nell’uso dei social media (recensioni, forum, discussioni, blog e social network); le persone e le aziende utilizzano sempre più le informazioni (opinioni e preferenze) pubblicate in questi mezzi per il loro processo decisionale. Tuttavia, il monitoraggio e la ricerca di opinioni sul Web da parte di un utente o azienda risulta essere un problema molto arduo a causa della proliferazione di migliaia di siti; in più ogni sito contiene un enorme volume di testo non sempre decifrabile in maniera ottimale (pensiamo ai lunghi messaggi di forum e blog). Inoltre, è anche noto che l’analisi soggettiva delle informazioni testuali è passibile di notevoli distorsioni, ad esempio, le persone tendono a prestare maggiore attenzione e interesse alle opinioni che risultano coerenti alle proprie attitudini e preferenze. Risulta quindi necessario l’utilizzo di sistemi automatizzati di Opinion Mining, per superare pregiudizi soggettivi e limitazioni mentali, al fine di giungere ad una metodologia di Sentiment Analysis il più possibile oggettiva.

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.