Social media platforms and online streaming services have spawned a new breed of Hate Speech (HS). Due to the massive amount of user-generated content on these sites, modern machine learning techniques are found to be feasible and cost-effective to tackle this problem. However, linguistically diverse datasets covering different social contexts in which offensive language is typically used are required to train generalizable models. In this paper, we identify the shortcomings of existing Bangla HS datasets and introduce a large manually labeled dataset BD-SHS that includes HS in different social contexts. The labeling criteria were prepared following a hierarchical annotation process, which is the first of its kind in Bangla HS to the best of our knowledge. The dataset includes more than 50,200 offensive comments crawled from online social networking sites and is at least 60% larger than any existing Bangla HS datasets. We present the benchmark result of our dataset by training different NLP models resulting in the best one achieving an F1-score of 91.0%. In our experiments, we found that a word embedding trained exclusively using 1.47 million comments from social media and streaming sites consistently resulted in better modeling of HS detection in comparison to other pre-trained embeddings. Our dataset and all accompanying codes is publicly available at github.com/naurosromim/hate-speech-dataset-for-Bengali-social-media

In this paper, we propose an annotated sentiment analysis dataset made of informally written Bangla texts. This dataset comprises public comments on news and videos collected from social media covering 13 different domains, including politics, education, and agriculture. These comments are labeled with one of the polarity labels, namely positive, negative, and neutral. One significant characteristic of the dataset is that each of the comments is noisy in terms of the mix of dialects and grammatical incorrectness. Our experiments to develop a benchmark classification system show that hand-crafted lexical features provide superior performance than neural network and pretrained language models. We have made the dataset and accompanying models presented in this paper publicly available at https://git.io/JuuNB.

Many new books get published every year, and only a fraction of them become popular among the readers. So the prediction of a book success can be a very useful parameter for publishers to make a reliable decision. This article presents the study of semantic word associations using the word embedding of book content for a set of Roget’s thesaurus concepts for book success prediction. In this work, we discuss the method to represent a book as a spectrum of concepts based on the association score between its content embedding and a global embedding (i.e. fastText) for a set of semantically linked word clusters. We show that the semantic word associations outperform the previous methods for book success prediction. In addition, we present that semantic word associations also provide better results than using features like the frequency of word groups in Roget’s thesaurus, LIWC (a popular tool for linguistic inquiry and word count), NRC (word association emotion lexicon), and part of speech (PoS). Our study reports that concept associations based on Roget’s Thesaurus using word embedding of individual novel resulted in the state-of-the-art performance of 0.89 average weighted F1-score for book success prediction. Finally, we present a set of dominant themes that contribute towards the popularity of a book for a specific genre.

In this paper, we attempt to improve upon the state-of-the-art in predicting a novel’s success by modeling the lexical semantic relationships of its contents. We created the largest dataset used in such a project containing lexical data from 17,962 books from Project Gutenberg. We utilized domain specific feature reduction techniques to implement the most accurate models to date for predicting book success, with our best model achieving an average accuracy of 94.0%. By analyzing the model parameters, we extracted the successful semantic relationships from books of 12 different genres. We finally mapped those semantic relations to a set of themes, as defined in Roget’s Thesaurus and discovered the themes that successful books of a given genre prioritize. At the end of the paper, we further showed that our model demonstrate similar performance for book success prediction even when Goodreads rating was used instead of download count to measure success.