Opinion Mining
57 papers with code • 1 benchmarks • 10 datasets
Identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral (Source: Oxford Languages)
Image Source: Deep learning for sentiment analysis: A survey
Benchmarks
These leaderboards are used to track progress in Opinion Mining
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Datasets
IMDb Movie Reviews
MPQA Opinion Corpus
UR-FUNNY
MOS Dataset
Most implemented papers
Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling
zhangmeishan/SRL4ORL
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NAACL 2019
Opinion role labeling (ORL) is an important task for fine-grained opinion mining, which identifies important opinion arguments such as holder and target for a given opinion trigger.
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning
Aspect category detection is an essential task for sentiment analysis and opinion mining.
Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training
gkaramanolakis/ISWD
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IJCNLP 2019
In this work, we consider weakly supervised approaches for training aspect classifiers that only require the user to provide a small set of seed words (i. e., weakly positive indicators) for the aspects of interest.
Snippext: Semi-supervised Opinion Mining with Augmented Data
A novelty of Snippext is its clever use of a two-prong approach to achieve state-of-the-art (SOTA) performance with little labeled training data through: (1) data augmentation to automatically generate more labeled training data from existing ones, and (2) a semi-supervised learning technique to leverage the massive amount of unlabeled data in addition to the (limited amount of) labeled data.
Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance in Twitter Conversations
Moreover, the existing datasets do not distinguish between the different types of conversations on social media (e. g., replying vs. quoting on Twitter).
Learning Opinion Dynamics From Social Traces
In this work we propose an inference mechanism for fitting a generative, agent-like model of opinion dynamics to real-world social traces.
Subjective Question Answering: Deciphering the inner workings of Transformers in the realm of subjectivity
Hence, to answer these subjective questions, a learner must extract opinions and process sentiment for various domains, and additionally, align the knowledge extracted from a paragraph with the natural language utterances in the corresponding question, which together enhance the difficulty of a QA task.
Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction
chenshaowei57/SDRN
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ACL 2020
The opinion entity extraction unit and the relation detection unit are developed as two channels to extract opinion entities and relations simultaneously.
BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text
keshav22bansal/BAKSA_IITK
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SEMEVAL 2020
Sentiment Analysis of code-mixed text has diversified applications in opinion mining ranging from tagging user reviews to identifying social or political sentiments of a sub-population.
Public Sentiment Toward Solar Energy: Opinion Mining of Twitter Using a Transformer-Based Language Model
Public acceptance and support for renewable energy are important determinants of renewable energy policies and market conditions.
