Challenge: Existing studies on sentiment lexicons have focused on domain-dependent sentiment words.
Approach: They propose a graph-based technique to detect and correct domain-dependent sentiment words . they propose to use a sentiment lexicon to classify sentiments in a lexical-based classifier .
Outcome: The proposed method is effective on multiple datasets from different domains.

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Enhancing a Lexicon of Polarity Shifters through the Supervised Classification of Shifting Directions (2020.lrec-1)

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Challenge: Existing polarity shifter lexica only specify when a word can cause shifting, but do not specify when this is limited to a single shifting direction.
Approach: They propose a classifier that determines the shifting direction of polarity shifters by using resource-driven features and data-driven feature.
Outcome: The proposed classifier enhances the largest available polarity shifter lexicon.
Domain-Specific Sentiment Lexicons Induced from Labeled Documents (2020.coling-main)

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Challenge: Existing sentiment lexicons reflect abstract notion of polarity and do not do justice to substantial differences of word polarities between domains.
Approach: They propose to use domain-specific sentiment lexicons to induce initial word intensity scores and train new deep models based on word vector representations to overcome the scarcity of the seed data.
Outcome: The proposed models show that they perform well on review classification and cross-lingual word sentiment prediction.
Introducing a Lexicon of Verbal Polarity Shifters for English (L18-1)

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Challenge: Negation words can change the sentiment polarity of a phrase, but there are more than 1200 other polarities.
Approach: They propose a lexicon of verbal polarity shifters that covers the entirety of verbs found in WordNet.
Outcome: The proposed lexicon covers the entirety of verbs found in WordNet.
Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification (P18-1)

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Challenge: Cross-domain sentiment classification is challenging due to polarity orientation and significance differences . supervised learning algorithms have to be re-trained on every new domain .
Approach: They propose that words that do not change their polarity and significance represent transferable information across domains for cross-domain sentiment classification.
Outcome: The proposed method improves cross-domain sentiment classification performance by identifying polarity-preserving significant words across domains.
Classifier-based Polarity Propagation in a WordNet (L18-1)

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Challenge: a wordnet-based sentiment lexicon can be built to express sentiment polarity in a way shared across domains.
Approach: They propose a method to build a sense-level sentiment lexicon on the basis of a wordnet . they use a rich set of wordnet-based features to recognize and assign sentiment polarity values .
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Enhancing General Sentiment Lexicons for Domain-Specific Use (C18-1)

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Challenge: Lexicon based methods for sentiment analysis rely on high quality polarity lexicons.
Approach: They evaluate SentProp framework for inducing domain-specific polarities from word embeddings and use it to enhance a general-purpose lexicon for use in the political domain.
Outcome: The proposed framework performs worse than the original lexicon in an out-domain task, showing that the words added and the polarity shifts applied are domain-specific and do not translate well to an out domain setting.
Learning Domain-Sensitive and Sentiment-Aware Word Embeddings (P18-1)

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Challenge: Existing word embeddings cannot produce domain-sensitive embeddables due to domain-specific nature of words.
Approach: They propose a method for learning domain-sensitive and sentiment-aware embeddings that captures sentiment semantics and domain sensitivity of individual words.
Outcome: The proposed method can produce domain-common embeddings and domain-specific embedds.
Is “hot pizza” Positive or Negative? Mining Target-aware Sentiment Lexicons (2021.eacl-main)

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Challenge: Existing sentiment lexicons assume words’ sentiments are invariant within a domain, but this assumption is weak for fine-granularity analyses of text sentiments.
Approach: They propose a "perturb-and-see" method to extract commonsense sentiments from large-scale datasets by binding a word's sentiment to its collocation words instead of domain labels.
Outcome: The proposed framework is able to achieve highly competitive performances on the unsupervised opinion relation extraction task.
Sense and Sentiment (2022.lrec-1)

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Challenge: Existing sentiment lexicons and concept-based sentiment-tagged corpora are not accurate, and it is difficult to map sentiment scores accurately to different languages.
Approach: They examine existing sentiment lexicons and sense-based sentiment-tagged corpora to find out how sense and concept-based semantic relations effect sentiment scores.
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Cross-Domain Sentiment Classification using Semantic Representation (2022.findings-emnlp)

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Challenge: Existing studies on cross-domain sentiment classification ignore the semantic relevance between domains.
Approach: They propose to use Abstract Meaning Representation to help with cross-domain sentiment classification by combining sentence-level AMRs with text-graph interaction models.
Outcome: The proposed model is effective over strong baselines and shows its importance over strong models.

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