We introduce KnowBias, a system for detecting the degree of political bias in textual content such as social media posts and news articles. In the space of scalable text classification, a common problem is domain mismatch, where easily accessible training data (i.e., tweets) does not correspond in format to the desired testing domain (i.e., longer form article content). While universal text encoders such as word or sentence embeddings could be leveraged to train target agnostic classifiers, such schemes result in poor performance on long-form articles. Our key insight is that long-form articles are a mix of neutral and political sentences, while tweets are concentrated with opinion. We propose a two-step classification system that first automatically filters out neutral sentences from the input text document at evaluation time, and then the resulting text is input into a polarity classifier. We evaluate our two-step approach using a variety of test suites, including a set of tweets and long-form articles where annotations were crowd-sourced to decrease label noise, measuring accuracy and Spearman-rho rank correlation. In practice, KnowBias achieves a high accuracy of 86 (rho = 0.65) on these tweets and 75 (rho = 0.69) on long-form articles.
There is extensive literature @cite_21 in the social science literature on polarization that deals with mathematical models and supervised learning techniques to accurately identify polarity in written content. While the survey recognizes the need for scalability, the authors are pessimistic that automatic content analysis can fully replace humans. Moreover, the methods described in the survey hinge on the availability of reliable and clean annotated data, which we do not have access to in our problem.
@cite_21
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The science of opinion analysis based on data from social networks and other forms of mass media has garnered the interest of the scientific community and the business world. Dealing with the increasing amount of information present on the Web is a critical task and requires efficient models developed by the emerging field of sentiment analysis. To this end, current research proposes an efficient approach to support emotion recognition and polarity detection in natural language text. In this paper, we show how to exploit the most recent technological tools and advances in Statistical Learning Theory (SLT) in order to efficiently build an Extreme Learning Machine (ELM) and assess the resultant model's performance when applied to big social data analysis. ELM represents a powerful learning tool, developed to overcome some issues in back-propagation networks. The main problem with ELM is in training them to work in the event of a large number of available samples, where the generalization performance has to be carefully assessed. For this reason, we propose an ELM implementation that exploits the Spark distributed in memory technology and show how to take advantage of the most recent advances in SLT in order to address the issue of selecting ELM hyperparameters that give the best generalization performance. This paper proposes an unsupervised lexicon building method for the detection of polar clauses, which convey positive or negative aspects in a specific domain. The lexical entries to be acquired are called polar atoms, the minimum human-understandable syntactic structures that specify the polarity of clauses. As a clue to obtain candidate polar atoms, we use context coherency, the tendency for same polarities to appear successively in contexts. Using the overall density and precision of coherency in the corpus, the statistical estimation picks up appropriate polar atoms among candidates, without any manual tuning of the threshold values. The experimental results show that the precision of polarity assignment with the automatically acquired lexicon was 94 on average, and our method is robust for corpora in diverse domains and for the size of the initial lexicon. A method for sentiment polarity assignment for textual content written in Polish using supervised machine learning approach with transfer learning scheme is proposed in the paper. It has been shown that performing simple natural language processing steps prior to classification, provides inspiring results without redundant computation overhead. The documents containing subjective opinions were classified using N-gram and Bi-gram language model that is able to encode some of complex word phrases. The experiments carried out on two real datasets taken from different domains proved that learning on one dataset and testing on another, which is commonly called transfer learning, can be effective and may result in very high classification quality. Determining the polarity of a sentiment-bearing expression requires more than a simple bag-of-words approach. In particular, words or constituents within the expression can interact with each other to yield a particular overall polarity. In this paper, we view such subsentential interactions in light of compositional semantics, and present a novel learning-based approach that incorporates structural inference motivated by compositional semantics into the learning procedure. Our experiments show that (1) simple heuristics based on compositional semantics can perform better than learning-based methods that do not incorporate compositional semantics (accuracy of 89.7 vs. 89.1 ), but (2) a method that integrates compositional semantics into learning performs better than all other alternatives (90.7 ). We also find that "content-word negators", not widely employed in previous work, play an important role in determining expression-level polarity. Finally, in contrast to conventional wisdom, we find that expression-level classification accuracy uniformly decreases as additional, potentially disambiguating, context is considered.