User-guided Cross-domain Sentiment Classification

@inproceedings{Nelakurthi2017UserguidedCS,
  title={User-guided Cross-domain Sentiment Classification},
  author={Arun Reddy Nelakurthi and Hanghang Tong and Ross Maciejewski and Nadya Bliss and Jingrui He},
  booktitle={SDM},
  year={2017}
}
Sentiment analysis has been studied for decades, and it is widely used in many real applications such as media monitoring. In sentiment analysis, when addressing the problem of limited labeled data from the target domain, transfer learning, or domain adaptation, has been successfully applied, which borrows information from a relevant source domain with abundant labeled data to improve the prediction performance in the target domain. The key to transfer learning is how to model the relatedness… 

Figures and Tables from this paper

Transductive Learning with String Kernels for Cross-Domain Text Classification

An algorithm composed of two simple yet effective transductive learning approaches to further improve the results of string kernels in cross-domain settings by adapting string kernels to the test set without using the ground-truth test labels is formally described.

Analyzing the acceptance of the 2018 brazilian presidential election' main candidates based on YouTube comments

This work analyzes the acceptance of the two candidates who disputed in the second shift of the Brazilian presidential election of 2018 (Fernando Haddad and Jair Messias Bolsonaro) since the beginning of the electoral campaign using a dataset of comments collected from YouTube.

Source Free Domain Adaptation Using an Off-the-Shelf Classifier

This paper proposes a generic framework named AOT for adapting the outputs from an off-the-shelf tool to accommodate the changes in the learning task, and aims to maximally boost the performance of the off- the- shelf tool in the target domain, with the help of a limited number of target domain labeled examples.

Curriculum Self-Paced Learning for Cross-Domain Object Detection

Addressing task heterogeneity in social media analytics

References

SHOWING 1-10 OF 21 REFERENCES

Cross-domain sentiment classification via spectral feature alignment

This work develops a general solution to sentiment classification when the authors do not have any labels in a target domain but have some labeled data in a different domain, regarded as source domain and proposes a spectral feature alignment (SFA) algorithm to align domain-specific words from different domains into unified clusters, with the help of domain-independent words as a bridge.

Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach

A deep learning approach is proposed which learns to extract a meaningful representation for each review in an unsupervised fashion and clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products.

SUIT: A Supervised User-Item Based Topic Model for Sentiment Analysis

A new Supervised User-Item based Topic model, called SUIT model, is proposed, which can simultaneously utilize the textual topic and latent user-item factors and shows significant improvement compared with supervised topic models and collaborative filtering methods.

Learning Semantic Representations of Users and Products for Document Level Sentiment Classification

By combining evidence at user-, product and documentlevel in a unified neural framework, the proposed model achieves state-of-the-art performances on IMDB and Yelp datasets1.

Sentiment Analysis and Opinion Mining

  • Lei ZhangB. Liu
  • Computer Science
    Encyclopedia of Machine Learning and Data Mining
  • 2012
This book is a comprehensive introductory and survey text that covers all important topics and the latest developments in the field with over 400 references and is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular.

Topic Correlation Analysis for Cross-Domain Text Classification

A novel approach named Topic Correlation Analysis (TCA), which extracts both the shared and the domain-specific latent features to facilitate effective knowledge transfer, is proposed and the experimental results justify the superiority of the proposed method over the stat-of-the-art baselines.

Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification

This work extends to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline.

Sentiment analysis: capturing favorability using natural language processing

This paper illustrates a sentiment analysis approach to extract sentiments associated with polarities of positive or negative for specific subjects from a document, instead of classifying the whole

Co-Training for Cross-Lingual Sentiment Classification

A cotraining approach is proposed to making use of unlabeled Chinese data for cross-lingual sentiment classification, which leverages an available English corpus for Chinese sentiment classification by using the English corpus as training data.

Graph-based transfer learning

A graph-based transfer learning framework that propagates the label information to both the features irrelevant to the source domain and the unlabeled examples in the target omain via the common features in a principled way is proposed.