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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

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

Linguistic synaesthesia in Chinese

A skip-bigram is a bigram that allows skips between words In this paper, we use a set of skip bigrams (a SBGSet) to represent a short word sequence, which is the typical form of a product feature The advantage of SBGSet representation for word sequences is that we can convert between a sequence and a set Under the SBGSet representation we can employ association rule mining to find frequent itemsets from which frequent product features can be extracted For infrequent product features, we use a pattern-based method to extract them A pattern is also represented by a SBGSet, and contains a variable that can be instantiated to a product feature We use two data sets to evaluate our method The experimental result shows that our method is suitable for extracting Chinese product features, and the pattern-based method to extract infrequent product features is effective

Extracting chinese product features: representing a sequence by a set of skip-bigrams

ABSTRACT Sensorimotor information is vital to the conceptual representation of our knowledge system. This study collects perceptual and action ratings for 664 disyllabic nouns among 438 native speakers and creates the first and largest dataset of sensorimotor norms for nouns in Chinese. Using aggregated semantic covariates, including concreteness ratings from a concreteness rating study, as well as the reaction times and error rates from a lexical decision study, our current work demonstrates the strengths of sensory modalities and action effectors in Chinese nouns and explores the contributions of embodied experiences in reflecting orthographic representations and semantic processing in the Chinese language. This study contributes valuable data sources to the study of Chinese lexical processing and highlights the importance of sensorimotor information and embodied manifestations in the semantic representations of concepts. Our results also support the language universal that orthographic awareness in lexical processing and reading supersedes phonological awareness.

Sensorimotor norms for Chinese nouns and their relationship with orthographic and semantic variables

This paper presents a linguistic analysis of emotions by introducing some concrete linguistic rules in identifying the important elements of an emotion, which are the experiencer and the cause. It specifically discusses two primary emotions, happiness and surprise, in Chinese. These rules can be very useful for emotion detection and classification. We also examine the features of a cause event according to its degree of transitivity, namely agentivity, kinesis, and event participation. We find that the emotion classes (happiness vs. surprise) override the emotion verb types (change-of-state verb vs. homogeneous verb) in terms of cause event features. We believe that this study will have some implications for the linguistic account of causal events as well as the theory of emotions in general. We also hope that this work will shed lights in the automatic detection and classification of emotion in language technology.

Cause Event Representations for Happiness and Surprise

Cross-lingual sentiment classification aims to predict the sentiment orientation of a text in a language (named as the target language) with the help of the resources from another language (named as the source language). However, current cross-lingual performance is normally far away from satisfaction due to the huge difference in linguistic expression and social culture. In this paper, we suggest to perform active learning for cross-lingual sentiment classification, where only a small scale of samples are actively selected and manually annotated to achieve reasonable performance in a short time for the target language. The challenge therein is that there are normally much more labeled samples in the source language than those in the target language. This makes the small amount of labeled samples from the target language flooded in the aboundance of labeled samples from the source language, which largely reduces their impact on cross-lingual sentiment classification. To address this issue, we propose a data quality controlling approach in the source language to select high-quality samples from the source language. Specifically, we propose two kinds of data quality measurements, intra- and extra-quality measurements, from the certainty and similarity perspectives. Empirical studies verify the appropriateness of our active learning approach to cross-lingual sentiment classification.

/pdf/active-learning-for-cross-lingual-sentiment-classification-1ycxjvxbis.pdf

Active Learning for Cross-Lingual Sentiment Classification

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