| 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. |
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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. |
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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Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon (2021.findings-acl)
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| Challenge: | Existing studies on sentiment lexicons have focused on domain-dependent sentiment words. |
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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. |
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Inducing Target-Specific Latent Structures for Aspect Sentiment Classification (2020.emnlp-main)
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| Challenge: | Aspect-level sentiment analysis aims to classify the sentiment polarity of an aspect or a target in a comment . graph convolutional networks can be used to classifice aspect terms in syllables . |
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A Helping Hand: Transfer Learning for Deep Sentiment Analysis (P18-1)
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| Challenge: | Existing deep neural models for sentiment polarity classification require large amounts of training data. |
| Approach: | They propose to feed generic cues into the training process of deep convolutional neural networks for sentiment analysis. |
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Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification (D18-1)
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| Challenge: | Existing sentiment lexicons do not handle word sense and the concept of semantic compositionality is non-existent in simple lexiconic approaches. |
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Lingmotif-lex: a Wide-coverage, State-of-the-art Lexicon for Sentiment Analysis (L18-1)
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| Challenge: | Sentiment Analysis is a subtask of Natural Language Processing. |
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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. |
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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. |
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