Semantic patterns for sentiment analysis of Twitter

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure

Morphological phenotypic dispersion of garlic cultivars by cluster analysis and multidimensional scaling

Multivariate techniques have become a useful tool for studying the phenotypic diversity of Germplasm Bank accessions, since they make it possible to combine a variety of different information from these accessions. This study aimed to characterize the phenotypic dispersion of garlic (Allium sativum L.) using two multivariate techniques with different objective functions. Twenty accessions were morphologically characterized for bulb diameter, length, and weight; number of cloves per bulb; number of leaves per plant; and leaf area. Techniques based on generalized quadratic distance of Mahalanobis, UPGMA (Unweighted Pair Group Method with Arithmetic Mean) clustering, and nMDS (nonmetrric MultiDimensional Scaling) were applied and the relative importance of variables quantified. The two multivariate techniques were capable of identifying cultivars with different characteristics, mainly regarding their classification in subgroups of common garlic or noble garlic, according to the number of cloves per bulb. The representation of the phenotypic distance of cultivars by multidimensional scaling was slightly more effective than that with UPGMA clustering

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

A variable-selection heuristic for K-means clustering

cluster analysis, K-means partitioning, variable selection, heuristics,