For subjective NLP problems, such as classification of hate speech, aggression, or emotions, personalized solutions can be exploited. Then, the learned models infer about the perception of the content independently for each reader. To acquire training data, texts are commonly randomly assigned to users for annotation, which is expensive and highly inefficient. Therefore, for the first time, we suggest applying an active learning paradigm in a personalized context to better learn individual preferences. It aims to alleviate the labeling effort by selecting more relevant training samples. In this paper, we present novel Personalized Active Learning techniques for Subjective NLP tasks (PALS) to either reduce the cost of the annotation process or to boost the learning effect. Our five new measures allow us to determine the relevance of a text in the context of learning users personal preferences. We validated them on three datasets: Wiki discussion texts individually labeled with aggression and toxicity, and on Unhealthy Conversations dataset. Our PALS techniques outperform random selection even by more than 30%. They can also be used to reduce the number of necessary annotations while maintaining a given quality level. Personalized annotation assignments based on our controversy measure decrease the amount of data needed to just 25%-40% of the initial size.

A unified gold standard commonly exploited in natural language processing (NLP) tasks requires high inter-annotator agreement. However, there are many subjective problems that should respect users individual points of view. Therefore in this paper, we evaluate three different personalized methods on the task of hate speech detection. The user-centered techniques are compared to the generalizing baseline approach. We conduct our experiments on three datasets including single-task and multi-task hate speech detection. For validation purposes, we introduce a new data-split strategy, preventing data leakage between training and testing. In order to better understand the model behavior for individual users, we carried out personalized ablation studies. Our experiments revealed that all models leveraging user preferences in any case provide significantly better results than most frequently used generalized approaches. This supports our overall observation that personalized models should always be considered in all subjective NLP tasks, including hate speech detection.

Analysis of emotions elicited by opinions, comments, or articles commonly exploits annotated corpora, in which the labels assigned to documents average the views of all annotators, or represent a majority decision. The models trained on such data are effective at identifying the general views of the population. However, their usefulness for predicting the emotions evoked by the textual content in a particular individual is limited. In this paper, we present a study performed on a dataset containing 7,000 opinions, each annotated by about 50 people with two dimensions: valence, arousal, and with intensity of eight emotions from Plutchik’s model. Our study showed that individual responses often significantly differed from the mean. Therefore, we proposed a novel measure to estimate this effect – Personal Emotional Bias (PEB). We also developed a new BERT-based transformer architecture to predict emotions from an individual human perspective. We found PEB a major factor for improving the quality of personalized reasoning. Both the method and measure may boost the quality of content recommendation systems and personalized solutions that protect users from hate speech or unwanted content, which are highly subjective in nature.

In this article, we present a novel multi-domain dataset of Polish text reviews, annotated with sentiment on different levels: sentences and the whole documents. The annotation was made by linguists in a 2+1 scheme (with inter-annotator agreement analysis). We present a preliminary approach to the classification of labelled data using logistic regression, bidirectional long short-term memory recurrent neural networks (BiLSTM) and bidirectional encoder representations from transformers (BERT).

In this article we present an extended version of PolEmo – a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).