Opinion Mining on Non-English Short Text

As the type and the number of such venues increase, automated analysis of sentiment on textual resources has become an essential data mining task. In this paper, we investigate the problem of mining opinions on the collection of informal short texts. Both positive and negative sentiment strength of texts are detected. We focus on a non-English language that has few resources for text mining. This approach would help enhance the sentiment analysis in languages where a list of opinionated words does not exist. We propose a new method projects the text into dense and low dimensional feature vectors according to the sentiment strength of the words. We detect the mixture of positive and negative sentiments on a multi-variant scale. Empirical evaluation of the proposed framework on Turkish tweets shows that our approach gets good results for opinion mining

Investigating the Effect of Emoji in Opinion Classification of Uzbek Movie Review Comments

Opinion mining on social media posts has become more and more popular. Users often express their opinion on a topic not only with words but they also use image symbols such as emoticons and emoji. In this paper, we investigate the effect of emoji-based features in opinion classification of Uzbek texts, and more specifically movie review comments from YouTube. Several classification algorithms are tested, and feature ranking is performed to evaluate the discriminative ability of the emoji-based features.Comment: 10 pages, 1 figure, 3 table

2004-2010

Neural tube defects (NTD) are among the most common congenital abnormalities, with an incidence of 3 per 1000 live births in Turkey. In a study of major congenital abnormalities in the city of Denizli, Turkey, abnormalities of the central nervous system are particularly common (31.1%). The objective of this study was to develop a registry of cases with NTDs in Denizli. Cases that had been diagnosed with NTD between January 2004 and September 2010 in State Hospitals of Central Denizli were retrospectively examined. The diagnoses were established based on the ICD-10 criteria. A total of 250 subjects with NTD were identified, including 123 (49.2%) females and 127 (50.8%) males with a mean age of 13.72 +/- 15.62 years (age range 1-81 years). Interestingly, spina bifida constituted a significant percentage of the cases (149 cases; 59.6%). In addition, 10 (4.0%) cases had hydrocephalus plus spina bifida. The second most common diagnosis was microcephaly, which included 70 cases (28.0%). Encephalocele was observed in only 2 cases (0.8%). Development of NTD is influenced by nutrition, socioeconomic factors, and the use of folic acid during the peri-conceptional period. Studies examining the effect of these factors on NTD in Turkey and a review of primary prevention measures are necessary

Nursing students' self-reported knowledge of genetics and genetic education.

INTRODUCTION: Nurses need to use genetic information in care; several areas of current need include primary care, public health, cardiovascular, geriatric and oncology nursing. Nursing faculties may be reluctant to add genetics to existing courses, let alone tackle the work of teaching an entire course in genetics. OBJECTIVES: The purpose of this study was to describe the current genetic knowledge of nursing students regarding genetics and genetic education. METHODS: This is a self-administered cross-sectional survey. A total of 162 of 209 nursing students returned a questionnaire. Data from the surveys reflect the students' self-reported knowledge regarding medical genetics (response rate = 77.5%). RESULTS: The majority of students noted 'some' to 'minimal' knowledge of Mendelian inheritance and human chromosome abnormalities. In terms of awareness of genetic abnormalities and disorders, students claimed to have 'high' knowledge of breast cancer, phenylketonuria, thalassemia, colon cancer, Rh factor, cleft lip/palate, hemophilia, color blindness, and polydactyly. Students stated that they would like to receive more education related to genetic diseases and genetic counseling (93.9%). CONCLUSIONS: The majority of students reported very little knowledge of genetics and genetic disorders. Most of students responded positively to all the education methods suggested in the questionnaire, adding that they would like more education about genetics. The results from this study may help to reform and upgrade the educational strategy concerning genetics in the Schools of Health

A Hybrid Approach for the Sentiment Analysis of Turkish Twitter Data

Social media is now playing an important role in influencing people’s sentiments. It also helps analyze how people, particularly consumers, feel about a particular topic, product or an idea. One of the recent social media platforms that people use to express their thoughts is Twitter. Due to the fact that Turkish is an agglutinative language, its complexity makes it difficult for people to perform sentiment analysis. In this study, a sum of 13K Turkish tweets has been collected from Twitter using the Twitter API and their sentiments are being analyzed using machine learning classifiers. Random forests and support vector machines are the two kinds of classifiers that are adopted. Preprocessing methods were applied on the obtained data to remove links, numbers, punctuations and un-meaningful characters. After the preprocessing phase, unsuitable data have been removed and 10,500 out of the 13K downloaded dataset are taken as the main dataset. The datasets are classified to be either positive, negative or neutral based on their contents. The main dataset was converted to a stemmed dataset by removing stopwords, applying tokenization and also applying stemming on the dataset, respectively. A portion of 3,000 and 10,500 of the stemmed data with equal distribution from each class has been identified as the first dataset and second dataset to be used in the testing phase. Experimental results have shown that while support vector machines perform better when it comes to classifying negative and neutral stemmed data, random forests algorithm perform better in classifying positive stemmed data and thus a hybrid approach which consists of the hierarchical combination of random forest and support vector machines has also been developed and used to find the result of the data. Finally, the applied methodologies have been tested on both the first and the second dataset. It has been observed that while both support vector machines and random forest algorithms could not achieve an accuracy of up to 77% on the first and 72% on the second dataset, the developed hybrid approach achieve an accuracy of up to 86.4% and 82.8% on the first and second dataset, respectively. © 2020, Springer Nature Switzerland AG

Investigating the Effect of Emoji in Opinion Classification of Uzbek Movie Review Comments

Opinion mining on social media posts has become more and more popular. Users often express their opinion on a topic not only with words but they also use image symbols such as emoticons and emoji. In this paper, we investigate the effect of emoji-based features in opinion classification of Uzbek texts, and more specifically movie review comments from YouTube. Several classification algorithms are tested, and feature ranking is performed to evaluate the discriminative ability of the emoji-based features

Analysis of cytokine immune response profile in response to inflammatory stimuli in mice with genetic defects in fetal and adult hemoglobin chain expression.

Injections of a crude fetal sheep liver extract (FSLE) containing fetal hemoglobin, MPLA, and glutathione (GSSH) reversed cytokine changes in aged mice. To investigate the role of fetal hemoglobin we derived mice with homzygous deletions for either of the two major βchains, Hgbβ <sub>ma</sub> KO or Hgbβ <sub>mi</sub> KO. Hgbβ <sub>mi</sub> is the most prominent fetal Hgbβ chain, with Hgbβ <sub>ma</sub> more prominent in adult mice. Mice lacking another fetal Hgb chain, HgbεKO, died in utero. CHO cells transfected with cloned Hgb chains were used to produce proteins for preparation of rabbit heteroantibodes. Splenocytes from Hgbβ <sub>ma</sub> KO mice stimulated in vitro with Conconavalin A showed a higher IL-2:IL-4 ratio than cells from Hgbβ <sub>mi</sub> KO mice. Following immunization in vivo with ovalbumin in alum, Hgbβ <sub>ma</sub> KO mice produced less IgE than Hgbβ <sub>mi</sub> KO mice, suggesting that in the absence of Hgbβ <sub>mi</sub> KO mice had a predeliction to heightened allergic-type responses. Using CHO cells transfected with cloned Hgb chains, we found that only the fetal Hgb chain, Hgbε, was secreted at high levels. Secretion of Hgbβ <sub>ma</sub> or Hgbβ <sub>mi</sub> chains was seen only after genetic mutation to introduce the two N-linked glycosylation sites present in Hgbε, but absent in the Hgbβ chains. We speculated that a previously unanticipated biological function of a naturally secreted fetal Hgb chain may be partly responsible for the effects reported following injection of animals with fetal, not adult, Hgb. Mice receiving injections of rabbit anti-Hgbε but not either anti-Hgbβ <sub>ma</sub> or anti-Hgbβ <sub>mi</sub> from day 14 gestation also showed a bias towards the higher IL-2:IL-4 ratios seen in Hgbβ <sub>mi</sub> KO mice