Quantifying Political Bias in News Articles

Search bias analysis is getting more attention in recent years since search results could affect In this work, we aim to establish an automated model for evaluating ideological bias in online news articles. The dataset is composed of news articles in search results as well as the newspaper articles. The current automated model results show that model capability is not sufficient to be exploited for annotating the documents automatically, thereby computing bias in search results

Preliminary Bias Results in Search Engines

This report aims to report my thesis progress so far. My work attempts to show the differences in the perspectives of two search engines, Bing and Google on several selected controversial topics. In this work, we try to make a distinction on the viewpoints of Bing \& Google by using sentiment as well as the ranking of the document returned from these two search engines on the same queries, these queries are related mainly to controversial topics. You can find the methods we used with experimental results below.Comment: arXiv admin note: text overlap with arXiv:2112.1280

Sentence-based sentiment analysis with domain adaptation capability

Sentiment analysis aims to automatically estimate the sentiment in a given text as positive, objective or negative, possibly together with the strength of the sentiment. Polarity lexicons that indicate how positive or negative each term is, are often used as the basis of many sentiment analysis approaches. Domain-specific polarity lexicons are expensive and time-consuming to build; hence, researchers often use a general purpose or domainindependent lexicon as the basis of their analysis. In this work, we address two sub-tasks in sentiment analysis. We introduce a simple method to adapt a general purpose polarity lexicon to a specific domain. Subsequently, we propose new features to be used in a term polarity based approach to sentiment analysis. We consider different aspects of sentences, such as length, purity, irrealis content, subjectivity, and position within the opinionated text. This analysis is used to find sentences that may convey better information about the overall review polarity. Therefore, our work is also focused on the sentence-based sentiment analysis differently from the other works. Moreover, we worked on two distinct domains, hotel and Twitter with three different systems which are compared with the existing state-of-the-art approaches in the literature

New features for sentiment analysis: Do sentences matter?

1st International Workshop on Sentiment Discovery from Affective Data 2012, SDAD 2012 - In Conjunction with ECML-PKDD 2012; Bristol; United Kingdom; 28 September 2012 through 28 September 2012In this work, we propose and evaluate new features to be used in a word polarity based approach to sentiment classification. In particular, we analyze sentences as the first step before estimating the overall review polarity. We consider different aspects of sentences, such as length, purity, irrealis content, subjectivity, and position within the opinionated text. This analysis is then used to find sentences that may convey better information about the overall review polarity. The TripAdvisor dataset is used to evaluate the effect of sentence level features on polarity classification. Our initial results indicate a small improvement in classification accuracy when using the newly proposed features. However, the benefit of these features is not limited to improving sentiment classification accuracy since sentence level features can be used for other important tasks such as review summarization.European Commission, FP7, under UBIPOL (Ubiquitous Participation Platform for Policy Making) Projec

Sentiment analysis in Turkish

In this chapter, we give an overview of sentiment analysis problem and present a system to estimate the sentiment of movie reviews in Turkish. Our approach combines supervised learning and lexicon-based approaches, making use of a recently constructed Turkish polarity lexicon called SentiTurkNet. For performance evaluation, we investigate the contribution of different feature sets, as well as the effect of lexicon size on the overall classification performance

Predicting worker disagreement for more effective crowd labeling

Crowdsourcing is a popular mechanism used for labeling tasks to produce large corpora for training. However, producing a reliable crowd labeled training corpus is challenging and resource consuming. Research on crowdsourcing has shown that label quality is much affected by worker engagement and expertise. In this study, we postulate that label quality can also be affected by inherent ambiguity of the documents to be labeled. Such ambiguities are not known in advance, of course, but, once encountered by the workers, they lead to disagreement in the labeling – a disagreement that cannot be resolved by employing more workers. To deal with this problem, we propose a crowd labeling framework: we train a disagreement predictor on a small seed of documents, and then use this predictor to decide which documents of the complete corpus should be labeled and which should be checked for document-inherent ambiguities before assigning (and potentially wasting) worker effort on them. We report on the findings of the experiments we conducted on crowdsourcing a Twitter corpus for sentiment classification

SU-Sentilab : a classification system for sentiment analysis in twitter

Sentiment analysis refers to automatically extracting the sentiment present in a given natural language text. We present our participation to the SemEval2013 competition, in the sentiment analysis of Twitter and SMS messages. Our approach for this task is the combination of two sentiment analysis subsystems which are combined together to build the final system. Both subsystems use supervised learning using features based on various polarity lexicon

Some new aphid records for the Turkish aphidofauna (Hemiptera: Aphidoidae)

[No Asbtact Available

Determination of the Morphological variations of the Schizolachnus Species Distributed in the Inner Western Anatolia Subregion

Bu tez çalışması, TUBİTAK tarafından 111T866 no'lu proje kapsamında desteklenmiştir.Bu çalışmada Pinus türleri üzerinden Afyonkarahisar, Kütahya ve Uşak illerinden örneklenen Schizolachnus Mordvilko,1909'a ait popülâsyonlar taksonomik açıdan ve lokalitenin morfolojik karakterler üzerine yaptığı etkiyle ortaya çıkabilecek morfometrik varyasyonlar açısından incelenmiştir. Çalışma alanından Schizolachnus orientalis (Takahashi, 1924), Schizolachnus obscurus Börner, 1940 ve Schizolachnus pineti (Fabricius, 1781) belirlenmiş ve bu türlerden S. orientalis'in Türkiye afit faunası için yeni kayıt olduğu belirlenmiştir. Belirlenen Schizolachnus türlerine ait 8 morfolojik karakter ve 3 adet morfolojik karakter oranları morfometrik varyasyonları ortaya koymak için ölçülmüştür. Morfolojik karakterlerin değerlendirilmesi sonucunda lokalitenin genel olarak Schizolachnus'a ve tek tek türler alındığında türler üzerinde önemli ölçüde etki yaptığı ve istatistiksel açıdan önemli morfometrik varyasyonlara yol açtığı gösterilmiştir. Morfolojik karakterlerden özellikle URS IV, URS V uzunlukları ve URS IV/URS V oranı üzerinde belirlenen anlamlı farklılıklar önemlidir, çünkü bu karakterler Schizolachnus türlerinin ayrımında en önemli ayırtedici karakterlerdendir. Schizolachnus türlerinde lokaliteye bağlı morfometrik varyasyonların belirlenmesi amacıyla düzenlenen bu ilk çalışma sonucunda önemli verilere ulaşılmıştır.In this study, Schizolachnus Mordvilko, 1909 populations, which were collected from Afyonkarahisar, Kütahya and Uşak provinces that feed on Pinus species, was investigated as a side of the taxonomic and morphometric variations with the effectiveness of localities on morphological features. From field area Schizolachnus orientalis (Takahashi, 1924), Schizolachnus obscurus Börner, 1940 and Schizolachnus pineti (Fabricius, 1781) were described and among them S.orientalis were deterermined as new entry for Turkey aphid fauna. Eight morphological features and three morphometric ratios of Schizolachnus members used to find out morphometric variations. As a result of evaluations of morphometric analyses, localities has significant effect not only on Schizolachnus genera but also on species level and lead to important morphometrical variations. Particularly statistically significant differentiations are important that were observed on URS IV, URS V length and URS IV/URS V ratio, as these features are important diagnostic characters for Schizolachnus. This is the first study conducted related with the determination of the morphometric variations in Schizolachnus species induced by locality and scientifically important findings derive

Bias in search: Evaluating search results through rank and relevance based measures

Search is ubiquitous. People continue to seek information through popular search engines, Bing and Google as well as online search platforms, YouTube. Nonetheless, they tend to think that these platforms are objective by only displaying information without injecting any bias. Since users are more susceptible to bias when they are unaware of it, it is important to evaluate the retrieved search results of the aforementioned platforms with respect to bias. This thesis analyses two main things as search engine bias towards controversial issues and gender bias in the context of online education. For evaluating specifically search engine bias, three novel rank and relevance-based measures have been proposed and search results of two widely-used search engines Google and Bing have been analysed through web documents’ content with respect to stance (in support or against), and ideological bias (conservative or liberal). Then, the impact of geolocation on the bias has been investigated. Lastly, in the scope of search engine bias, the source of bias has been tracked, to check whether the bias (if exists) comes from the input data, or the ranking algorithm. For assessing gender bias in online education, two new rank and relevance based measures that are more suitable in the scope of gender bias have been proposed. Further, video search results returned by YouTube towards the queries in STEM and NON-STEM fields have been analysed using narrators’ information. Lastly, the source of gender bias has been investigated by proposing the specifically-curated gender bias measure