Mapping the mental space of emotional concepts through kinematic measures of decision uncertainty

Emotional concepts and their mental representations have been extensively studied. Yet, some ecologically relevant aspects, such as how emotional concepts are processed in ambiguous contexts, are incompletely known. We employed a similarity judgment of emotional concepts and manipulated the contextual congruency along the two main affective dimensions of hedonic valence and physiological activation, respectively. Behavioral and kinematics (mouse-tracking) measures were combined to gather a novel ‘similarity index’ between concepts, to derive topographical maps of their mental representations. Self-report (interoceptive sensibility, positive-negative affectivity, depression) and physiological measures (heart rate variability) have been tested to shape the emotional maps. Results indicate that low arousal concepts profit by contextual congruency, with faster responses and reduced uncertainty when contextual ambiguity decreases. The emotional maps exhibit two almost orthogonal axes of valence and arousal, and the similarity measure captures the smooth boundaries between emotions. The emotional map of individuals with low positive affectivity reveals a narrower conceptual distribution, with variations on positive emotions and on emotions with reduced arousal (as individuals with reduced heart rate variability). In sum, our work introduces a novel methodology for delve conceptual representations, bringing the dynamics of decision-making processes and choice uncertainty into the affective domain.

Antecedents of similarity perception for store-branded lookalikes

PurposeThis paper aims to identify the key antecedences contributing consumer similarity perception toward store branded lookalikes (SBLs), testing to what extent each of the antecedences influences the overall similarity perception.Design/methodology/approachTwo empirical studies were conducted. Study 1 was an online experiment to test the relative importance of packaging features on similarity judgment of SBLs. Study 2 examined the impacts of consumer characteristics and store related elements on similarity perception through a Qualtrics web-based questionnaire covering seven product categories.FindingsThis research yields two key findings. First of all, it revealed that all three packaging attributes studied (i.e. size and shape, image and color) exert positive influence on similarity judgment of SBLs, among which color shows the most significant importance, followed by size and shape, and then color. Then, it showed that brand loyalty fully mediated the effect of brand familiarity on consumers' similarity perception, giving that no direct effect was found from brand familiarity to similarity perception but consumers' brand loyalty increased as they become more familiar with the NBs. As consumers become more loyal to the brands, they would perceive the SBLs to be less similar to imitated national brands.Practical implicationsThis research confirmed the significance of proper manipulation of packaging design, either to the SBLs or to the imitated NBs. It also reveals the critical role of gaining high familiarity and strong loyalty for the NB manufacturers. For retailers, the research highlights the need to maintain a better store image in order to take advantage of SBLs to help with marketing competition.Originality/valueThis research contributes new knowledge on the lookalike phenomenon by uncovering the prerequisites that cause similarity perception between two products.

Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm

Nowadays, with the constant change of public aesthetic standards, a large number of new types and themes of film programs have emerged. For this reason, this paper proposes an improved neural network optimized by mutation ant colony algorithm for automatic acquisition of film labels, which not only overcomes the disadvantages of traditional neural network, such as difficulty in determining weights, slow convergence speed, and easiness to fall into local minimum, but also makes up for the shortcomings faced by using ant colony algorithm alone through the gradient information of quantum genetic algorithm neural network. The results show that the user similarity judgment is added in the process of calculating the user rating deviation between movies, and the neighbor chooses to add the movie tag weight and rating similarity as the basis for the neighbor selection of the target movie in the process of predicting the target movie rating. Experiments show the effectiveness of the algorithm.

Internet Tourism Resource Retrieval Using PageRank Search Ranking Algorithm

At present, there is a wide variety of tourism resources on the Internet. Tourism management departments must monitor these resources. At the same time, tourists must also retrieve personalized information that they are interested in. This requires a lot of time and energy. This essay studies and implements the tourism network resource monitoring system. The main work completed in the thesis proposes and constructs a topic collection algorithm and establishes a starting point, topic keywords, and a prediction mechanism. The algorithm includes three stages: the first climbing stage, the learning stage, and the continuous climbing stage. Open category directory search is used for similarity judgment and result evaluation. The experimental results show that with the continuous execution of the crawling process, the collection speed of related pages is getting faster and faster. We propose an algorithm for the extraction of wood based on the density of Internet tourism resources. The algorithm calculates the ratio of Internet tourism resource labels by row and uses a threshold extraction algorithm to distinguish area from private non-Internet tourism resource area. Experimental results show that the algorithm can successfully extract the main content of the article from a wide variety of web pages. This thesis takes the monitoring of tourism network resources as the research object and establishes a tourism network resource monitoring system, which can provide users with customizable, all-round, and real-time tourism network resource collection, extraction, and retrieval services so as to monitor tourism resources. The research results of this article can promote the construction of tourism informatization and can help users grasp the latest tourism information, thereby bringing great convenience to tourism. The system only downloads travel-related information through the use of topic collection technology, reducing the interference of irrelevant redundant web pages.

Calculating semantic relatedness of lists of nouns using WordNet path length

Lists of semantically related words are better recalled on immediate memory tests than otherwise equivalent lists of unrelated words. However, measuring the degree of relatedness is not straightforward. We report three experiments that assess the ability of various measures of semantic relatedness—including latent semantic analysis (LSA), GloVe, fastText, and a number of measures based on WordNet—to predict whether two lists of words will be differentially recalled. In Experiment 1, all measures except LSA correctly predicted the observed better recall of the related than the unrelated list. In Experiment 2, all measures except JCN predicted that abstract words would be recalled equally as well as concrete words because of their enhanced semantic relatedness. In Experiment 3, LSA, GLoVe, and fastText predicted an enhanced concreteness effect because the concrete words were more related; three WordNet measures predicted a small concreteness effect because the abstract and concrete words did not differ in semantic relatedness; and three other WordNet measures predicted no concreteness effect because the abstract words were more related than the concrete words. A small concreteness effect was observed. Over the three experiments, only two measures, both based on simple WordNet path length, predicted all three results. We suggest that the results are not unexpected because semantic processing in episodic memory experiments differs from that in reading, similarity judgment, and analogy tasks which are the most common way of assessing such measures.

Exploiting Syntactic and Semantic Information for Textual Similarity Estimation

The textual similarity task, which measures the similarity between two text pieces, has recently received much attention in the natural language processing (NLP) domain. However, due to the vagueness and diversity of language expression, only considering semantic or syntactic features, respectively, may cause the loss of critical textual knowledge. This paper proposes a new type of structure tree for sentence representation, which exploits both syntactic (structural) and semantic information known as the weight vector dependency tree (WVD-tree). WVD-tree comprises structure trees with syntactic information along with word vectors representing semantic information of the sentences. Further, Gaussian attention weight is proposed for better capturing important semantic features of sentences. Meanwhile, we design an enhanced tree kernel to calculate the common parts between two structures for similarity judgment. Finally, WVD-tree is tested on widely used semantic textual similarity tasks. The experimental results prove that WVD-tree can effectively improve the accuracy of sentence similarity judgments.