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

Data Mining Practical Machine Learning Tools and Techniques

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

/pdf/opinion-mining-and-sentiment-analysis-3v5ya3qqiw.pdf

Opinion Mining and Sentiment Analysis

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.

/pdf/sentiment-analysis-and-opinion-mining-8314pykwrf.pdf

Sentiment Analysis and Opinion Mining

Unsupervised vector-based approaches to semantics can model rich lexical meanings, but they largely fail to capture sentiment information that is central to many word meanings and important for a wide range of NLP tasks. We present a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term--document information as well as rich sentiment content. The proposed model can leverage both continuous and multi-dimensional sentiment information as well as non-sentiment annotations. We instantiate the model to utilize the document-level sentiment polarity annotations present in many online documents (e.g. star ratings). We evaluate the model using small, widely used sentiment and subjectivity corpora and find it out-performs several previously introduced methods for sentiment classification. We also introduce a large dataset of movie reviews to serve as a more robust benchmark for work in this area.

/pdf/learning-word-vectors-for-sentiment-analysis-19n5e5xfor.pdf

Learning Word Vectors for Sentiment Analysis

We present two methods for determining the sentiment expressed by a movie review. The semantic orientation of a review can be positive, negative, or neutral. We examine the effect of valence shifters on classifying the reviews. We examine three types of valence shifters: negations, intensifiers, and diminishers. Negations are used to reverse the semantic polarity of a particular term, while intensifiers and diminishers are used to increase and decrease, respectively, the degree to which a term is positive or negative. The first method classifies reviews based on the number of positive and negative terms they contain. We use the General Inquirer to identify positive and negative terms, as well as negation terms, intensifiers, and diminishers. We also use positive and negative terms from other sources, including a dictionary of synonym differences and a very large Web corpus. To compute corpus-based semantic orientation values of terms, we use their association scores with a small group of positive and negative terms. We show that extending the term-counting method with contextual valence shifters improves the accuracy of the classification. The second method uses a Machine Learning algorithm, Support Vector Machines. We start with unigram features and then add bigrams that consist of a valence shifter and another word. The accuracy of classification is very high, and the valence shifter bigrams slightly improve it. The features that contribute to the high accuracy are the words in the lists of positive and negative terms. Previous work focused on either the term-counting method or the Machine Learning method. We show that combining the two methods achieves better results than either method alone.

/pdf/sentiment-classification-of-movie-reviews-using-contextual-17123j34lj.pdf

SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS

We present a method for determining the sentiment expressed by a customer review. The semantic orientation of a review can be positive, negative, or neutral. Our method counts positive and negative terms, but also takes into account contextual valence shifters, such as negations and intensifiers. Tests are done taking both negations and intensifiers into account, and also using only negations without intensifiers. Negations are used to reverse the semantic polarity of a particular term, while intensifiers are used to change the degree to which a term is positive or negative. We use the General Inquirer in order to identify positive and negative terms, as well as negations, overstatements, and understatements. We also test the impact of adding extra positive and negative terms from other sources, including a dictionary of synonym differences and a very large web corpus. To compute the corpus-based values of the semantic orientation of individual terms we use their association scores with a small group of positive and negative terms. We show that including contextual valence shifters improves the accuracy of the classification.

Sentiment Classification of Movie and Product Reviews Using Contextual Valence Shifters

The research reported in this paper is the first phase of a larger project on the automatic classification of Web pages by their genres. The long term goal is the incorporation of web page genre into the search process to improve the quality of the search results. In this phase, a neural net classifier was trained to distinguish home pages from non-home pages and to classify those home pages as personal home page, corporate home page or organization home page. Results indicate that the classifier is able to distinguish home pages from non-home pages and within the home page genre it is able to distinguish personal from corporate home pages. Organization home pages, however, were more difficult to distinguish from personal and corporate home pages.

https://web.cs.dal.ca/~shepherd/pubs/home_pages.pdf

Automatic Identification of Home Pages on the Web

The research reported in this paper is part of a larger project on the automatic classification of web pages by their genres. The long term goal is the incorporation of web page genre into the search process to improve the quality of the search results. In this phase, a neural net classifier was trained to distinguish home pages from non-home pages and to classify those home pages as personal home page, corporate home page or organization home page. In order to evaluate the importance of the functionality attribute of cybergenre in such classification, the web pages were characterized by the cybergenre attributes of 〈content, form, functionality〉 and the resulting classifications compared to classifications in which the web pages were characterized by the genre attributes of 〈content, form〉. Results indicate that the classifier is able to distinguish home pages from non-home pages and within the home page genre it is able to distinguish personal from corporate home pages. Organization home pages, however, were more difficult to distinguish from personal and corporate home pages. A significant improvement was found in identifying personal and corporate home pages when the functionality attribute was included.

/pdf/cybergenre-automatic-identification-of-home-pages-on-the-web-1i39bq1f1u.pdf

Cybergenre: automatic identification of home pages on the web

Roget’s Thesaurus has gone through many revisions since it was first published 150 years ago. But how do these revisions affect Roget’s usefulness for NLP? We examine the differences in content between the 1911 and 1987 versions of Roget’s, and we test both versions with each other and WordNet on problems such as synonym identification and word relatedness. We also present a novel method for measuring sentence relatedness that can be implemented in either version of Roget’s or in WordNet. Although the 1987 version of the Thesaurus is better, we show that the 1911 version performs surprisingly well and that often the differences between the versions of Roget’s and WordNet are not statistically significant. We hope that this work will encourage others to use the 1911 Roget’s Thesaurus in NLP tasks.

/pdf/evaluating-roget-s-thesauri-pgpu637cwn.pdf

Alistair Kennedy

Papers

SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS

Sentiment Classification of Movie and Product Reviews Using Contextual Valence Shifters

Automatic Identification of Home Pages on the Web

Cybergenre: automatic identification of home pages on the web

Evaluating Roget's Thesauri