New Avenues in Opinion Mining and Sentiment Analysis

Knowledge-Based Approaches to Concept-Level Sentiment Analysis

Erik Cambria, National University of Singapore

Björn Schuller, Technical University of Munich

Yunqing Xia, Tsinghua University

Catherine Havasi, Massachusetts Institute of Technology

thers’ opinions can be crucial when it’s time to make a decision or Ochoose among multiple options. When those choices involve valuable The Web holds resources (for example, spending time and money to buy products or services) valuable, vast, people often rely on their peers’ past experiences. Until recently, the main sources and unstructured of information were friends and special- and sentiment analysis actually focus on po- information about ized magazine or websites. Now, the “social larity detection and emotion recognition, web” provides new tools to efficiently create respectively. Because the identification of public opinion. Here, and share ideas with everyone connected to sentiment is often exploited for detecting the World Wide Web. Forums, blogs, social polarity, however, the two fields are usually the history, current networks, and content-sharing services help combined under the same umbrella or even people share useful information. This infor- used as synonyms. Both fields use data min- use, and future of mation is unstructured, however, and being and natural language processing (NLP) cause it’s produced for human consumption, techniques to discover, retrieve, and distill opinion mining and it’s not something that’s “machine process- information and opinions from the World able.” Capturing public opinion about social Wide Web’s vast textual information. sentiment analysis events, political movements, company strat- Mining opinions and sentiments from egies, marketing campaigns, and product natural language is challenging, because are discussed, preferences is garnering increasing interest it requires a deep understanding of the ex- from the scientific community (for the excit- plicit and implicit, regular and irregular, along with relevant ing open challenges), and from the business and syntactical and semantic language world (for the remarkable marketing fall- rules. Sentiment analysis researchers strug- techniques and tools. outs and for possible financial market pre- gle with NLP’s unresolved problems: co- diction). The resulting emerging fields are reference resolution, negation handling, opinion mining and sentiment analysis. Al- anaphora resolution, named-entity recogni- though commonly used interchangeably to tion, and word-sense disambiguation. Opin- denote the same field of study, opinion mining ion mining is a very restricted NLP problem,

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because the system only needs to SenticNet (http://sentic.net), Luminoso emotional content for purposes such understand the positive or negative (http://luminoso.com), Factiva (http:// as affective human-machine interac- sentiments of each sentence and the dowjones.com/factiva), Attensity tion, troll filtering, and cyber-issue target entities or topics. Therefore, (http://attensity.com), and Converseon detection. If the text doesn’t contain sentiment analysis is an opportunity (http://converseon.com). Most existing strong opinions or covers more than for NLP researchers to make tangi- tools and research, however, are lim- one issue or item, new challenges ble progress on all fronts of NLP, ited to polarity evaluation or mood arise, such as subjectivity detection and potentially have a huge practical classification according to a limited and opinion-target identification. impact. set of emotions. Such methods mainly Distinguishing between subjective Many companies use opinion min- rely on parts of text in which people and objective text helps classify the ing and sentiment analysis as part explicitly express emotional states, sentiment. Moreover, a piece of text of their research. For instance, com- and therefore the tools can’t capture a might have a polarity without neces- panies use opinion mining to create reviewer’s implicitly expressed opin- sarily containing an opinion; for ex- and automatically maintain review ion or sentiment. To better consider ample, a news article can be classified and opinion-aggregation websites. the state of this field, we discuss here into good or bad news without being Their systems continuously gather the past, present, and future trends subjective. a wide array of information from of sentiment analysis by delving into Typically, a system performs sentiment the Web, such as product reviews, the evolution of opinion mining sys- analysis over on-topic documents— brand perception, and political is- tems. More comprehensive surveys using, for example, the results of a sues. Other systems might also use on sentiment analysis can be found topic-based search engine. However, opinion mining and sentiment anal- elsewhere.1–3 several studies suggest that managing ysis as subcomponent technology to these two tasks jointly might benefit improve customer relationship man- Common Sentiment overall performance. For example, a agement and recommendation sys- Analysis Tasks document’s off-topic passages might tems through positive and negative The basic task of opinion mining is contain irrelevant affective informa- customer feedback. Similarly, opinion polarity classification. Polarity clas- tion and create inaccurate global- mining and sentiment analysis might sification occurs when a piece of text sentiment polarity about the main detect and exclude “flames” (overly stating an opinion on a single issue is topic. Also, a document might con- heated or antagonistic language) in classified as one of two opposing sen- tain information on multiple top- social communication and enhance timents. Reviews such as “thumbs ics that interest the user. In such antispam systems. up” versus “thumbs down,” or “like” instances, it’s important to identify Companies use sentiment analysis versus “dislike” are examples of po- topics and separate the opinions asso- to develop marketing strategies by larity classification. Polarity classifi- ciated with each topic. assessing and predicting public atti- cations also identify pro and con ex- tudes toward their brand. Research pressions in online reviews and help Evolution of Opinion Mining and development focuses on design- make the product evaluations more Currently, opinion mining and senti- ing automatic tools that crawl online credible. ment analysis rely on vector extrac- reviews and condense the infor Agreement detection is another tion to represent the most salient and mation gathered. Numerous compa- form of binary sentiment classifica- important text features. We can use nies already provide tools that track tion. Agreement detection determines this vector to classify the most relevant public viewpoints on a large scale by whether a pair of text documents features. Two commonly used features offering graphical summarizations should receive the same or different are term frequency and presence. of trends and opinions in the blogo- sentiment-related labels. After the Presence is a binary-valued feature sphere. Developing opinion-tracking system identifies the polarity classi- vector in which the entries indicate systems is commercially important. fication, it might assign degrees of only whether a term occurs (value 1) Also, several tools already exist to positivity to the polarity—that is, it or doesn’t (value 0). Presence forms a help companies extract and analyze might locate the opinion on a con- more effective basis to review polar- information from blogs about large- tinuum between positive and nega- ity classification and reveals an inter- scale trends in customers’ opinions tive. Also, it can classify multi esting difference: although recurrent about products; those tools include media resources according to mood and keywords indicate a topic, repeated

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IS-28-02-Cambria.indd 16 6/5/13 11:05 AM terms might not reflect the overall mostly on linguistic heuristics. For ex- research, Bo Pang and Lillian Lee sentiment. ample, in their work on polarity clas- attempted to partially address this It’s possible to add other term-based sification, Vasileios Hatzivassiloglou problem by incorporating location in- features to the features vector. Po- and Kathleen McKeown discuss how formation into the feature set.7 sition refers to how a token’s posi- two classes of interest represent oppo- More recent studies emphasize the tion in a text unit might affect the sites.4 These opposite constraints help importance of position in sentiment text’s sentiment. Further, we might the system with label decisions. summarization. For example, the in- consider presence n-grams—typically These approaches were unable cipits of articles in topic-based sum- bigrams and trigrams—to be useful to detect novel expression of senti- marization usually indicate the text’s features. Some methods also rely on the ment. Consequently, later work fo- sentiment. However, the last n sen- distance between terms. General tex- cused on propagating the valence of tences of a product review often best tual analysis uses part of speech (POS) seed words (for which the polarity is summarize the document’s overall information (for example, nouns, ad- known) to terms that co-occur with sentiment—almost as well as the n jectives, adverbs, and verbs) as a basic them in general text (or in dictionary (automatically computed) of most sub- form of word-sense disambiguation. glosses) or to synonyms and words jective sentences.7 Mahesh Joshi and Certain adjectives are good indicators that co-occur with them in other Carolyn Penstein-Rosé, for example, of sentiment and guide feature selection WordNet-defined relations. For ex- explored how to use features based on to classify the sentiment. Also, selected ample, Ana-Maria Popescu and Oren syntactic dependency relations to im- phrases chosen by pre-specified POS Etzioni proposed an iterative collec- prove opinion-mining performance.8 patterns, usually including an adjective tive labeling algorithm.5 This algo- They converted a transformation of or adverb, help detect sentiments. rithm starts with a global word label dependency-relation triples into com- Some researchers have developed computed over a large collection of posite back-off features that general- other text mapping techniques that generic topic text. Gradually the al- ize better than the regular, lexicon- assign labels to predefined categories gorithm redefines the label with more based, dependency-relation features. or real numbers representing the de- specificity: first to a specific review gree of polarity. These approaches corpus, then specific to a product fea- From Coarse- to are strictly bound by domain and ture, and finally to a label specific to Fine-Grained Analysis topic. Moreover, most research on the context in which the word occurs. We see opinion mining and senti- sentiment analysis focuses on text Benjamin Snyder and Regina Barzilay ment analysis research evolving in written in English and, consequently, similarly explored using discourse both technique sophistication and most of the resources developed (such information to infer relationships be- analysis depth. Early on, Bo Pang and as sentiment lexicons and corpora) tween product attributes.6 They de- her colleagues classified entire docu- are in English. Applying this research signed a linear classifier that would ments by overall positive or negative to other languages is a domain adap- predict whether all aspects of a prod- polarity, and also by rating scores tation problem. uct would be given the same rating. of reviews.9,10 These documents were Then they combined the prediction mainly supervised, manually labeled From Heuristics with individual-aspect classifiers, samples, such as movie or product re- to Discourse Structure which would minimize loss function. views explicitly indicating an overall In some unsupervised learning ap- For opinionated documents, such positive or negative opinion. proaches, a sentiment lexicon is gen- as product reviews, regression tech- Opinions and sentiments don’t oc- erated and later used to determine the niques are often used to predict the cur only at the document level, nor text unit’s degree of positivity or sub- degree of positivity of opinions. Re- are they limited to a single valence or jectivity. Creating the sentiment lexi- gression techniques implicitly model target. One document might contain con through unsupervised polarity similar relationships between classes positive and negative opinions to- or subjectivity labeling of words or that correspond to points on a scale, ward one or more topics. Hence, later phrases is crucial.1 The sentiment lexi- such as the number of stars that a re- work adopted a segment-level opin- con identifies a term or a phrase’s prior viewer gives.1 Modeling discourse ion analysis that used graph-based polarity or prior subjectivity, which in structure, such as twists and turns techniques to distinguish sentimen- turn helps identify contextual polarity in a document, leads to more effec- tal from unsentimental sections. Pang or subjectivity. Early works focused tive sentiment labeling. In earlier and Lee used segment-level opinion

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analysis in their work to segment sec- tokens, or building blocks, and the Lexical affinity. This approach not tions of a document by subjectiv- implicit information associated with only detects obvious affect words, it ity. In another study, Peter Turney those tokens. We can group the also assigns arbitrary words a probable classified items based on fixed, syn- existing approaches into four main “affinity” to particular emotions. For tactic phrases used for expressing categories: keyword spotting, lexi- example, lexical affinity might as- opinions.11 Finally, Jaap Kamps and cal affinity, statistical methods, and sign the word “accident” a 75-percent his colleagues classified items by concept-based techniques. probability of indicating a negative bootstrapping—using a small set of affect, as in “car accident” or “hurt seed opinion words and a knowledge Keyword spotting. Although the most by accident.” This approach usu- base such as WordNet.12 naïve approach, keyword spotting’s ally trains probability from linguistic In another work, Ellen Riloff and accessibility and economy make it corpora.21–23 Although it often out- Janyce Weibe reduced text-analysis popular. This approach classifies text performs pure keyword spotting, there granularity to the sentence level by by affect categories based on the pres- are two main problems with this using the presence of opinion-bearing ence of unambiguous affect words approach. First, negated sentences lexical items (single words or n- such as happy, sad, afraid, and bored. (I avoided an accident) and sentences grams) to detect subjective sen- For example, Clark Elliott’s Affective with other meanings (I met my girl- tences.13 Soo-Min Kim and Eduard Reasoner watches for 198 affect key- friend by accident) trick lexical affin- Hovy, instead, used semantic frames words (such as distressed or enraged), ity, because they operate solely on the that identified sentimental topics (or affect intensity modifiers (such as ex- word level. Second, lexical affinity targets).14 Reviewers tend to adhere tremely, somewhat, or mildly), and a probabilities are often biased toward to being either subjective or objective, handful of cue phrases (such as did text of a particular genre, dictated by and that creates continuity among that and wanted to).18 Other popular the linguistic corpora’s source. This adjacent sentences. Hence, other resources of affect words are Andrew makes it difficult to develop a re searchers collectively classify docu- Ortony and his colleagues’ Affec- usable, domain-independent model. ments by assigning preferences for tive Lexicon,19 which groups terms pairs of nearby sentences.10 into affective categories, and Janyce Statistical methods. This approach, Even sentence-level approaches of- Wiebe and her colleagues’ linguistic which includes Bayesian inference ten fail to discover sentiments about annotation scheme.20 and support vector machines, is pop- an entity and/or its aspects. To cor- Keyword spotting is weak in two ular for affect text classification. Re- rect that, other researchers adopted areas: it can’t reliably recognize affect- searchers use statistical methods on an aspect-level approach, wherein an negated words, and it relies on sur- projects such as Pang’s movie review opinion consists of targets and the face features. Although keyword spot- classifier and many others.9,10,15,24 By sentiments associated with them.15–17 ting can correctly classify the sentence feeding a machine-learning algorithm For example, the sentence “the new “today was a happy day” as being af- a large training corpus of affectively iPhone 5’s screen size is amazing, but fectively positive, it is likely to assign annotated texts, the system might its battery life is short” evaluates two the same classification to a sentence not only learn the affective valence of aspects (opinion targets): the screen like “today wasn’t a happy day at all.” affect keywords (as in the keyword- size and battery life of the same en- Also, keyword spotting relies on the spotting approach), but also take into tity. The sentiment about the iPhone presence of obvious affect words that account the valence of other arbitrary 5’s screen size is positive, but the sen- are only surface features of the prose. keywords (similar to lexical affinity), timent about its battery life is nega- Sometimes, a sentence conveys affect punctuation, and word co-occurrence tive. Based on this level of analysis, through underlying meaning rather frequencies. we can produce a structured opinion than affect adjectives. For example, Generally, statistical methods are summary about an entity and its as- the text “My husband just filed for di- semantically weak, which means that pects, and can draw more accurate vorce and he wants to take custody of individually—with the exception of statistics about those aspects. my children away from me” evokes obvious affect keywords—a sta- strong emotions, but uses no affect tistical model’s other lexical or co- From Keywords to Concepts keywords, and therefore is ineffec- occurrence elements have little predic- We can study the evolution of senti- tive. Lexical affinity is slightly more tive value. As a result, statistical text ment analysis research by the analytical sophisticated than keyword spotting. classifiers only work well when they

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IS-28-02-Cambria.indd 18 6/5/13 11:05 AM receive sufficiently large text input. in an audio or audiovisual format rather it might be extremely valuable when a So, while these methods might be than in text. For a rough idea of the textual transcript is unavailable, and able to affectively classify a user’s text amount of material, consider that You- we need a performance point of view on the page level or paragraph level, Tube users upload two days’ worth of for synergy effects and fail-safeness. they don’t work well on smaller text video material to its website every min- In the latter respect, it will be particu- units such as sentences or clauses. ute. Aside from converting spoken lan- larly interesting to see further modali- guage to written text for analysis, the ties involved—such as physiological Concept-based approaches. These audiovisual format provides an oppor- and brain signals, along with the use methods use Web ontologies or tunity to mine opinions and sentiment. of contextual knowledge. We’ll then semantic networks to accomplish se- Many new areas might be useful in need to investigate analyses of robust- mantic text analysis.25–27 This helps opinion mining, such as facial expres- ness against disturbances in individual the system grasp the conceptual and sion, body movement, or a video blog- (or all) modalities alongside audio affective information associated with ger’s choice of music or color filters. visual confidence estimation. natural language opinions. By relying Affect analysis, a related field, ad- on large semantic knowledge bases, dresses the use of linguistic, acous- Discussion such approaches step away from tic, and (potentially) video informa- Gradually, sentiment analysis re- blindly using keywords and word co- tion. This field focuses on a broader search is distinguishing itself as a sep- occurrence counts, and instead rely set of emotions or the estimation of arate field, falling between NLP and on the implicit meaning/features as- continuous emotion primitives; for natural language understanding. Un- sociated with natural language con- example, valence can be related to like standard syntactical NLP tasks, cepts. Superior to purely syntactical sentiment. In one study, research- such as summarization and auto- techniques, concept-based approaches ers provide recent surveys on spoken categorization, opinion mining mainly can detect subtly expressed senti- and written-language-based analy- focuses on semantic inferences and ments. Concept-based approaches sis; in another study, researchers ex- affective information associated with can analyze multi-word expressions plore further multimodal combina- natural language, and doesn’t require that don’t explicitly convey emotion, tions.28,29 There’s almost no research a deep understanding of text. We en- but are related to concepts that do. that focuses on multimodal sentiment vision sentiment analysis research The concept-based approach relies and opinion analysis. Stephan Raaij- moving toward content-, concept-, heavily on the depth and breadth of makers and his colleagues fuse acous- and context-based analysis of natu- the knowledge bases it uses. Without tic and linguistic information, but ral language text, supported by time- a comprehensive resource that encom- that information is based on the tran- efficient parsing techniques suitable passes human knowledge, an opinion- script of the spoken content rather for big social data analysis.32 mining system will have difficulty than on automatic speech recognition Collecting opinions on the Web will grasping the semantics of natural lan- output.30 In addition to this research, still require processing at the content/ guage text. Moreover, the typicality Louis-Philippe Morency and his col- syntactic level, filtering out unopin- of knowledge bases—that is, the fact leagues combine acoustic, textual, ionated user-generated content (sub- that they contain only typical informa- and video features to assess opinion jectivity detection) and evaluating the tion associated with concepts—limits polarity in 47 YouTube videos.31 They trustworthiness of the opinion and its their capability to handle semantic demonstrate significant improvement source. By contrast, concept/semantic nuances. Their fixed/flat representa- in leave-one-video-out evaluation us- analysis infers semantic and affective tion, finally, places bounds on infer- ing Hidden Markov Models for clas- information associated with natural ences of semantic and affective fea- sification. The authors identified po- language opinions, and hence, enables tures associated with concepts. larized words, smiles, gazes, pauses, a comparative fine-grained feature- and voice pitch as relevant features. based sentiment analysis. Rather than Multimodal Again, the researchers relied on tran- gathering isolated opinions about a Sentiment Analysis scripts to analyze the text and not the whole item, users generally prefer to New sources of opinion mining and actual spoken word. compare specific features of differ- sentiment analysis abound. Webcams Multimodal sentiment analysis hasn’t ent products (for example, the iPhone 5 installed in smartphones, touchpads, been fully explored, but holds great versus the Galaxy S3 touchscreen) or other devices let users post opinions promise as an application. For example, or even sub-features (comparing the

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fragility of iPhone 5 and Galaxy S3 own discourse, models, and methods. 5. A. Popescu and O. Etzioni, “Extracting touchscreens). To make these com- Opinion mining and sentiment analysis Product Features and Opinions from parisons, researchers must construct are inextricably bound to the affective Reviews,” Proc. Human Language comprehensive common-knowledge sciences that attempt to understand Technology Conf./Conf. Empirical bases to spot features and common- human emotions. Affect-sensitive sys- Methods in Natural Language Process- sense bases to detect polarity.33 Such tems and psychological emotion re- ing, Assoc. for Computational Linguis- commonsense bases, in particular, will search must develop together. tics, 2005, pp. 339–346. be key in properly deconstructing nat- Recent approaches aim to better 6. B. Snyder and R. Barzilay, “Multiple As- ural language text into sentiments— grasp the conceptual rules that gov- pect Ranking Using the Good Grief Al- for example, in appraising the concept ern sentiment, as well as the clues that gorithm,” Proc. Ann. Conf. North Am. “small room” as negative for a hotel re- can convert these concepts from real- Chapter of the Assoc. for Computational view and “small queue” as positive for ization to verbalization in the human Linguistics, Assoc. for Computational a post office, or the concept “go read mind. Future opinion-mining systems Linguistics, 2007, article no. N07-1038. the book” as positive for a book review need broader and deeper common and 7. B. Pang and L. Lee, “A Sentimental but negative for a movie review. commonsense knowledge bases. More Education: Sentiment Analysis Using Context-/intent-level analysis ensures complete knowledge must be combined Subjectivity Summarization Based on the relevance of the opinions gathered. with reasoning methods that are more Minimum Cuts,” Proc. 42nd Ann. Social context will continue to gain im- deeply inspired by human thought and Meeting of the Assoc. for Computa- portance, and an intelligent system will psychology. This will lead to a bet- tional Linguistics, Assoc. for Computa- have access to the comprehensive per- ter understanding of natural language tional Linguistics, 2004, pp. 271–278. sonal information of vast numbers of opinions and will more efficiently 8. M. Joshi and C. Penstein-Rosé, “Gener- people. Opinion mining will be specific bridge the gap between (unstructured) alizing Dependency Features for Opin- to each user’s or group of users’ pref- multimodal information and (struc- ion Mining,” Proc. 47th Ann. Meet- erences and needs. Opinions won’t be tured) machine-processable data. ing of the Assoc. for Computational generic, but will reflect their source (for Blending scientific theories of emo- Linguistics and the 4th Int’l Joint Conf. example, a relevant circle of friends or tion with the practical engineering Natural Language Processing of the users with similar interests, or the se- goals of analyzing sentiments in natural- Asian Federation of Natural Language lection of a camera for trekking rather language text will lead to more bio- Processing, Assoc. for Computational than for night shooting). inspired approaches to the design of Linguistics, 2009, pp. 313–316. intelligent opinion-mining systems 9. B. Pang, L. Lee, and S. Vaithyanathan, he Web has changed from “read- capable of handling semantic knowl- “Thumbs Up? Sentiment Classification Us- Tonly” to “read-write.” This evo- edge, making analogies, learning new ing Machine Learning Techniques,” Proc. lution created enthusiastic users in- affective knowledge, and detecting, Ann. Conf. Empirical Methods in Natural teracting and sharing through social perceiving, and “feeling” emotions. Language Processing, Assoc. for Compu- networks, online communities, blogs, tational Linguistics, 2002, pp. 79–86. wikis, and other collaborative me- References 10. B. Pang and L. Lee, “Seeing Stars: Exploit- dia. Collective knowledge has spread 1. B. Pang and L. Lee, “Opinion Mining ing Class Relationships for Sentiment throughout the Web, particularly in and Sentiment Analysis,” Foundations Categorization with Respect to Rating areas related to everyday life, such as and Trends in Information Retrieval, Scales,” Proc. 43rd Ann. Assoc. for Com- commerce, tourism, education, and vol. 2, nos. 1–2, 2008, pp. 1–135. putational Linguistics, Assoc. for Compu- health. Despite significant progress, 2. B. Liu, Sentiment Analysis and Opinion tational Linguistics, 2005, pp. 115–124. however, opinion mining and senti- Mining, Morgan and Claypool, 2012. 11. P. Turney, “Thumbs Up or Thumbs ment analysis are still finding their own 3. E. Cambria and A. Hussain, Sentic Down? Semantic Orientation Applied to voice as new interdisciplinary fields. Computing: Techniques, Tools, and Unsupervised Classification of Reviews,” Engineers and computer scientists Applications, Springer, 2012. Proc. 40th Ann. Assoc. for Computa- use machine-learning techniques for 4. V. Hatzivassiloglou and K. McKeown, tional Linguistics, Assoc. for Computa- automatic affect classification from “Predicting the Semantic Orientation tional Linguistics, 2002, pp. 417–424. video, voice, text, and physiology. of Adjectives,” Proc. 8th Conf. Assoc. 12. J. 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IS-28-02-Cambria.indd 20 6/5/13 11:05 AM The Authors

Erik Cambria is a research scientist in the Cognitive Science Programme, Temasek Labo- Language Resources and Evaluation, ratories, National University of Singapore. His research interests include AI, the Seman- European Language Resources Assoc., tic Web, natural language processing, and big social data analysis. Cambria has a PhD in computing science and mathematics from the University of Stirling. He is on the editorial 2004, pp. 1115–1118. board of Springer’s Cognitive Computation and is the chair of many international con- 13. E. Riloff and J. Wiebe, “Learning ferences. Contact him at [email protected]. Extraction Patterns for Subjective Björn Schuller leads the Machine Intelligence and Signal Processing group at the Insti- Expressions,” Proc. 2003 Conf. Em- tute for Human-Machine Communication at the Technical University of Munich. His re- pirical Methods in Natural Language search interests include machine learning, affective computing, and automatic speech rec- Processing, Assoc. for Computational ognition. Schuller has a PhD in electrical engineering and information technology from the Technical University of Munich. Contact him at [email protected]. Linguistics, 2003, pp. 105–112. 14. S. Kim and E. Hovy, “Extracting Yunqing Xia is an associate professor in the Tsinghua National Laboratory for Informa- Opinions, Opinion Holders, and Topics tion Science and Technology at Tsinghua University. His research interests include natural language processing, information retrieval, and text mining. Xia has a PhD in com- Expressed in Online News Media Text,” puter science from the Chinese Academy of Science. Contact him at [email protected]. Proc. Workshop on Sentiment and Sub- jectivity in Text, Assoc. for Computa- C atherine Havasi is a cofounder of the Open Mind Common Sense project at the Mas- sachusetts Institute of Technology (MIT) Media Lab, where she works as a postdoctoral tional Linguistics, 2006, pp. 1–8. associate. Her research interests include commonsense reasoning, dimensionality reduc- 15. M. Hu and B. Liu, “Mining and Sum- tion, machine learning, language acquisition, cognitive modeling, and intelligent user in- marizing Customer Reviews,” Proc. terfaces. Havasi has a PhD in computer science from Brandeis University. Contact her at [email protected]. 10th ACM SIGKDD Conf. Knowledge Discovery and Data Mining, ACM, 2004, pp. 168 –177. 22. S. Somasundaran, J. Wiebe, and Mining,” Biologically Inspired Cogni- 16. B. Lu et al., “Multi-Aspect Sentiment J. Ruppenhofer, “Discourse Level tive Architectures, vol. 4, 2013, pp. 41–53. Analysis with Topic Models,” Proc. Opinion Interpretation,” Proc. 22nd 28. B. 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