Data Mining Practical Machine Learning Tools and Techniques

How to Do Things With Words

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

The measurement of meaning

We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.

Lexicon-based methods for sentiment analysis

Hantology: An Ontology based on Conventionalized Conceptualization

This paper presents the development of a Chinese event-based emotion corpus. It specifically describes the corpus design, collection and annotation. The proposed annotation scheme provides a consistent way of identifying some emotion-associated events (namely pre-events and post-events). Corpus data show that there are significant interactions between emotions and pre-events as well as that of between emotion and post-events. We believe that emotion as a pivot event underlies an innovative approach towards a linguistic model of emotion as well as automatic emotion detection and classification.

/pdf/annotating-events-in-an-emotion-corpus-3ex7eo6fg9.pdf

Annotating Events in an Emotion Corpus

In this paper, we propose a new approach to identify unknown words in Chinese. This approach adopts an n-grams program to sort out the collocating word / character sequences which are possible words and phrases in Chinese. In addition to proposing the criteria for identifying Chinese new words, was also classify these new words according to their structural and semantic characteristics. The corpus-based approach in identifying Chinese disyllabic words based on mutual information was first studied by Sproat and Shih [1]. The attempt here is to identify Chinese unknown words by collocations. Collocations are sequences of words that tend to appear together. In this paper we describe a set of techniques based on statistical methods for retrieving and identifying unknown words from a Chinese corpus. Here unknown words refer to words that are not included in the 90,000 entries CKIP Electronic Dictionary developed at Institute of Information Science, Academia Sinica. The results retrieved by the n-grams program will be crucial information for updating dictionaries. The n-grams program locates words in context and makes statistical observations to identify collocations. It produces a wide range of collocations which can be further sub-classified as abbreviational words, derived words, proper names, new words, ambiguous words, and collocating strings. The effectiveness of the n-grams program as a retrieval tool for unknown words is measured and evaluated.

/pdf/the-identification-and-classification-of-unknown-words-in-1mviaj403d.pdf

The Identification and Classification of Unknown Words in Chinese : A N-Grams-Based Approach

The Academia Sinica Ancient Chinese Corpus is designed for linguistic research. The corpus contains ancient texts that are selected because of their usefulness in grammatical and lexical studies, as well as an inspection program with keyword searching, statistics, and collocation functions. The corpus is divided into three subcorpora according to stages of grammatical developments, thus both synchronic and diachronic studies can be performed on them. Their current sizes are as follows: * E-Mail: Weipc@pluto.ihp.sinica.tw + ** 131

建構一個以共時與歷時語言研究為導向的歷史語料庫 (Historical Corpora for Synchronic and Diachronic Linguistics Studies).

The study of word frequency has been discussed by linguists, psychologists, and computer scientists. However, the results of these studies cannot be valid unless the corpus is big enough and properly-segmented. This paper observes the distributional information derived from word frequency based on . a 14-million-character corpus of Chinese newspaper (CKIP 1993). This is the first available Mandarin Chinese corpus of such magnitude. The word frequency count is obtained with an automatic-segmentation program with above 99% accuracy rate (Chen and Liu 1992). The count reflects some general phenomena of Chinese usage. For example, among the first thousand high frequency words, there are more bi-syllabic words than mono-syllabic words, attesting to the trend of bi-syllabicfication observed by many linguists. However, in general, the mono-syllabic function words occur more frequently than bi-syllabic words. In addition, the frequency of numerals is ranked according to their numeric order ('one' is higher than 'two', and 'two' is in turn higher than 'three', etc.) This paper discusses the theoretical and applicational implications of these distributional properties. For instance, we find that the most frequent 2452 characters and 28124 words make up 99% of the corpus content. It is suggested that the optimal strategy for learning Chinese lies in the mastery of the most frequent 2452 characters plus words whose meanings can not be predicted on the basis of their component characters. This implies that one need not know 28124 words in order to achieve good reading knowledge in Chinese. Given the noted parallel between the internal structure of words and phrases, one can predict that knowledge of a few thousand words and of the morphosyntactic rules will enable one to read 'Chinese without much difficulty.

/pdf/some-distributional-properties-of-mandarin-chinese-a-study-29q11rix6x.pdf

Some Distributional Properties of Mandarin Chinese : A Study based on the Academia Sinica Corpus

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