Biblio

In our era, most of the communication between people is realized in the form of electronic messages and especially through smart mobile devices. As such, the written text exchanged suffers from bad use of punctuation, misspelling words, continuous chunk of several words without spaces, tables, internet addresses etc. which make traditional text analytics methods difficult or impossible to be applied without serious effort to clean the dataset. Our proposed method in this paper can work in massive noisy and scrambled texts with minimal preprocessing by removing special characters and spaces in order to create a continuous string and detect all the repeated patterns very efficiently using the Longest Expected Repeated Pattern Reduced Suffix Array (LERP-RSA) data structure and a variant of All Repeated Patterns Detection (ARPaD) algorithm. Meta-analyses of the results can further assist a digital forensics investigator to detect important information to the chunk of text analyzed.

Social media has become an important platform for people to express opinions, share information and communicate with others. Detecting and tracking topics from social media can help people grasp essential information and facilitate many security-related applications. As social media texts are usually short, traditional topic evolution models built based on LDA or HDP often suffer from the data sparsity problem. Recently proposed topic evolution models are more suitable for short texts, but they need to manually specify topic number which is fixed during different time period. To address these issues, in this paper, we propose a nonparametric topic evolution model for social media short texts. We first propose the recurrent semantic dependent Chinese restaurant process (rsdCRP), which is a nonparametric process incorporating word embeddings to capture semantic similarity information. Then we combine rsdCRP with word co-occurrence modeling and build our short-text oriented topic evolution model sdTEM. We carry out experimental studies on Twitter dataset. The results demonstrate the effectiveness of our method to monitor social media topic evolution compared to the baseline methods.

Community question answering (cQA) has become an important issue due to the popularity of cQA archives on the Web. This paper focuses on addressing the lexical gap problem in question retrieval. Question retrieval in cQA archives aims to find the existing questions that are semantically equivalent or relevant to the queried questions. However, the lexical gap problem brings a new challenge for question retrieval in cQA. In this paper, we propose to model and learn distributed word representations with metadata of category information within cQA pages for question retrieval using two novel category powered models. One is a basic category powered model called MB-NET and the other one is an enhanced category powered model called ME-NET which can better learn the distributed word representations and alleviate the lexical gap problem. To deal with the variable size of word representation vectors, we employ the framework of fisher kernel to transform them into the fixed-length vectors. Experimental results on large-scale English and Chinese cQA data sets show that our proposed approaches can significantly outperform state-of-the-art retrieval models for question retrieval in cQA. Moreover, we further conduct our approaches on large-scale automatic evaluation experiments. The evaluation results show that promising and significant performance improvements can be achieved.

In today's computerized and information-based society, individuals are constantly presented with vast amounts of text data, ranging from news articles, scientific publications, product reviews, to a wide range of textual information from social media. To extract value from these large, multi-domain pools of text, it is of great importance to gain an understanding of entities and their relationships. In this tutorial, we introduce data-driven methods to recognize typed entities of interest in massive, domain-specific text corpora. These methods can automatically identify token spans as entity mentions in documents and label their fine-grained types (e.g., people, product and food) in a scalable way. Since these methods do not rely on annotated data, predefined typing schema or hand-crafted features, they can be quickly adapted to a new domain, genre and language. We demonstrate on real datasets including various genres (e.g., news articles, discussion forum posts, and tweets), domains (general vs. bio-medical domains) and languages (e.g., English, Chinese, Arabic, and even low-resource languages like Hausa and Yoruba) how these typed entities aid in knowledge discovery and management.

Malware detection increasingly relies on machine learning techniques, which utilize multiple features to separate the malware from the benign apps. The effectiveness of these techniques primarily depends on the manual feature engineering process, based on human knowledge and intuition. However, given the adversaries' efforts to evade detection and the growing volume of publications on malware behaviors, the feature engineering process likely draws from a fraction of the relevant knowledge. We propose an end-to-end approach for automatic feature engineering. We describe techniques for mining documents written in natural language (e.g. scientific papers) and for representing and querying the knowledge about malware in a way that mirrors the human feature engineering process. Specifically, we first identify abstract behaviors that are associated with malware, and then we map these behaviors to concrete features that can be tested experimentally. We implement these ideas in a system called FeatureSmith, which generates a feature set for detecting Android malware. We train a classifier using these features on a large data set of benign and malicious apps. This classifier achieves a 92.5% true positive rate with only 1% false positives, which is comparable to the performance of a state-of-the-art Android malware detector that relies on manually engineered features. In addition, FeatureSmith is able to suggest informative features that are absent from the manually engineered set and to link the features generated to abstract concepts that describe malware behaviors.

Text mining has developed and emerged as an essential tool for revealing the hidden value in the data. Text mining is an emerging technique for companies around the world and suitable for large enduring analyses and discrete investigations. Since there is a need to track disrupting technologies, explore internal knowledge bases or review enormous data sets. Most of the information produced due to conversation transcripts is an unstructured format. These data have ambiguity, redundancy, duplications, typological errors and many more. The processing and analysis of these unstructured data are difficult task. But, there are several techniques in text mining are available to extract keywords from these unstructured conversation transcripts. Keyword Extraction is the process of examining the most significant word in the context which helps to take decisions in a much faster manner. The main objective of the proposed work is extracting the keywords from meeting transcripts by using the Swarm Intelligence (SI) techniques. Here Stochastic Diffusion Search (SDS) algorithm is used for keyword extraction and Firefly algorithm used for clustering. These techniques will be implemented for an extensive range of optimization problems and produced better results when compared with existing technique.

Tremendous amounts of data are generated daily. Accordingly, unstructured text data that is distributed through news, blogs, and social media has gained much attention from many researchers as this data contains abundant information about various consumers' opinions. However, as the usefulness of text data is increasing, attempts to gain profits by distorting text data maliciously or non-maliciously are also increasing. In this sense, various types of spam detection techniques have been studied to prevent the side effects of spamming. The most representative studies include e-mail spam detection, web spam detection, and opinion spam detection. "Spam" is recognized on the basis of three characteristics and actions: (1) if a certain user is recognized as a spammer, then all content created by that user should be recognized as spam; (2) if certain content is exposed to other users (regardless of the users' intention), then content is recognized as spam; and (3) any content that contains malicious or non-malicious false information is recognized as spam. Many studies have been performed to solve type (1) and type (2) spamming by analyzing various metadata, such as user networks and spam words. In the case of type (3), however, relatively few studies have been conducted because it is difficult to determine the veracity of a certain word or information. In this study, we regard a hashtag that is irrelevant to the content of a blog post as spam and devise a methodology to detect such spam hashtags.

Decision makers need capabilities to quickly model and effectively assess consequences of actions and reactions in crisis de-escalation environments. The creation and what-if exercising of such models has traditionally had onerous resource requirements. This research demonstrates fast and viable ways to build such models in operational environments. Through social network extraction from texts, network analytics to identify key actors, and then simulation to assess alternative interventions, advisors can support practicing and execution of crisis de-escalation activities. We describe how we used this approach as part of a scenario-driven modeling effort. We demonstrate the strength of moving from data to models and the advantages of data-driven simulation, which allow for iterative refinement. We conclude with a discussion of the limitations of this approach and anticipated future work.

Interactive visualization provides valuable support for exploring, analyzing, and understanding textual documents. Certain tasks, however, require that insights derived from visual abstractions are verified by a human expert perusing the source text. So far, this problem is typically solved by offering overview-detail techniques, which present different views with different levels of abstractions. This often leads to problems with visual continuity. Focus-context techniques, on the other hand, succeed in accentuating interesting subsections of large text documents but are normally not suited for integrating visual abstractions. With VarifocalReader we present a technique that helps to solve some of these approaches' problems by combining characteristics from both. In particular, our method simplifies working with large and potentially complex text documents by simultaneously offering abstract representations of varying detail, based on the inherent structure of the document, and access to the text itself. In addition, VarifocalReader supports intra-document exploration through advanced navigation concepts and facilitates visual analysis tasks. The approach enables users to apply machine learning techniques and search mechanisms as well as to assess and adapt these techniques. This helps to extract entities, concepts and other artifacts from texts. In combination with the automatic generation of intermediate text levels through topic segmentation for thematic orientation, users can test hypotheses or develop interesting new research questions. To illustrate the advantages of our approach, we provide usage examples from literature studies.

An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets semantic patterns in consumers' discussions on social media. Specifically, the purposes of this study are twofold: 1) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities, two forms of positive vs. negative opinions and 2) driving actual content from the textual data that has a semantic trend. The considered tweets include consumers' opinions on US retail companies (e.g., Amazon, Walmart). Cosine similarity and K-means clustering methods are used to achieve the former goal, and Latent Dirichlet Allocation (LDA), a popular topic modeling algorithm, is used for the latter purpose. This is the first study which discover semantic properties of textual data in consumption context beyond sentiment analysis. In addition to major findings, we apply LDA (Latent Dirichlet Allocations) to the same data and drew latent topics that represent consumers' positive opinions and negative opinions on social media.