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

Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the text domain. We will discuss the key methods used for text clustering, and their relative advantages. We will also discuss a number of recent advances in the area in the context of social network and linked data.

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A Survey of Text Clustering Algorithms

Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.

Sentiment Analysis: Mining Opinions, Sentiments, and Emotions

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Book Review: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing Liu

Retrieving similar questions is very important in community-based question answering. A major challenge is the lexical gap in sentence matching. In this paper, we propose a convolutional neural tensor network architecture to encode the sentences in semantic space and model their interactions with a tensor layer. Our model integrates sentence modeling and semantic matching into a single model, which can not only capture the useful information with convolutional and pooling layers, but also learn the matching metrics between the question and its answer. Besides, our model is a general architecture, with no need for the other knowledge such as lexical or syntactic analysis. The experimental results shows that our method outperforms the other methods on two matching tasks.

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Convolutional neural tensor network architecture for community-based question answering

While traditional question answering (QA) systems tailored to the TREC QA task work relatively well for simple questions, they do not suffice to answer real world questions The community-based QA systems offer this service well, as they contain large archives of such questions where manually crafted answers are directly available However, finding similar questions in the QA archive is not trivial In this paper, we propose a new retrieval framework based on syntactic tree structure to tackle the similar question matching problem We build a ground-truth set from Yahoo! Answers, and experimental results show that our method outperforms traditional bag-of-word or tree kernel based methods by 83% in mean average precision It further achieves up to 50% improvement by incorporating semantic features as well as matching of potential answers Our model does not rely on training, and it is demonstrated to be robust against grammatical errors as well

https://www.cc.gatech.edu/~zha/CSE8801/QA/p187-wang.pdf

A syntactic tree matching approach to finding similar questions in community-based qa services

This paper presents a domain-assisted approach to organize various aspects of a product into a hierarchy by integrating domain knowledge (e.g., the product specifications), as well as consumer reviews. Based on the derived hierarchy, we generate a hierarchical organization of consumer reviews on various product aspects and aggregate consumer opinions on these aspects. With such organization, user can easily grasp the overview of consumer reviews. Furthermore, we apply the hierarchy to the task of implicit aspect identification which aims to infer implicit aspects of the reviews that do not explicitly express those aspects but actually comment on them. The experimental results on 11 popular products in four domains demonstrate the effectiveness of our approach.

Domain-Assisted Product Aspect Hierarchy Generation: Towards Hierarchical Organization of Unstructured Consumer Reviews

Question detection serves great purposes in the cQA question retrieval task. While detecting questions in standard language data corpus is relatively easy, it becomes a great challenge for online content. Online questions are usually long and informal, and standard features such as question mark or 5W1H words are likely to be absent. In this paper, we explore question characteristics in cQA services, and propose an automated approach to detect question sentences based on lexical and syntactic features. Our model is capable of handling informal online languages. The empirical evaluation results further demonstrate that our model significantly outperforms traditional methods in detecting online question sentences, and it considerably boosts the question retrieval performance in cQA.

Exploiting Salient Patterns for Question Detection and Question Retrieval in Community-based Question Answering

Existing question retrieval models work relatively well in finding similar questions in community-based question answering (cQA) services. However, they are designed for single-sentence queries or bag-of-word representations, and are not sufficient to handle multi-sentence questions complemented with various contexts. Segmenting questions into parts that are topically related could assist the retrieval system to not only better understand the user's different information needs but also fetch the most appropriate fragments of questions and answers in cQA archive that are relevant to user's query. In this paper, we propose a graph based approach to segmenting multi-sentence questions. The results from user studies show that our segmentation model outperforms traditional systems in question segmentation by over 30% in user's satisfaction. We incorporate the segmentation model into existing cQA question retrieval framework for more targeted question matching, and the empirical evaluation results demonstrate that the segmentation boosts the question retrieval performance by up to 12.93% in Mean Average Precision and 11.72% in Top One Precision. Our model comes with a comprehensive question detector equipped with both lexical and syntactic features.

Segmentation of multi-sentence questions: towards effective question retrieval in cQA services

This paper presents a novel prototype hierarchy based clustering (PHC) framework for the organization of web collections. It solves simultaneously the problem of categorizing web collections and interpreting the clustering results for navigation. By utilizing prototype hierarchies and the underlying topic structures of the collections, PHC is modeled as a multi-criterion optimization problem based on minimizing the hierarchy evolution, maximizing category cohesiveness and inter-hierarchy structural and semantic resemblance. The flexible design of metrics enables PHC to be a general framework for applications in various domains. In the experiments on categorizing 4 collections of distinct domains, PHC achieves 30% improvement in ¼F1 over the state-of-the-art techniques. Further experiments provide insights on performance variations with abstract and concrete domains, completeness of the prototype hierarchy, and effects of different combinations of optimization criteria.

Kai Wang

Papers

A syntactic tree matching approach to finding similar questions in community-based qa services

Domain-Assisted Product Aspect Hierarchy Generation: Towards Hierarchical Organization of Unstructured Consumer Reviews

Exploiting Salient Patterns for Question Detection and Question Retrieval in Community-based Question Answering

Segmentation of multi-sentence questions: towards effective question retrieval in cQA services

Prototype hierarchy based clustering for the categorization and navigation of web collections