Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

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Sentiment Analysis and Opinion Mining

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Semi-Supervised Learning Literature Survey

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction. Adaptive Computation and Machine Learning series

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Semi-Supervised Learning

: This report documents the intersection of computational social network analysis and sociolinguistic research aimed at discovering how social intent is communicated through online bilingual speech acts in African cultures. Researchers from the US Army Research Lab (ARL) and Howard University (HU) exchanged information, data, and analyses to examine the feasibility of using automated text analytics software to provide contextual understanding within a text corpus. This effort extends the Army Research Office Partners in Research Transition program titled Extracting Social Meaning from Linguistic Structures Involving Code-Switching in English (and French) with Selected African Languages led by HU. It also provided test and evaluation opportunities for ARL prototype software designed to extract relational networks and sentiment from unstructured Tweets. This collaboration was driven by the realization that more social input is needed to refine context for sociolinguistic analysis and also by the increasing importance of modeling social issues for military decision making. To address social communication acts, we focus on using Twitter for sharing individual and collective opinions. Social media services in general have gained popularity in recent years and are frequently used for discovery and analysis of social intent. We examine the sociolinguistic features that can be used to discover social intent, discuss how social network analysis can be used to inform contextual nuances in which that intent is communicated, and describe how automated tools can be used to support sociolinguistic analysis. We conclude with future research directions that can extend the rich connections between computational social network analysis and the study of sociolinguistics.

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Exploring Social Meaning in Online Bilingual Text through Social Network Analysis

The RNA-recognition motif (RRM) is the most abundant RNA-binding domain involved in many post-transcriptional processes. Since RRM-containing proteins have different functions with similar domain architecture, it is challenging to implement an automated annotation tool for these proteins in proteomic analysis. In this study, we implemented a proteomic analyzing pipeline to identify proteins with multiple RRMs and predict their domain boundaries using specific PSSMs, domain architectures, and proteins with the same entity name. After clustering sequences on the basis of their evolutionary distances, a reference group is selected comparing domain architectures. Then, candidate proteins are collected in a proteome using specific PSSMs from seed alignments in PFAM. Finally, target proteins are identified using multiple alignments and phyolgenetic trees between candidate and reference proteins. Therefore, we identified 33 proteins close to 12 types of RRM containing proteins and their domain boundaries among 508 candidates from 33610 sequences in a human proteome.

The development of a proteomic analyzing pipeline to identify proteins with multiple RRMs and predict their domain boundaries

Nowadays a lot of data is collected in online forums. One of the key tasks is to determine the social structure of these online groups, for example the identification of subgroups within a larger group. We will approach the grouping of individual as a classification problem. The classifier will be based on fuzzy logic. The input to the classifier will be linguistic features and degree of relationships (among individuals). The output of the classifiers are the groupings of individuals. We also incorporate a method that ranks the members of the detected subgroup to identify the hierarchies in each subgroup. Data from the HBO television show The Wire is used to analyze the efficacy and usefulness of fuzzy logic based methods as alternative methods to classical statistical methods usually used for these problems. The proposed methodology could detect automatically the most influential members of each organization The Wire with 90% accuracy.

Automatic Detection of Small Groups of Persons, Influential Members, Relations and Hierarchy in Written Conversations Using Fuzzy Logic.

Accurate Identification of Mass Peaks for Tandem Mass Spectra Using MCMC Model

Tandem mass spectrometry is a popular tool for the identification of peptide sequences. In this paper, we present a method for rapid generation of short peptide sequences via tandem mass spectrometry based on a graph search. The approach takes advantage of several pairs of peaks that have high intensities. We proposed a pair peak value set (PPS) and used the pair peak values of highest intensities as the root of a tree. The other nodes are viewed as the reference nodes to find the most promising path. We aimed to determine the peptide sequences for MS/MS spectra that have low signal-to-noise ratios. Our experiment on 2420 experimental MS/MS spectra with two PTMs shows that our algorithm achieves better accuracy than PepNovo approaches with higher efficiency.

Mugizi Robert Rwebangira

Papers

Exploring Social Meaning in Online Bilingual Text through Social Network Analysis

The development of a proteomic analyzing pipeline to identify proteins with multiple RRMs and predict their domain boundaries

Automatic Detection of Small Groups of Persons, Influential Members, Relations and Hierarchy in Written Conversations Using Fuzzy Logic.

Accurate Identification of Mass Peaks for Tandem Mass Spectra Using MCMC Model

Rapid generation of peptide sequence tags with a graph search algorithm