A survey on opinion summarization technique s for social media

The volume of data on the social media is huge and even keeps increasing. The need for efficient processing of this extensive information resulted in increasing research interest in knowledge engineering tasks such as Opinion Summarization. This survey shows the current opinion summarization challenges for social media, then the necessary pre-summarization steps like preprocessing, features extraction, noise elimination, and handling of synonym features. Next, it covers the various approaches used in opinion summarization like Visualization, Abstractive, Aspect based, Query-focused, Real Time, Update Summarization, and highlight other Opinion Summarization approaches such as Contrastive, Concept-based, Community Detection, Domain Specific, Bilingual, Social Bookmarking, and Social Media Sampling. It covers the different datasets used in opinion summarization and future work suggested in each technique. Finally, it provides different ways for evaluating opinion summarization

Semantic Detection of Targeted Attacks Using DOC2VEC Embedding

The targeted attack is one of the social engineering attacks. The detection of this type of attack is considered a challenge as it depends on semantic extraction of the intent of the attacker. However, previous research has primarily relies on the Natural Language Processing or Word Embedding techniques that lack the context of the attacker\u27s text message. Based on Sentence Embedding and machine learning approaches, this paper introduces a model for semantic detection of targeted attacks. This model has the advantage of encoding relevant information, which helps to improve the performance of the multi-class classification process. Messages will be categorized based on the type of security rule that the attacker has violated. The suggested model was tested using a dialogue dataset taken from phone calls, which was manually categorized into four categories. The text is pre-processed using natural language processing techniques, and the semantic features are extracted as Sentence Embedding vectors that are augmented with security policy sentences. Machine Learning algorithms are applied to classify text messages. The experimental results show that sentence embeddings with doc2vec achieved high prediction accuracy 96.8%. So, it outperformed the method applied to the same dialog dataset

Effect of human activities on biodiversity in Nabq Protected Area, South Sinai, Egypt

Nabq as “A Managed Resource Protected Area” or as “Multiple Use Management Area” is subjected to human activities that influence its biodiversity. Therefore, many field trips from January 2016 to January 2017 were carried out to detect the main activities in Nabq protectorate and their impacts on faunal macro-benthos biodiversity. Four stations representing two major habitats (mangrove and rocky shore) were chosen depending on anthropogenic activities. A total of 112 macro-benthos taxa were recorded in Nabq dominated with Planaxis salcatus, Nerita spp., Barbatia trapezina and Ophiocoma scolopendrina. The anthropogenic activities don't only affect the presence and absence of species, but also influence on the dominance status of species in the investigated stations. El-Rowayseaa and El-Dagal were unaffected stations showing more abundance and diversification than El-Gharkana and El-Monkateaa which were affected mangrove and rocky shore stations, respectively. In conclusion, anthropogenic activities are the main cause of recent changes to the marine biodiversity in Nabq Protected Area and their continuation may cause habitat damage. Keywords: Macro-benthos, Biodiversity, Human activities, Ecotourism, Fishing, Protected area