Understanding the connotation of words plays an important role in interpreting subtle shades of sentiment beyond denotative or surface meaning of text , as seemingly objective statements often allude nuanced sentiment of the writer , and even purposefully conjure emotion from the readers ' minds . The focus of this paper is drawing nuanced , connotative sentiments from even those words that are objective on the surface , such as " intelligence " , " human " , and " cheesecake " . We propose induction algorithms encoding a diverse set of linguistic insights ( semantic prosody , distributional similarity , semantic parallelism of coordination ) and prior knowledge drawn from lexical resources , resulting in the first broad-coverage connotation lexicon . There has been a substantial body of research in sentiment analysis over the last decade ( Pang and Lee , 2008 ) , where a considerable amount of work has focused on recognizing sentiment that is generally explicit and pronounced rather than implied and subdued . However in many real-world texts , even seemingly objective statements can be opinion-laden in that they often allude nuanced sentiment of the writer ( Greene and Resnik , 2009 ) , or purposefully conjure emotion from the readers ' minds ( Mohammad and Turney , 2010 ) . Although some researchers have explored formal and statistical treatments of those implicit and implied sentiments ( e.g. Wiebe et al . ( 2005 ) , Esuli and Sebastiani ( 2006 ) , Greene and Resnik ( 2009 ) , Davidov et al . ( 2010 ) ) , automatic analysis of them largely remains as a big challenge . In this paper , we concentrate on understanding the connotative sentiments of words , as they play an important role in interpreting subtle shades of sentiment beyond denotative or surface meaning of text . For instance , consider the following : Geothermal replaces oil-heating ; it helps reducing greenhouse emissions.1 Although this sentence could be considered as a factual statement from the general standpoint , the subtle effect of this sentence may not be entirely objective : this sentence is likely to have an influence on readers ' minds in regard to their opinion toward " geothermal " . In order to sense the subtle overtone of sentiments , one needs to know that the word " emissions " has generally negative connotation , which geothermal reduces . In fact , depending on the pragmatic contexts , it could be precisely the intention of the author to transfer his opinion into the readers ' minds . The main contribution of this paper is a broadcoverage connotation lexicon that determines the connotative polarity of even those words with ever so subtle connotation beneath their surface meaning , such as " Literature " , " Mediterranean " , and " wine " . Although there has been a number of previous work that constructed sentiment lexicons ( e.g. , Esuli and Sebastiani ( 2006 ) , Wilson et al . ( 2005a ) , Kaji and Kitsuregawa ( 2007 ) , Qiu et al . ( 2009 ) ) , which seem to be increasingly and inevitably expanding over words with ( strongly ) connotative sentiments rather than explicit sentiments alone ( e.g. , " gun " ) , little prior work has directly tackled this problem of learning connotation , 2 and much of the subtle connotation of many seemingly objective words is yet to be determined . POSITIVE NEGATIVE FEMA , Mandela , Intel , Google , Python , Sony , Pulitzer , Harvard , Duke , Einstein , Shakespeare , Elizabeth , Clooney , Hoover , Goldman , Swarovski , Hawaii , Yellowstone Katrina , Monsanto , Halliburton , Enron , Teflon , Hiroshima , Holocaust , Afghanistan , Mugabe , Hutu , Saddam , Osama , Qaeda , Kosovo , Helicobacter , HIV A central premise to our approach is that it is collocational statistics of words that affect and shape the polarity of connotation . Indeed , the etymology of " connotation " is from the Latin " com- " ( " together or with " ) and " notare " ( " to mark " ) . It is important to clarify , however , that we do not simply assume that words that collocate share the same polarity of connotation . Although such an assumption played a key role in previous work for the analogous task of learning sentiment lexicon ( Velikovich et al . , 2010 ) , we expect that the same assumption would be less reliable in drawing subtle connotative sentiments of words . As one example , the predicate " cure " , which has a positive connotation typically takes arguments with negative connotation , e.g. , " disease " , when used as the " relieve " sense . 3 Therefore , in order to attain a broad coverage lexicon while maintaining good precision , we guide the induction algorithm with multiple , carefully selected linguistic insights : [ 1 ] distributional similarity , [ 2 ] semantic parallelism of coordination , [ 3 ] selectional preference , and [ 4 ] semantic prosody ( e.g. , Sinclair ( 1991 ) , Louw ( 1993 ) , Stubbs ( 1995 ) , Stefanowitsch and Gries ( 2003 ) ) ) , and also exploit existing lexical resources as an additional inductive bias . We cast the connotation lexicon induction task as a collective inference problem , and consider approaches based on three distinct types of algorithmic framework that have been shown successful for conventional sentiment lexicon induction : Random walk based on HITS / PageRank ( e.g. , Kleinberg ( 1999 ) Label / Graph propagation ( e.g. , Zhu and Ghahra-(2011 ) but with practical limitations . See § 3 for detailed discussion . 3 Note that when " cure " is used as the " preserve " sense , it expects objects with non-negative connotation . Hence wordsense-disambiguation ( WSD ) presents a challenge , though not unexpectedly . In this work , we assume the general connotation of each word over statistically prevailing senses , leaving a more cautious handling of WSD as future work . mani ( 2002 ) , Velikovich et al . ( 2010 ) ) Constraint optimization ( e.g. , Roth and Yih ( 2004 ) , Choi and Cardie ( 2009 ) , Lu et al . ( 2011 ) ) . We provide comparative empirical results over several variants of these approaches with comprehensive evaluations including lexicon-based , human judgments , and extrinsic evaluations . It is worthwhile to note that not all words have connotative meanings that are distinct from denotational meanings , and in some cases , it can be difficult to determine whether the overall sentiment is drawn from denotational or connotative meanings exclusively , or both . Therefore , we encompass any sentiment from either type of meanings into the lexicon , where non-neutral polarity prevails over neutral one if some meanings lead to neutral while others to non-neutral . 4Our work results in the first broad-coverage connotation lexicon,5 significantly improving both the coverage and the precision of Feng et al . ( 2011 ) . As an interesting by-product , our algorithm can be also used as a proxy to measure the general connotation of real-world named entities based on their collocational statistics . Table 1 highlights some example proper nouns included in the final lexicon . The rest of the paper is structured as follows . In § 2 we describe three types of induction algorithms followed by evaluation in § 3 . Then we revisit the induction algorithms based on constraint optimization in § 4 to enhance quality and scalability . § 5 presents comprehensive evaluation with human judges and extrinsic evaluations . Related work and conclusion are in § 6 and § 7 . We presented a broad-coverage connotation lexicon that determines the subtle nuanced sentiment of even those words that are objective on the surface , including the general connotation of realworld named entities . Via a comprehensive evaluation , we provided empirical insights into three different types of induction algorithms , and proposed one with good precision , coverage , and efficiency .

Challenge: Most works on automatic sentiment analysis focus on explicit expression while subtle implicit and implied sentiments have not been investigated.
Approach: They regard the connotation lexicon induction task as a collective inference problem and propose induction algorithms that encode a diverse set of linguistic insights.
Outcome: They show their algorithms can significantly improve the coverage and the precision from the previous method and also measure named entities on the collocational statistics.