We develop induction algorithms based on three distinct types of algorithmic framework that have been shown successful for the analogous task of sentiment lexicon induction: HITS & PageRank (§2.1), Label/Graph Propagation (§2.2), and Constraint Optimization via Integer Linear Programming (§2.3).

3.1 Comparison against Conventional Sentiment Lexicon

Note that we consider the connotation lexicon to be inclusive of a sentiment lexicon for two practical reasons: first, it is highly unlikely that any word with non-neutral sentiment (i.e., positive or negative) would carry connotation of the opposite, i.e., conflicting10 polarity.

Therefore, sentiment lexicons can serve as a surrogate to measure a subset of connotation words induced by the algorithms, as shown in Table 3 with respect to General Inquirer (Stone and Hunt (1963)) and MPQA (Wilson et al.

In this section, we present comprehensive intrinsic §5.l and extrinsic §5 .2 evaluations comparing three representative lexicons from §2 & §42 C-LP, OVERLAY, PRED-ARG (CP), and two popular sentiment lexicons : SentiWordNet (Baccianella et al., 2010) and GI+MPQA.14 Note that C-LP is the largest among all connotation lexicons, including ~70,000 polar words.15

The main contribution of this paper is a broad-coverage 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.

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.

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:

13 Note that doing so will prevent us from evaluating against the same sentiment lexicon used as a seed set.

Some recent work explored the use of constraint optimization framework for inducing domain-dependent sentiment lexicon (Choi and Cardie (2009), Lu et al.

Starting with a domain-independent, high-precision sentiment lexicon and a large pool of unlabeled data, we bootstrap Twitter-specific sentiment lexicons , using a small amount of labeled data to guide the process.

General, domain-independent sentiment lexicons have low coverage.

Most of the previous work on sentiment lexicon construction relies on existing natural language

Although bootstrapping has been used for leam-ing sentiment lexicons in other domains (Turney and Littman, 2002; Banea et al., 2008), it has not yet been applied to learning sentiment lexicons for microblogs.

To create a Twitter-specific sentiment lexicon for a given language, we start with a general-purpose, high-precision sentiment lexicon2 and bootstrap from the unlabeled data (BOOT) using the labeled development data (DEV) to guide the process.

Dictionary-based methods rely on existing lexical resources to bootstrap sentiment lexicons .

(2009) use a thesaurus to aid in the construction of a sentiment lexicon for English.

Corpus-based methods extract subjectivity and sentiment lexicons from large amounts of unlabeled data using different similarity metrics to measure the relatedness between words.

We demonstrate our approach on a new dataset just released by Yelp, and release a new sentiment lexicon with 1329 adjectives for the restaurant domain.

Using topic models to discover subtypes of businesses, a domain-specific sentiment lexicon , and a number of new techniques for increasing precision in sentiment aspect extraction yields attributes that give a rich representation of the restaurant domain.

We have made this l329-term sentiment lexicon for the restaurant domain available as useful resource to the community.

First we develop a domain specific sentiment lexicon .

2.2.1 Sentiment Lexicon

To identify noun-phrases which are targeted by predicates in our sentiment lexicon , we develop handcrafted extraction patterns defined over syntactic dependency parses (Blair—Goldensohn et al., 2008; Somasundaran and Wiebe, 2009) generated by the Stanford parser (Klein and Manning, 2003).

In the following paper, we describe our attempts to use modern sentiment lexicons and dialogue structure to algorithmically track and model—with no domain-specific customization—the emotion dynamics between characters in Shakespeare’s

These methods are driven by sentiment lexicons , fixed lists associating words with “valences” (signed integers representing positive and negative feelings) (Kim and Hovy, 2004).

To extract these relationships, we mined for character-to-character sentiment by summing the valence values (provided by the AFINN sentiment lexicon (Nielsen, 2011)) over each instance of continuous speech and then assumed that sentiment was directed towards the character that spoke immediately before the current speaker.

The features used by these models include subjectivity and sentiment lexicons , counts of unigrams and bigrams, distributional similarity, discourse relationships, and so on.

Of the 654 words included in this lexicon, 433 were unique to this lexicon (i.e., recorded in neither Riloff and Wiebe’s (2003) subjectivity lexicon nor Liu et al.’s (2005) sentiment lexicon ) and represented many one-sided or controversial terms, e.g., abortion, same-sex, execute.

To this end, the features based on subjectivity and sentiment lexicons turn out to be helpful, and incorporating more features for stance detection is an important direction for future work.