Theory-driven text analysis has made extensive use of psychological concept dictionaries, leading to a wide range of important results. These dictionaries have generally been applied through word count methods which have proven to be both simple and effective. In this paper, we introduce Distributed Dictionary Representations (DDR), a method that applies psychological dictionaries using semantic similarity rather than word counts. This allows for the measurement of the similarity between dictionaries and spans of text ranging from complete documents to individual words. We show how DDR enables dictionary authors to place greater emphasis on construct validity without sacrificing linguistic coverage. We further demonstrate the benefits of DDR on two real-world tasks and finally conduct an extensive study of the interaction between dictionary size and task performance. These studies allow us to examine how DDR and word count methods complement one another as tools for applying concept dictionaries and where each is best applied. Finally, we provide references to tools and resources to make this method both available and accessible to a broad psychological audience. Keywords Methodological innovation · Text analysis · Semantic representation · Dictionary-based text analysisElectronic supplementary material The online version of this article

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Drawing from a common lexicon of semantic units, humans fashion narratives whose meaning transcends that of their individual utterances. However, while brain regions that represent lower-level semantic units, such as words and sentences, have been identified, questions remain about the neural representation of narrative comprehension, which involves inferring cumulative meaning. To address these questions, we exposed English, Mandarin and Farsi native speakers to native language translations of the same stories during fMRI scanning. Using a new technique in natural language processing, we calculated the distributed representations of these stories (capturing the meaning of the stories in high-dimensional semantic space), and demonstrate that using these representations we can identify the specific story a participant was reading from the neural data. Notably, this was possible even when the distributed representations were calculated using stories in a different language than the participant was reading.Relying on over 44 billion classifications, our results reveal that identification relied on a collection of brain regions most prominently located in the default mode network.These results demonstrate that neuro-semantic encoding of narratives happens at levels higher than individual semantic units and that this encoding is systematic across both individuals and languages. NEURO-SEMANTIC REPRESENTATION OF STORIES 3 Decoding the Neural Representation of Story Meanings across LanguagesOne of the defining characteristics of human language is its capacity for semantic extensibility. Drawing from a common lexicon of morphemes and words, humans generate and comprehend sophisticated, higher-level utterances that transcend the sum of their individual units. This is perhaps best exemplified in stories, in which sequences of events invite inferences about the intentions and motivations of characters, about cause and effect, and about theme and message. The kind of meaning that emerges over time as one listens to a story is not easily captured by analysis at the word level alone.Further, a necessary condition for generating higher-level semantic constructs is that speakers of the same language infer similar meanings from expressions of both lower and higher level semantic units. For example, it can be assumed that when speakers of the same language listen to stories, the perceived meanings of these stories have much in NEURO-SEMANTIC REPRESENTATION OF STORIES 4In this work, our aim is to move beyond word-level semantics to investigate neuro-semantic representations at the story-level across three different languages.Specifically, we set out to determine if there are systematic patterns in the neuro-semantic representations of stories beyond those corresponding to word-level stimuli. Our aim is motivated by the long-standing understanding that discourse representations are different from the sum of all of their lexical or clausal parts. Most psycholinguistic models of discourse processing are concerned with the con...

Do appeals to moral values promote charitable donation during natural disasters? Using Distributed Dictionary Representation, we analyze tweets posted during Hurricane Sandy to explore associations between moral values and charitable donation sentiment. We then derive hypotheses from the observed associations and test these hypotheses across a series of preregistered experiments that investigate the effects of moral framing on perceived donation motivation (Studies 2 & 3), hypothetical donation (Study 4), and real donation behavior (Study 5). Overall, we find consistent positive associations between moral care and loyalty framing with donation sentiment and donation motivation. However, in contrast with people's perceptions, we also find that moral frames may not actually have reliable effects on charitable donation, as measured by hypothetical indications of donation and real donation behavior. Overall, this work demonstrates that theoretically constrained, exploratory social media analyses can be used to generate viable hypotheses, but also that such approaches should be paired with rigorous controlled experiments.

According to American Heart Association report, cardiovascular diseases are one of the five leading causes of death in the world. Coronary Artery Disease (CAD) is the most common fatal heart disease, and is the subject of large body of studies. According to prevalence of CAD, early diagnosis of this disease is very important. The most reliable method for CAD diagnosis is angiography, but it is costly, time-consuming, and hazardous. Therefore in order to predict such diseases, study of non-invasive methods such as analysis and mining of patients' medical information is becoming popular, and has proved to be effective. Unfortunately, majority of approaches in the literature rely on a limited and small set of medical features for disease detection. This paper aims to examine effects of set of features; including lab data and echo information on CAD diagnosis which some of them were not considered in previous studies. The data set consists of the information gathered from 303 random visitors to Tehran's Shaheed Rajaei Cardiovascular, Medical and Research Center which is one of the largest heart hospitals in Asia. The method used in this research was data mining. Several classification algorithms were adopted to analyze the data set, including SMO, Naïve Bayes, C4.5 and AdaBoost. According to the comprehensive set of features used, the obtained classification accuracy exceeded 82 percent. Results showed that new added features including Region with RWMA and Ejection Fraction (EF) have a large effect on CAD.