This study advances theories of impression formation by focusing on two factors that generate emotional responses: physical attractiveness and positive word use. Although considerable research on impression formation exists, most studies consider factors in isolation and neglect possible interactions. Our theory introduces competing mechanisms regarding possible interaction effects, and we empi...

This paper describes University of Leipzig’s approach to SemEval-2013 task 2B on Sentiment Analysis in Twitter: message polarity classification. Our system is designed to function as a baseline, to see what we can accomplish with well-understood and purely data-driven lexical features, simple generalizations as well as standard machine learning techniques: We use one-against-one Support Vector ...

Using a speeded word fragment completion task, we assessed age differences in the automatic accessibility of emotional versus neutral words from semantic memory. Participants were instructed to complete a series of single-solution word fragments as quickly as possible. The results demonstrate that older adults are biased against accessing both positive and negative words relative to neutral wor...

The research investigates the impact of customer hope, positive emotional advertising, and multi-context number (MCN) brand strategy on purchase intention word mouth in context an eco-efficient product. This study uses a quantitative survey energy-saving lamp. results indicated that consumer hope positively affects word-of-mouth; advertising numbers have effect intentions. However, has no inten...

The study investigates the impact of service recovery on word of mouth, customer trust and customer loyalty in public sector organizations of Pakistan (Pakistan Post, Pakistan Railways and Pakistan International Air Lines). This study employed a quantitative method approach to collect and analyse the data collected through a survey questionnaire. The study sample population comprised 300 custom...

This paper proposes to solve the bottleneck of finding training data for word sense disambiguation (WSD) in the domain of web queries, where a complete set of ambiguous word senses are unknown. In this paper, we present a combination of active learning and semi-supervised learning method to treat the case when positive examples, which have an expected word sense in web search result, are only g...

The present study begins to fill a gap in the recruitment literature by investigating whether the effects of negative publicity on organizational attractiveness can be mitigated by recruitment advertising and positive word-of-mouth. The accessibility–diagnosticity model was used as a theoretical framework to formulate predictions about the effects of these recruitment-related information source...

There are many examples in which a word changes its polarity from domain to domain. For example, unpredictable is positive in the movie domain, but negative in the product domain. Such words cannot be entered in a “universal sentiment lexicon” which is supposed to be a repository of words with polarity invariant across domains. Rather, we need to maintain separate domain specific sentiment lexi...

This paper describes the system developed by the Serendio team for the SemEval-2013 Task 2 competition (Task A). We use a lexicon based approach for discovering sentiments. Our lexicon is built from the Serendio taxonomy. The Serendio taxonomy consists of positive, negative, negation, stop words and phrases. A typical tweet contains word variations, emoticons, hashtags etc. We use preprocessing...

The present study begins to fill a gap in recruitment literature by investigating whether the effects of negative publicity on organizational attractiveness can be mitigated by recruitment advertising and positive word-of-mouth. The accessibility-diagnosticity model was used as a theoretical framework to formulate predictions about the effects of these recruitment-related information sources. A...