SIEL: Aspect Based Sentiment Analysis in Reviews

Guha, Satarupa; Joshi, Aditya; Varma, Vasudeva

doi:10.18653/v1/s15-2129

Cited by 18 publications

(9 citation statements)

References 20 publications

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“…The simplest method of determining the context for such an explicit aspect is by employing a window around the target phrase and developing an input representation based only on the words in the window. For example, Guha et al [77] only consider the aspect itself, the three words to the left of the aspect, and the three words to the right of the aspect. However, such methods based on the physical proximity may not be optimal since the words expressing the sentiment may be far removed from the aspect.…”

Section: Contextmentioning

confidence: 99%

A Survey on Aspect-Based Sentiment Classification

Brauwers

Frăsincar

2022

ACM Comput. Surv.

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With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.

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Section: Contextmentioning

confidence: 99%

A Survey on Aspect-Based Sentiment Classification

Brauwers

Frăsincar

2022

ACM Comput. Surv.

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“…Therefore, employing these in the sentiment classification task yields a better performance than the "bag of words" model. An approach (Guha, Joshi, and Varma 2015) relies on the presence or absence of some words when performing aspect-based sentiment analysis. For example, the presence of wh-words and conditional words, such as "what" and "if," are mostly characteristic of sentences and reviews of negative polarity.…”

Section: Related Workmentioning

confidence: 99%

Sentiment analysis in Turkish: Supervised, semi-supervised, and unsupervised techniques

Aydın

Güngör

2020

Nat. Lang. Eng.

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Although many studies on sentiment analysis have been carried out for widely spoken languages, this topic is still immature for Turkish. Most of the works in this language focus on supervised models, which necessitate comprehensive annotated corpora. There are a few unsupervised methods, and they utilize sentiment lexicons either built by translating from English lexicons or created based on corpora. This results in improper word polarities as the language and domain characteristics are ignored. In this paper, we develop unsupervised (domain-independent) and semi-supervised (domain-specific) methods for Turkish, which are based on a set of antonym word pairs as seeds. We make a comprehensive analysis of supervised methods under several feature weighting schemes. We then form ensemble of supervised classifiers and also combine the unsupervised and supervised methods. Since Turkish is an agglutinative language, we perform morphological analysis and use different word forms. The methods developed were tested on two datasets having different styles in Turkish and also on datasets in English to show the portability of the approaches across languages. We observed that the combination of the unsupervised and supervised approaches outperforms the other methods, and we obtained a significant improvement over the state-of-the-art results for both Turkish and English.

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“…Aspect based polarity disambiguation is explained in [19] [20], where the sentiment words are high-quality and yielding relatively high precision over a set of sentiment features, the recall is slightly low. Similarly by considering the aspect, in [21] a novel aspect based sentiment analysis methodology based on CRF and lexical features are used.…”

Section: Related Workmentioning

confidence: 99%

BbNCPD-Bayesian belief Network Based Contextual Polarity Disambiguation in Sentiment Analysis

Singh

Kumari

2020

Preprint

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Sentiment analysis has enormous contributions and vivid utility in many areas of natural language processing, e-commerce and social network analysis. Although there are several methods designated for this task but major thrust of this domain to develop machine learning based polarity classification of user generated text, review and post. It is even tougher to classify the text containing multi-polarity words. In the meantime, several studies have discussed and focused on contextual information of word in Natural Language Processing. But, due to lack of methodologies, it becomes challenging for contextual polarity disambiguation because no evidence is available which proves that whether the sentiment expressed by the context is correctly identified. Therefore, in this work an effort to compute the quantified amount of polarity of terms/words in a given text, based on its local as well as global contexts. The proposed work contains two major parts, firstly it extracts the local and global features from semantic dependency parsing and co-occurrences with which association of the designated word to be disambiguated with previous polarities. Then, the second part computes a Bayesian belief network of these features along with their conditional probabilities which are used to disambiguate and compute sentiments. The performance of the proposed scheme is exhaustively tested on standard datasets and results clearly show the efficacy of the proposed scheme.

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SIEL: Aspect Based Sentiment Analysis in Reviews

Cited by 18 publications

References 20 publications

A Survey on Aspect-Based Sentiment Classification

A Survey on Aspect-Based Sentiment Classification

Sentiment analysis in Turkish: Supervised, semi-supervised, and unsupervised techniques

BbNCPD-Bayesian belief Network Based Contextual Polarity Disambiguation in Sentiment Analysis

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