ABSA Toolkit: An Open Source Tool for Aspect Based Sentiment Analysis

Nasim, Zarmeen; Haider, Sajjad

doi:10.1142/s0218213017500233

Cited by 14 publications

(10 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, 30% of the training set was selected as the validation set. This study used the method proposed by Poria et al (2016) to extract the aspect term, as it has been proved to have better performance than others such as LSTM, random forest or SVM (Nasim and Haider, 2017). The performance metrics were satisfactory (F 1 score = 0.85, Precision = 0.87, Recall = 0.83).…”

Section: Ijchm 332mentioning

confidence: 98%

Online review helpfulness: the moderating effects of review comprehensiveness

2021

IJCHM

View full text Add to dashboard Cite

Purpose Consumers’ evaluation of online review helpfulness has been widely examined. The extant literature suggests that the attributes of review content (e.g. review length and extremity) influence review helpfulness. However, review length cannot fully reflect the richness of the review content. Anchoring on information diagnosticity and extremity bias, this study aims to explore the effect of review comprehensiveness on its helpfulness. Design/methodology/approach Field observations were obtained from 11,812 online restaurant reviews on a popular restaurant review platform. A controlled experiment was conducted to further delineate the effect of review comprehensiveness. Findings Review comprehensiveness moderates the effects of review length and an extremely negative review on helpfulness. It also confirms that for reviews of the same length, one covering more aspects is perceived by consumers as more helpful. Practical implications Different aspects of information in a review can efficiently assist decision-making. The results suggest that review platforms can better design their interface by providing separate areas for different product aspects. The platform can then receive more comprehensive and helpful reviews and increase the diagnosticity of these. Originality/value The study enriches the literature by introducing review comprehensiveness and examining the joint effects of review length and comprehensiveness on helpfulness. It also contributes to the literature by indicating how to reduce the effect of review extremity.

show abstract

Section: Ijchm 332mentioning

confidence: 98%

Online review helpfulness: the moderating effects of review comprehensiveness

2021

IJCHM

View full text Add to dashboard Cite

show abstract

“…In other words, supervised techniques use algorithms that require training. Examples of the supervised techniques are, Conditional Random Field (CRF) [46], [47] for explicit aspects, Hierarchy [48] for implicit aspects, and long short term memory [49] for a combined implicit and explicit aspects.…”

Section: Semi-supervised Aspect Extraction Techniquesmentioning

confidence: 99%

“…Despite its promising nature, CRF possesses some limitations which were highlighted according to the articles. Nasim and Haider [46] proposed an open-source oriented framework for summarizing and analyzing customer reviews. This framework contributed in circumventing document-level SA using CRF, by effectively tracking people's sentiment towards different products.…”

Section: ) Explicit Aspect Extraction Techniques With Their Associatmentioning

confidence: 99%

A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

et al. 2020

View full text Add to dashboard Cite

Aspect-based sentiment analysis (ABSA) is currently among the most vigorous areas in natural language processing (NLP). Individuals, private and government institutions are increasingly using media sources for decision making. In the last decade, aspect extraction has been the most essential phase of sentiment analysis (SA) to conduct an abridged sentiment classification. However, previous studies on sentiment analysis mostly focused on explicit aspects extraction with limited work on implicit aspects. To the best of our knowledge, this is the first systematic review that covers implicit, explicit, and the combination of both implicit and explicit aspect extractions. Therefore, this systematic review has been conducted to, 1) identify techniques used for extracting implicit, explicit, or both implicit and explicit aspects; 2) analyze the various evaluation metrics, data domains, and languages involved in the implicit and explicit aspect extraction in sentiment analysis from years 2008 to 2019; 3) identify the key challenges associated with the techniques based on the result of a comprehensive comparative analysis; and finally, 4) highlight the feasible opportunities for future research directions. This review can be used to assist novice and prominent researchers to understand the concept of both implicit and explicit aspect extractions in aspect-based sentiment analysis domain. INDEX TERMS aspect-based sentiment analysis, aspect extraction, explicit aspect, feature extraction, implicit aspect.

show abstract

“…In addition to method/approach and model, some aspect-based researchers presents a framework as the main contribution, some of these studies includes: [59,92,94,96,109,110,113,123]. Although their names were used interchangeably, tool/system are presented as the major contribution of some papers as shown by: [58,79,122,129,131]. For example, [131] proposed an open-source tool titled ABSA Toolkit that primarily analysed sentiments associated to aspects, while [79] presents an aspect-based hierarchical system which is considered as a fine-grained sentiment analysis system in edge computing.…”

Section: What Are the Research Facet Used In Eae And What Contribumentioning

confidence: 99%

“…Although their names were used interchangeably, tool/system are presented as the major contribution of some papers as shown by: [58,79,122,129,131]. For example, [131] proposed an open-source tool titled ABSA Toolkit that primarily analysed sentiments associated to aspects, while [79] presents an aspect-based hierarchical system which is considered as a fine-grained sentiment analysis system in edge computing. Some authors tend to unveil architecture for EAE among which are [43,66].…”

Section: What Are the Research Facet Used In Eae And What Contribumentioning

confidence: 99%

A Systematic Mapping Study of the Empirical Explicit Aspect Extractions in Sentiment Analysis

2020

View full text Add to dashboard Cite

Aspect-based sentiment analysis (ABSA) is described as one of the most vibrant research areas over the last decade. However, due to the exponential increase in aspect-based sentiment researches, there is a massive interest in advanced explicit aspect extraction (EAE) techniques. This interest brings about a huge amount of literature in the EAE domain. This study aims to investigate and identify the existing approaches, techniques, types of research, quantity of publications, publication trends and demographics shaping the EAE research domain in the last decade (2009-2019). Accordingly, an evidence-based systematic methodology was adopted to effectively capture all the relevant studies. The main findings revealed that, 1) there is considerable and continuous rise of EAE research activities around different parts of the globe in the last five years, particularly Asia, Middle-East, and European countries; 2) EAE research has been very limited among African countries which need to be addressed due its role on business intelligence as well as semantic values; 3) three research facets were highlighted based on this study, i.e. solution research, validation research, and evaluation research, in which solution research gets the highest attention; and finally 4) the EAE challenges, as well as feasible future recommendations, were highlighted in this study.

show abstract

ABSA Toolkit: An Open Source Tool for Aspect Based Sentiment Analysis

Cited by 14 publications

References 2 publications

Online review helpfulness: the moderating effects of review comprehensiveness

Online review helpfulness: the moderating effects of review comprehensiveness

A Systematic Review on Implicit and Explicit Aspect Extraction in Sentiment Analysis

A Systematic Mapping Study of the Empirical Explicit Aspect Extractions in Sentiment Analysis

Contact Info

Product

Resources

About