Consensus vote models for detecting and filtering neutrality in sentiment analysis

Valdivia, Ana; Luzón, M. Victoria; Wang, Zhaoxia; Herrera, Francisco

doi:10.1016/j.inffus.2018.03.007

Cited by 102 publications

(36 citation statements)

References 33 publications

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“…In order to define this representative linguistic expression, the sentiment of each review needs to be determined. Some sentiment analysis methods consider degrees of positivity [3], while others focus on binary classification of positive and negative sentiment [32]. Since neutral sentiment is between positive and negative sentiment, it may be viewed as potential noise [32,30].…”

Section: Defining Hfltss From Text Reviews and Ratingsmentioning

confidence: 99%

“…Some sentiment analysis methods consider degrees of positivity [3], while others focus on binary classification of positive and negative sentiment [32]. Since neutral sentiment is between positive and negative sentiment, it may be viewed as potential noise [32,30]. Therefore, Valdivia et al [32] proposed to detect and filter out neutral opinions to improve sentiment classification.…”

Section: Defining Hfltss From Text Reviews and Ratingsmentioning

confidence: 99%

“…Since neutral sentiment is between positive and negative sentiment, it may be viewed as potential noise [32,30]. Therefore, Valdivia et al [32] proposed to detect and filter out neutral opinions to improve sentiment classification. Although the technique improves classification performance, our methodology does not require precise classification but rather the essence of positive and/or negative sentiment inherent in the text reviews to be reflected in the linguistic expression.…”

Section: Defining Hfltss From Text Reviews and Ratingsmentioning

confidence: 99%

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Fusing hotel ratings and reviews with hesitant terms and consensus measures

Nguyen

Montserrat-Adell

Agell

et al. 2020

Neural Comput & Applic

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People have come to refer to reviews for valuable information on products before making a purchase. Digesting relevant opinions regarding a product by reading all the reviews is challenging. An automated methodology which aggregates opinions across all the reviews for a single product to help differentiate any two products having the same overall rating is defined. In order to facilitate this process, rating values, which capture the overall satisfaction, and written reviews, which contain the sentiment of the experience with a product, are fused together. In this manner, each reviewer's opinion is expressed as an interval rating by means of hesitant fuzzy linguistic term sets. These new expressions of opinion are then aggregated and expressed in terms of a central opinion and degree of consensus representing the agreement among the sentiment of the reviewers for an individual product. A real case example based on 2,506 TripAdvisor reviews of hotels in Rome during 2017 is provided. The efficiency of the proposed methodology when discriminating between two hotels is compared with the TripAdvisor rating and median of reviews. The proposed methodology obtains significant differentiation between product rankings.

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Section: Defining Hfltss From Text Reviews and Ratingsmentioning

confidence: 99%

Section: Defining Hfltss From Text Reviews and Ratingsmentioning

confidence: 99%

Section: Defining Hfltss From Text Reviews and Ratingsmentioning

confidence: 99%

See 1 more Smart Citation

Fusing hotel ratings and reviews with hesitant terms and consensus measures

Nguyen

Montserrat-Adell

Agell

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…In the sentiment analysis domain, various systems have been proposed to filter out noise data, such as a proximity function [41], multiple noise filtering system [42], and manual filtering [43,44]. However, preprocessing steps are required in proximity function-based filtering, which is a time consuming and complex task.…”

Section: Fuzzy Ontology and Lstm-based Text Miningmentioning

confidence: 99%

Fuzzy Ontology and LSTM-Based Text Mining: A Transportation Network Monitoring System for Assisting Travel

Ali

El–Sappagh

Kwak

2019

Sensors

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Intelligent Transportation Systems (ITSs) utilize a sensor network-based system to gather and interpret traffic information. In addition, mobility users utilize mobile applications to collect transport information for safe traveling. However, these types of information are not sufficient to examine all aspects of the transportation networks. Therefore, both ITSs and mobility users need a smart approach and social media data, which can help ITSs examine transport services, support traffic and control management, and help mobility users travel safely. People utilize social networks to share their thoughts and opinions regarding transportation, which are useful for ITSs and travelers. However, user-generated text on social media is short in length, unstructured, and covers a broad range of dynamic topics. The application of recent Machine Learning (ML) approach is inefficient for extracting relevant features from unstructured data, detecting word polarity of features, and classifying the sentiment of features correctly. In addition, ML classifiers consistently miss the semantic feature of the word meaning. A novel fuzzy ontology-based semantic knowledge with Word2vec model is proposed to improve the task of transportation features extraction and text classification using the Bi-directional Long Short-Term Memory (Bi-LSTM) approach. The proposed fuzzy ontology describes semantic knowledge about entities and features and their relation in the transportation domain. Fuzzy ontology and smart methodology are developed in Web Ontology Language and Java, respectively. By utilizing word embedding with fuzzy ontology as a representation of text, Bi-LSTM shows satisfactory improvement in both the extraction of features and the classification of the unstructured text of social media.

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“…This operator gives different weights to every variable in the aggregation . The OWA operator has been used in other fuzzy linguistic models . Finally, the decision‐making process of every agent consists of evaluating every product assessment and selecting one of them.…”

Section: Introductionmentioning

confidence: 99%

Modeling agent‐based consumers decision‐making with 2‐tuple fuzzy linguistic perceptions

et al. 2019

Self Cite

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Understanding consumer behaviors and how consumers react to marketing campaigns and viral word-of-mouth processes is crucial for marketers. Classical approaches try to infer this information from a global top-down perspective. However, a more suitable and natural approach is to model consumer behaviors in a heterogeneous and decentralized bottom-up approach. In this case, each virtual consumer has her own mental state and decision-making strategies to simulate her purchase decisions. The system of virtual consumers generates the global sales and a marketer can understand the rules that govern the market. A well-known paradigm to model these systems is agent-based modeling (ABM). In this manuscript we present an ABM where the brand preferences of the consumer agents are modeled using 2-tuple fuzzy linguistic variables. These variables represent the perceptions these consumers have on the different aspects or drivers every product available in the market has (e.g., price or quality). The product selection process of the agents is based on those perceptions and a utility maximization rule. This rule requires a fuzzy aggregation of the fuzzy linguistic perceptions about the products. Our proposal employs an ordered weighted average (OWA) to aggregate them. Our experiments | 297

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Consensus vote models for detecting and filtering neutrality in sentiment analysis

Cited by 102 publications

References 33 publications

Fusing hotel ratings and reviews with hesitant terms and consensus measures

Fusing hotel ratings and reviews with hesitant terms and consensus measures

Fuzzy Ontology and LSTM-Based Text Mining: A Transportation Network Monitoring System for Assisting Travel

Modeling agent‐based consumers decision‐making with 2‐tuple fuzzy linguistic perceptions

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