A Sesotho news headlines dataset for sentiment analysis.

Sentiment Analysis (SA) is a subset of Natural Language Processing (NLP) which has become a promising research area enabling the provision of language specific services. Although research in high resource languages such as English and Chinese has achieved promising results, research in low resource African languages such as Sesotho is still in its infancy due to limited text and speech datasets. This study contributes in this regard by availing the Sesotho News (SN) dataset, as an annotated dataset for the SA and Aspect Based Sentiment Analysis (ABSA) tasks. This dataset may be used for NLP research to benefit 1.85 million Sesotho speakers in Lesotho and 11.5 million speakers in South Africa. The dataset includes 4651 headlines for the ABSA task and 2401 headlines for the SA task using Lesotho's orthography of Sesotho. The news headlines were collected from Sesotho online newspapers and then annotated for the ABSA and SA tasks. The Spearman's correlation and Cohen's Kappa Index metrics show that there is good correlation between the annotators, implying that the SN dataset is of gold standard.

Deep Neural Networks for Automatic Flower Species Localization and Recognition.

Deep neural networks are efficient methods of recognizing image patterns and have been largely implemented in computer vision applications. Object detection has many applications in computer vision, including face and vehicle detection, video surveillance, and plant leaf detection. An automatic flower identification system over categories is still challenging due to similarities among classes and intraclass variation, so the deep learning model requires more precisely labeled and high-quality data. In this proposed work, an optimized and generalized deep convolutional neural network using Faster-Recurrent Convolutional Neural Network (Faster-RCNN) and Single Short Detector (SSD) is used for detecting, localizing, and classifying flower objects. We prepared 2000 images for various pretrained models, including ResNet 50, ResNet 101, and Inception V2, as well as Mobile Net V2. In this study, 70% of the images were used for training, 25% for validation, and 5% for testing. The experiment demonstrates that the proposed Faster-RCNN model using the transfer learning approach gives an optimum mAP score of 83.3% with 300 and 91.3% with 100 proposals on ten flower classes. In addition, the proposed model could identify, locate, and classify flowers and provide essential details that include flower name, class classification, and multilabeling techniques.