Sentiment Analysis of Text Guided by Semantics and Structure

As moods and opinions play a pivotal role in various business and economic processes, keeping track of one's stakeholders' sentiment can be of crucial importance to decision makers. Today's abundance of user-generated content allows for the automated monitoring of the opinions of many stakeholders, like consumers. One challenge for such automated sentiment analysis systems is to identify whether pieces of natural language text are positive or negative. Typical methods of identifying this polarity involve low-level linguistic analysis. Existing systems predominantly use morphological, lexical, and syntactic cues for polarity, like a text's words, their parts-of-speech, and negation or amplification of the conveyed sentiment. This dissertation argues that the polarity of text can be analysed more accurately when additionally accounting for semantics and structure. Polarity classification performance can benefit from exploiting the interactions that emoticons have on a semantic level with words – emoticons can express, stress, or disambiguate sentiment. Furthermore, semantic relations between and within languages can help identify meaningful cues for sentiment in multi-lingual polarity classification. An even better understanding of a text's conveyed sentiment can be obtained by guiding automated sentiment analysis by the rhetorical structure of the text, or at least of its most sentiment-carrying segments. Thus, the sentiment in, e.g., conclusions can be treated differently from the sentiment in background information. The findings of this dissertation suggest that the polarity of natural language text should not be determined solely based on what is said. Instead, one should account for how this message is conveyed as well

Sentiment Analysis of Text Guided by Semantics and Structure

As moods and opinions play a pivotal role in various business and economic processes, keeping track of one's stakeholders' sentiment can be of crucial importance to decision makers. Today's abundance of user-generated content allows for the automated monitoring of the opinions of many stakeholders, like consumers. One challenge for such automated sentiment analysis systems is to identify whether pieces of natural language text are positive or negative. Typical methods of identifying this polarity involve low-level linguistic analysis. Existing systems predominantly use morphological, lexical, and syntactic cues for polarity, like a text's words, their parts-of-speech, and negation or amplification of the conveyed sentiment. This dissertation argues that the polarity of text can be analysed more accurately when additionally accounting for semantics and structure. Polarity classification performance can benefit from exploiting the interactions that emoticons have on a semantic level with words – emoticons can express, stress, or disambiguate sentiment. Furthermore, semantic relations between and within languages can help identify meaningful cues for sentiment in multi-lingual polarity classification. An even better understanding of a text's conveyed sentiment can be obtained by guiding automated sentiment analysis by the rhetorical structure of the text, or at least of its most sentiment-carrying segments. Thus, the sentiment in, e.g., conclusions can be treated differently from the sentiment in background information. The findings of this dissertation suggest that the polarity of natural language text should not be determined solely based on what is said. Instead, one should account for how this message is conveyed as well

Semantics-based information extraction for detecting economic events

As today's financial markets are sensitive to breaking news on economic events, accurate and timely automatic identification of events in news items is crucial. Unstructured news items originating from many heterogeneous sources have to be mined in order to extract knowledge useful for guiding decision making processes. Hence, we propose the Semantics-Based Pipeline for Economic Event Detection (SPEED), focusing on extracting financial events from news articles and annotating these with meta-data at a speed that enables real-time use. In our implementation, we use some components of an existing framework as well as new components, e.g., a high-performance Ontology Gazetteer, a Word Group Look-Up component, a Word Sense Disambiguator, and components for detecting economic events. Through their interaction with a domain-specific ontology, our novel, semantically enabled components constitute a feedback loop which fosters future reuse of acquired knowledge in the event detection process

Resistência a Septoria lycopersici em espécies de Solanum (Secção Lycopersicon) e em progênies de S. lycopersicum × S. peruvianum

Septoria leaf spot (Septoria lycopersici) is one of the major fungal diseases of tomatoes (Solanum lycopersicum) in tropical and subtropical regions with humid climates and/or in areas cultivated under sprinkler irrigation systems. Sources of resistance have been found in accessions of Solanum (section Lycopersicon) species. However, many of the described sources are not effective under Brazilian conditions. The objective of this work was to evaluate wild and cultivated Solanum (section Lycopersicon) germplasm to S. lycopersici isolates. A collection of 124 accessions was initially evaluated under greenhouse conditions. Ten accessions were highly resistance (HR), whereas 33 were classified as having a resistant (R) response to S. lycopersici isolates. Field evaluation was also conducted with a sub-set of accessions identified as either HR or R in the greenhouse experiment. This field evaluation confirmed greenhouse tests and indicated the presence of some potential sources of rate-reducing resistance. One highly resistant and eight resistant S. habrochaites accessions were identified as being resistant under both conditions, confirming that this wild species is one of the most promising sources of resistance to S. lycopersici. Five new sources with high levels of resistance were found in S. peruvianum accessions (PI-306811, CNPH-1036, LA-1910, LA-1984 and LA-2744). One accession derived from an interspecific cross between S. lycopersicum and S. peruvianum was also found to be highly resistant and might be useful to introgress resistance factors from this wild species into cultivated tomato germplasm. However, additional breeding efforts will be necessary to introgress into the cultivated tomato the resistance factors identified in other S. peruvianum accessions due to the presence of natural crossing barriers between the two species.A mancha-de-septória (Septoria lycopersici) é importante doença fúngica do tomateiro (Solanum lycopersicum) em áreas tropicais e subtropicais com alta umidade ou quando esta hortaliça é cultivada sob irrigação por aspersão. Fontes de resistência têm sido encontradas em germoplasma de Solanum (secção Lycopersicon). No entanto, muitas das fontes descritas não funcionam nas condições brasileiras. Avaliou-se uma coleção de germoplasma de tomate cultivado e selvagem (Solanum secção Lycopersicon) visando identificar novas fontes de elevada resistência. Uma coleção de 124 acessos foi inicialmente avaliada sob condições de casa de vegetação. Somente dez acessos foram classificados como altamente resistentes e 33 foram classificados como resistentes. Um ensaio de campo foi também conduzido com um subconjunto de acessos promissores identificados no primeiro experimento. Foi confirmada a resposta da maioria dos acessos avaliados em casa de vegetação e indicou a presença de fontes de resistência capazes de reduzir a taxa de progresso da doença. Um acesso de S. habrochaites com elevada resistência e oito acessos resistentes foram identificados, confirmando que esta espécie representa uma das mais promissoras fontes de genes de resistência a S. lycopersici. Cinco novas fontes com elevados níveis de resistência foram identificadas em acessos da espécie S. peruvianum (PI-306811, CNPH-1036, LA-1910, LA-1984 e LA-2744). Um acesso, derivado de cruzamento interespecífico entre S. lycopersicum e S. peruvianum também mostrou-se altamente resistente e poderá ser útil na introgressão deste(s) gene(s) em germoplasma de tomateiro cultivado. No entanto, esforços adicionais de melhoramento serão necessários para transferir para o tomateiro cultivado os fatores de resistência identificados em outros acessos de S. peruvianum, uma vez que existem barreiras naturais de cruzamentos entre estas duas espécies