Sentiment analysis in a multilingual world remains a challenging problem, because developing language-specific sentiment lexicons is an extremely resource-intensive process.

In this paper, we address this lexicon gap by building high-quality sentiment lexicons for 136 major languages.

By appropriately propagating from seed words, we construct sentiment lexicons for each component language of our graph.

Sentiment propagation starts from English sentiment lexicons .

Although several well-regarded sentiment lexicons are available in English (Esuli and Sebastiani, 2006; Liu, 2010), the same is not true for most of the world’s languages.

Indeed, our literature search identified only 12 publicly available sentiment lexicons for only 5 non-English languages (Chinese mandarin, German, Arabic, Japanese and

In this paper, we strive to produce a comprehensive set of sentiment lexicons for the worlds’ major languages.

In this section we will describe how we leverage off a variety of NLP resources to construct the semantic connection graph we will use to propagate sentiment lexicons .

(2010) focus on generating topic specific sentiment lexicons .

(2013) build the top-performed system in the Twitter sentiment classification track of SemEval 2013 (Nakov et al., 2013), using diverse sentiment lexicons and a variety of handcrafted features.

The quality of SSWE is also directly evaluated by measuring the word similarity in the embedding space for sentiment lexicons .

We also directly evaluate the effectiveness of the SSWE by measuring the word similarity in the embedding space for sentiment lexicons .

(5) NRC: NRC builds the top-performed system in SemEval 2013 Twitter sentiment classification track which incorporates diverse sentiment lexicons and many manually designed features.

We achieve 84.98% by using only SSWEu as features without borrowing any sentiment lexicons or handcrafted rules.

We propose a semi-supervised framework for generating a domain-specific sentiment lexicon and inferring sentiments at the segment level.

Our framework can greatly reduce the human effort for building a domain-specific sentiment lexicon with high quality.

Specifically, in our evaluation, working with just 20 manually labeled reviews, it generates a domain-specific sentiment lexicon that yields weighted average F-Measure gains of 3%.

A domain-specific sentiment lexicon con-

Given a set of reviews, the tasks of sentiment analysis in ReNew are (l) splitting each review into segments, (2) associating each segment with a sentiment label (positive, neutral, negative), and (3) automatically generating a domain-specific sentiment lexicon .

a general sentiment lexicon and a small labeled training dataset.

High-quality sentiment lexicons can improve the performance of sentiment analysis models over general-purpose lexicons (Choi and Cardie, 2009).

(2) ReNew leverages the relationships between consecutive segments to infer their sentiments and automatically generates a domain-specific sentiment lexicon in a semi-su-pervised fashion.

(3) To capture the contextual sentiment of words, ReNew uses dependency relation pairs as the basic elements in the generated sentiment lexicon .

We present comprehensive evaluation to demonstrate the quality and utility of the resulting lexicon in comparison to existing connotation and sentiment lexicons .

For comparison, we also test the connotation lexicon from (Feng et al., 2013) and the combined sentiment lexicon GENINQ+MPQA.

Sentiment lexicons such as SentiWordNet (Baccianella et al.

We also extended the seed set with the sentiment lexicon words and denote these runs with E- for ‘Extended’.

ConnotationWordNet is expected to be the superset of a sentiment lexicon , as it is highly likely for any word with positive/negative sentiment to carry connotation of the same polarity.

Thus, we use two conventional sentiment lexicons , General Inquirer (GENINQ) (Stone et al., 1966) and MPQA (Wilson et al., 2005b), as surrogates to measure the performance of our inference algorithm.

The sentiment lexicons we use as gold standard are small, compared to the size (i.e., number of words) our graphs contain.

Several previous approaches explored the use of graph propagation for sentiment lexicon induction (Velikovich et al., 2010) and connotation lexicon

There have been a number of previous studies that aim to construct a word-level sentiment lexicon (Wiebe et al., 2005; Qiu et al., 2009) and a sense-level sentiment lexicon (Esuli and Sebas-tiani, 2006).

Although we focus on learning connotative polarity of words and senses in this paper, the same approach would be applicable to constructing a sentiment lexicon as well.

We conjecture that sentiment prediction for AUTO category is largely driven by one-shot phrases and statements where it is hard to improve upon the bag-of-words and sentiment lexicon features.

We enrich the traditional bag-of-word representation with features from a sentiment lexicon and features quantifying the negation present in the comment.

- lexicon: a sentiment lexicon is a collection of words associated with a positive or negative sentiment.

We use two manually constructed sentiment lexicons that are freely available: the MPQA Lexicon (Wilson et al., 2005) and the lexicon of Hu and Liu (2004).

Prior knowledge of the sentiment of words, such as sentiment lexicons , has been incorporated into cross-domain sentiment classification.

(2011) propose a joint sentiment-topic model that imposes a sentiment-prior depending on the occurrence of a word in a sentiment lexicon .

A sentiment lexicon is used to create features for a document.