• Corpus ID: 17918788

Author-Specific Sentiment Aggregation for Polarity Prediction of Reviews

@inproceedings{Mukherjee2014AuthorSpecificSA,
  title={Author-Specific Sentiment Aggregation for Polarity Prediction of Reviews},
  author={Subhabrata Mukherjee and Sachindra Joshi},
  booktitle={International Conference on Language Resources and Evaluation},
  year={2014}
}
In this work, we propose an author-specific sentiment aggregation model for polarity prediction of reviews using an ontology. We propose an approach to construct a Phrase Annotated Author Specific Sentiment Ontology Tree (PASOT), where the facet nodes are annotated with opinion phrases of the author, used to describe the facets, as well as the author’s preference for the facets. We show that an author-specific aggregation of sentiment over an ontology fares better than a flat classification… 

Figures and Tables from this paper

Aspect Ranking Based on Author Specific Information Aggregation

All the scores which will help to extract the aspects specific to the domain and summarizes the aspects based on its rank in an efficient manner are integrated.

A systematic study on the role of SentiWordNet in opinion mining

A detailed and comprehensive review of the work related to opinion mining using SentiWordNet is provided in a very distinctive way and major challenges and tasks related to lexicon-based approaches towards opinion mining are discussed.

Sentiment Analysis of Reviews

Sentiment Analysis (SA) of reviews refers to the task of analyzing natural language text in forums like Amazon, TripAdvisor, Yelp, IMDB etc. to obtain the writer’s feelings, attitudes, and emotions

Sentiment analysis using deep learning approaches: an overview

This paper reviews deep learning approaches that have been applied to various sentiment analysis tasks and their trends of development, and provides the performance analysis of different deep learning models on a particular dataset at the end of each sentiment analysis task.

360 degree view of cross-domain opinion classification: a survey

An organized survey of SA (also known as opinion mining) containing approaches, datasets, languages, and applications used is presented to support researches to get a greater understanding on emerging trends and state-of-the-art methods to be applied for future exploration.

Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian

Recent evolutions in the e-commerce market have led to an increasing importance attributed by consumers to product reviews made by third parties before proceeding to purchase. The industry, in order

An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian

This work aims to introduce a different approach for Twitter sentiment analysis based on two steps: the tweet jargon is transformed into plain text, exploiting procedures that are language-independent or easily applicable to different languages, and pre-trained models on plain text are easily available in many languages.

References

SHOWING 1-10 OF 31 REFERENCES

Sentiment Aggregation using ConceptNet Ontology

This work analyzes the influence of the hierarchical relationship between the product attributes and their sentiments on the overall review polarity and proposes a weakly supervised system that achieves a reasonable performance improvement over the baseline without requiring any tagged training data.

WikiSent : Weakly Supervised Sentiment Analysis Through Extractive Summarization With Wikipedia

A weakly supervised system for sentiment analysis in the movie review domain, WikiSent, which achieves a better or comparable accuracy to the existing semi-supervised and unsupervised systems in the domain, on the same dataset.

Joint Author Sentiment Topic Model

This work introduces Joint Author Sentiment Topic Model (JAST), a generative process of writing a review by an author, which is the first work in Natural Language Processing to bring all these dimensions together to have an author-specific generative model of a review.

Feature Specific Sentiment Analysis for Product Reviews

A novel approach to identify feature specific expressions of opinion in product reviews with different features and mixed emotions and achieves a high accuracy across all domains and performs at par with state-of-the-art systems despite its data limitations.

Sentiment Learning on Product Reviews via Sentiment Ontology Tree

This paper proposes a novel HL-SOT approach to labeling a product's attributes and their associated sentiments in product reviews by a Hierarchical Learning process with a defined Sentiment Ontology Tree (SOT).

Incorporating author preference in sentiment rating prediction of reviews

This work shows that the inclusion of author preferences in sentiment rating prediction of reviews improves the correlation with ground ratings, over a generic author independent rating prediction model.

A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge

A novel approach to learn from lexical prior knowledge in the form of domain-independent sentiment-laden terms, in conjunction with domain-dependent unlabeled data and a few labeled documents is proposed.

SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS

It is shown that extending the term‐counting method with contextual valence shifters improves the accuracy of the classification, and combining the two methods achieves better results than either method alone.

A Comparative Study of Bayesian Models for Unsupervised Sentiment Detection

A comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentiment-topic (JST) model, and the Reverse-JST model finds that the JST model is more appropriate for joint sentiment topic detection.

Latent aspect rating analysis without aspect keyword supervision

A unified generative model is proposed for LARA, which does not need pre-specified aspect keywords and simultaneously mines 1) latent topical aspects, 2) ratings on each identified aspect, and 3) weights placed on different aspects by a reviewer.