Inferring Interpersonal Relations in Narrative Summaries

Characterizing relationships between people is fundamental for the understanding of narratives. In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries. We formulate the problem as a joint structured prediction for each narrative, and present a model that combines evidence from linguistic and semantic features, as well as features based on the structure of the social community in the text. We also provide a clustering-based approach that can exploit regularities in narrative types. e.g., learn an affinity for love-triangles in romantic stories. On a dataset of movie summaries from Wikipedia, our structured models provide more than a 30% error-reduction over a competitive baseline that considers pairs of characters in isolation

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Sentiment analysis em relatórios anuais de empresas brasileiras com ações negociadas na BM&FBovespa

We investigated the association between the tone of annual reports issued by a sample of listed Brazilian firms and market variables (abnormal returns, trading volume and price volatility). The tone was measured using sentiment analysis techniques (Liu et al., 2005; Liu, 2010). As in Loughran and McDonald (2011), we developed and used lists of positive, negative, litigious, uncertainty-related and modal words in Portuguese to assess the tone of annual reports. Using a sample of 829 annual reports from 1997 to 2009, we observed a weak association between the tone of annual reports and stock market variables in Brazil. Additionally, we considered a sub-sample prior to GAAP changes in Brazil (1997-2007) and our results are maintained. Contrary to other studies using data from the United States, we found that the tone of annual reports released by Brazilian firms is not conducive to estimating returns.Keywords: sentiment analysis, textual sentiment, positive words, negative words, annual reports.Foi investigada a associação entre o tom dos relatórios anuais divulgados por uma amostra de empresas brasileiras listadas na BM&FBovespa com variáveis de mercado (retornos anormais, volume de negociação e volatilidade). O tom dos relatórios foi medido por meio de técnicas de sentiment analysis (Liu et al., 2005; Liu, 2010). Seguindo o trabalho de Loughran e McDonald (2011), foram construídas listas de palavras positivas, negativas, litigiosas e de incerteza, além de modais, para construir uma medida de tom dos relatórios. A análise das 829 observações de relatórios, referentes ao período de 1997 a 2009, resultou na identificação de uma fraca associação entre as medidas de tom dos textos e as variáveis retorno anormal, volatilidade e volume anormal. Adicionalmente, realizou-se uma análise de robustez excluindo-se da amostra os anos de transição de regime contábil no Brasil (2008 e 2009), e nossos resultados se mantêm. Contrariamente aos estudos anteriores que usaram dados do mercado norte-americano, o tom dos relatórios divulgados pelas empresas brasileiras não contribui para melhorar as estimativas de retorno.Palavras-chave: sentiment analysis, tom textual, palavras positivas, palavras negativas, relatórios anuais

An Analysis of Major Acquisition Reforms through Text Mining and Grounded Theory Design

Cost growth is an established phenomenon within Defense Acquisition that the US Government has attempted to abolish for decades through seemingly endless cycles of reform. Dozens of experts and senior leaders within the acquisition community have published their notions on the reasons for cost growth, nevertheless, legislation has yet to eradicate this presumed conundrum. For this reason, this research is aimed at identifying existing trends within past major Defense Acquisition Reform legislation, as well as in a compendium of views from leaders within the Defense Acquisition community on the efficacy of acquisition reform, to determine the possible disconnect. To accomplish this goal, this research takes a qualitative approach, utilizing various Text Mining methodologies (word frequency, word relationships, term frequency-inverse document frequency, sentiment analysis, and topic modeling), along with Grounded Theory Design, to analyze the major reforms and expert views. The results of this research corroborate the current literature’s claim that past Defense Acquisition reforms have not been able to sufficiently address the root causes of cost growth, and identifies six potential root causes of cost growth: Strategy, the Industrial Base, Risk Management, the Requirements and Research, Development, Test, and Evaluation (RDT&E) Processes, the Workforce, and Cost Estimates and the Planning, Programming, Budget, and Execution (PPBE) Process

Sentiment analysis: text, pre-processing, reader views and cross domains

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonSentiment analysis has emerged as a field that has attracted a significant amount of attention since it has a wide variety of applications that could benefit from its results, such as news analytics, marketing, question answering, knowledge management and so on. This area, however, is still early in its development where urgent improvements are required on many issues, particularly on the performance of sentiment classification. In this thesis, three key challenging issues affecting sentiment classification are outlined and innovative ways of addressing these issues are presented. First, text pre-processing has been found crucial on the sentiment classification performance. Consequently, a combination of several existing preprocessing methods is proposed for the sentiment classification process. Second, text properties of financial news are utilised to build models to predict sentiment. Two different models are proposed, one that uses financial events to predict financial news sentiment, and the other uses a new interesting perspective that considers the opinion reader view, as opposed to the classic approach that examines the opinion holder view. A new method to capture the reader sentiment is suggested. Third, one characteristic of financial news is that it stretches over a number of domains, and it is very challenging to infer sentiment between different domains. Various approaches for cross-domain sentiment analysis have been proposed and critically evaluated