Orith Toledo-Ronen, Roy Bar-Haim, Alon Halfon, Charles Jochim, Amir Menczel, Ranit Aharonov, Noam Slonim
| Challenge: | Sentiment composition is a fundamental problem in sentiment analysis. |
| Approach: | They propose a method for learning sentiment composition from a large, unlabeled corpus . they automatically generate large sentiment lexicons of bigrams and unigrams . |
| Outcome: | The proposed approach is validated through manual annotation and sentiment classification experiments with phrase-level and sentence-level benchmarks. |
Similar Papers
Domain-Specific Sentiment Lexicons Induced from Labeled Documents (2020.coling-main)
Copied to clipboard
| 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. |
Utilizing Large Twitter Corpora to Create Sentiment Lexica (L18-1)
Copied to clipboard
| Challenge: | Existing sentiment analysis systems only use word unigrams and bigrams, but lexicons using sentiment lexica are effective. |
| Approach: | They describe an automatic Twitter sentiment lexicon creator and a lexico-based sentiment analysis system. |
| Outcome: | The proposed system outperforms a manually annotated system in a comparison experiment. |
Learning and Evaluating Emotion Lexicons for 91 Languages (2020.acl-main)
Copied to clipboard
| Challenge: | Emotion lexicons describe the affective meaning of words but are limited in coverage for most languages. |
| Approach: | They propose a method for creating arbitrarily large emotion lexicons for any target language. |
| Outcome: | The proposed method exceeds human reliability for some languages and variables. |
Latent Variable Sentiment Grammar (P19-1)
Copied to clipboard
| Challenge: | Existing neural models do not explicitly model sentiment composition, which requires to encode sentiment class labels. |
| Approach: | They propose a sentiment grammar that captures sentiment subtype expressions by latent variables and Gaussian mixture vectors. |
| Outcome: | The proposed model outperforms vanilla neural encoders on the Stanford Sentiment Treebank benchmark. |
Semantic Simplification for Sentiment Classification (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on document-level sentiment classification has shown that the sentiment in the original text is often hard to capture . previous studies focus on predicting the overall sentiment from original text using statistical or neural models, but these methods either heavily rely on human knowledge or suffer from the complex structure of the text. |
| Approach: | They propose a document-level sentiment classification model that enhances the original text with a simplified clause to intensify its sentiment. |
| Outcome: | Empirical studies show that the proposed model over strong baselines is effective over several strong baseline models. |
Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | generative approach to multilingual sentiment classification is based on syntactic and lexical knowledge and requires retraining and tuning. |
| Approach: | They propose to use a sentiment extractor supported by syntactic and lexical resources to enhance multilingual sentiment classification without retraining LLMs. |
| Outcome: | The proposed approach reduces the multilingual sentiment classification error by 33 points and performs well even for nongenerative tasks such as topic classification and sentiment polarity judgment. |
Classifier-based Polarity Propagation in a WordNet (L18-1)
Copied to clipboard
| 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 . |
| Outcome: | The proposed method allows for the construction of a more reliable sentiment lexicon . the proposed method is partially automated, but it's performance drops in cross-domain applications . |
Direct parsing to sentiment graphs (2022.acl-short)
Copied to clipboard
| Challenge: | Existing methods for structured sentiment analysis (SSA) focus on subcomponents of sentiment graphs without explicitly expressing their relations or the polarity. |
| Approach: | They propose a graph-based semantic parser which directly predicts sentiment graphs from text without reliance on lossy conversions to intermediate dependency representations. |
| Outcome: | The proposed model performs on 4 out of 5 standard benchmark sets and compares with dependency-based models on the more structurally complex datasets. |
Odi et Amo. Creating, Evaluating and Extending Sentiment Lexicons for Latin. (2020.lrec-1)
Copied to clipboard
| Challenge: | a new paper aims to provide sentiment analysis tools for ancient languages . the current sentiment analysis resources only cover modern languages based on textual typologies . |
| Approach: | They propose to use manually-curated Latin lexicons to evaluate sentiment analysis tools . they propose a gold standard and a silver standard for evaluating lexical items . |
| Outcome: | The proposed lexicons are evaluated using a gold standard and a silver standard for sentiment analysis. |
Non-Compositionality in Sentiment: New Data and Analyses (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Many studies on sentiment analysis focus on the fact that sentiment computations are compositional . linguistic utterances often do not adhere to strict patterns and can be surprising when looking at the individual words involved. |
| Approach: | They propose a method for obtaining non-compositionality ratings for phrases with respect to their sentiment . they also propose evaluating computational models for sentiment analysis using the rating resource . |
| Outcome: | The proposed method enables non-compositional ratings for phrases with respect to their sentiment . the results are compared with a new resource of ratings for 259 phrases . |