Positive words carry less information than negative words

We show that the frequency of word use is not only determined by the word length \cite{Zipf1935} and the average information content \cite{Piantadosi2011}, but also by its emotional content. We have analyzed three established lexica of affective word usage in English, German, and Spanish, to verify that these lexica have a neutral, unbiased, emotional content. Taking into account the frequency of word usage, we find that words with a positive emotional content are more frequently used. This lends support to Pollyanna hypothesis \cite{Boucher1969} that there should be a positive bias in human expression. We also find that negative words contain more information than positive words, as the informativeness of a word increases uniformly with its valence decrease. Our findings support earlier conjectures about (i) the relation between word frequency and information content, and (ii) the impact of positive emotions on communication and social links.Comment: 16 pages, 3 figures, 3 table

Information Processing In Anxiety And Depression: Attention Responses To Mood Congruent Stimuli

Previous research (e.g., MacLeod & Mathews, 1990) has found that anxious individuals show an attentional bias towards negative information, but evidence for such a bias in depressed individuals is equivocal. Conversely, there are fairly consistent findings that depressed individuals display a recall bias for negative information, whereas the findings for anxious individuals are mixed. However, task demands from this research may not have allowed anxious and depressed subjects to process information to the same extent. In the present study, 15 clinically depressed, 15 clinically anxious, 16 community control, 17 mildly depressed, 19 mildly anxious, and 17 nonclinical control subjects were tested on three attentional (modified dot probe, lexical decision, and negative priming) and two memory (word recall, and word completion) tasks using positive and negative words that were related to anxiety, depression, or a control condition. Clinically anxious and clinically depressed subjects both showed that some types of negative information (e.g., anxiety related) were more accessible than positive, but others were not (e.g., depression related, control). Also, clinically depressed subjects showed a tendency to disproportionately attend to negative information in general, whereas clinically anxious subjects avoided it. However, clinically depressed subjects were found to be slower to process information, and this effect could not be accounted for by motor retardation alone. It was concluded that clinically anxious and clinically depressed individuals recognize and respond to negative information in a similar fashion, except that clinically depressed individuals are slower in general to carry out these processes. The results from the two memory tasks indicated that clinically depressed subjects show a recall advantage for negative information. Clinically anxious subjects showed a similar, but less robust pattern. On all tasks, nonclinical samples showed similar, but less pervasive robust effects as their clinical counterparts. Overall, the results suggest that anxiety and depression are characterized by similar attentional biases, except that depressed individuals are slower processors. This difference may produce divergent patterns in later cognitive processes (e.g., memory) or their products

Text Analytics Methods for Sentence-level Sentiment Analysis

Opinions have important effects on the process of decision making. With the explosion of text information on networks, sentiment analysis, which aims at predicting the opinions of people about specific entities, has become a popular tool to make sense of countless text information. There are multiple approaches for sentence-level sentiment analysis, including machine-learning methods and lexicon-based methods. In this MSc thesis we studied two typical sentiment analysis techniques -- AFINN and RNTN, which are also the representation of lexicon-based and machine-learning methods, respectively. The assumption of a lexicon-based method is that the sum of sentiment orientation of each word or phrase predicts the contextual sentiment polarity. AFINN is a word list with sentiment strength ranging from -5 to +5, which is constructed with the inclusion of Internet slang and obscene words. With AFINN, we extract sentiment words from sentences and sentiment scores are then assigned to these words. The sentiment of a sentence is aggregated as the sum of scores from all its words. The Stanford Sentiment Treebank is a corpus with labeled parse trees, which provides the community with the possibility to train compositional models based on supervised machine learning techniques. The labels of Stanford Sentiment Treebank involve 5 categories: negative, somewhat negative, neutral, somewhat positive and positive. Compared to the standard recursive neural network (RNN) and Matrix-Vector RNN, Recursive Neural Tensor Network (RNTN) is a more powerful composition model to compute compositional vector representations for input sentences. Dependent on the Stanford Sentiment Treebank, RNTN can predict the sentiment of input sentences by its computed vector representations. With the benchmark datasets that cover diverse data sources, we carry out a thorough comparison between AFINN and RNTN. Our results highlight that although RNTN is much more complicated than AFINN, the performance of RNTN is not better than that of AFINN. To some extent, AFINN is more simple, more generic and takes less computation resources than RNTN in sentiment analysis

Memory for Emotionally Provocative Words in Alexithymia: A Role for Stimulus Relevance

Alexithymia is associated with emotion processing deficits, particularly for negative emotional information. However, also common are a high prevalence of somatic symptoms and the perception of somatic sensations as distressing. Although little research has yet been conducted on memory in alexithymia, we hypothesized a paradoxical effect of alexithymia on memory. Specifically, recall of negative emotional words was expected to be reduced in alexithymia, while memory for illness words was expected to be enhanced in alexithymia. Eighty-five high or low alexithymia participants viewed and rated arousing illness-related ( pain ), emotionally positive ( thrill ), negative ( hatred ), and neutral words ( horse ). Recall was assessed 45 min later. High alexithymia participants recalled significantly fewer negative emotion words but also more illness-related words than low alexithymia participants. The results suggest that personal relevance can shape cognitive processing of stimuli, even to enhance retention of a subclass of stimuli whose retention is generally impaired in alexithymia

Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a Thousand Categories

Attribute-based recognition models, due to their impressive performance and their ability to generalize well on novel categories, have been widely adopted for many computer vision applications. However, usually both the attribute vocabulary and the class-attribute associations have to be provided manually by domain experts or large number of annotators. This is very costly and not necessarily optimal regarding recognition performance, and most importantly, it limits the applicability of attribute-based models to large scale data sets. To tackle this problem, we propose an end-to-end unsupervised attribute learning approach. We utilize online text corpora to automatically discover a salient and discriminative vocabulary that correlates well with the human concept of semantic attributes. Moreover, we propose a deep convolutional model to optimize class-attribute associations with a linguistic prior that accounts for noise and missing data in text. In a thorough evaluation on ImageNet, we demonstrate that our model is able to efficiently discover and learn semantic attributes at a large scale. Furthermore, we demonstrate that our model outperforms the state-of-the-art in zero-shot learning on three data sets: ImageNet, Animals with Attributes and aPascal/aYahoo. Finally, we enable attribute-based learning on ImageNet and will share the attributes and associations for future research.Comment: Accepted as a conference paper at CVPR 201

Measuring Emotional Contagion in Social Media

Social media are used as main discussion channels by millions of individuals every day. The content individuals produce in daily social-media-based micro-communications, and the emotions therein expressed, may impact the emotional states of others. A recent experiment performed on Facebook hypothesized that emotions spread online, even in absence of non-verbal cues typical of in-person interactions, and that individuals are more likely to adopt positive or negative emotions if these are over-expressed in their social network. Experiments of this type, however, raise ethical concerns, as they require massive-scale content manipulation with unknown consequences for the individuals therein involved. Here, we study the dynamics of emotional contagion using Twitter. Rather than manipulating content, we devise a null model that discounts some confounding factors (including the effect of emotional contagion). We measure the emotional valence of content the users are exposed to before posting their own tweets. We determine that on average a negative post follows an over-exposure to 4.34% more negative content than baseline, while positive posts occur after an average over-exposure to 4.50% more positive contents. We highlight the presence of a linear relationship between the average emotional valence of the stimuli users are exposed to, and that of the responses they produce. We also identify two different classes of individuals: highly and scarcely susceptible to emotional contagion. Highly susceptible users are significantly less inclined to adopt negative emotions than the scarcely susceptible ones, but equally likely to adopt positive emotions. In general, the likelihood of adopting positive emotions is much greater than that of negative emotions.Comment: 10 pages, 5 figure