<scp>T‐SAF</scp>: Twitter sentiment analysis framework using a hybrid classification scheme

Asghar, Muhammad Zubair; Kundi, Fazal Masud; Ahmad, Shakeel; Khan, Amin; Khan, Furqan

doi:10.1111/exsy.12233

Cited by 87 publications

(37 citation statements)

References 21 publications

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“…The results showed that computing the sentiment score of slang expressions lead to an improved accuracy in the sentiment classification of tweets. In terms of studying the impact of SC, the framework proposed by Asghar et al [55] achieved an F-score of 0.92 compared to 0.85 obtained by Masud et al [49]. The results also showed that the presence of emoticons in Twitter sentiment increased classification accuracy from 79% to 85%.…”

Section: Twitter Sentiment Analysis Using Hybrid Methodsmentioning

confidence: 88%

“…Asghar et al [55] proposed a hybrid Twitter sentiment system that incorporated four classifiers: a slang classifier (SC), an emoticon classifier (EC), a general purpose sentiment classifier (GPSC), and an improved domain specific classifier (IDSC). Their technique was inspired by the previous studies by Khan et al [53] and Asghar et al [50], which classified tweets using multiple supervised and unsupervised classification models.…”

Section: Twitter Sentiment Analysis Using Hybrid Methodsmentioning

confidence: 99%

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A Study on Sentiment Analysis Techniques of Twitter Data

Alsaeedi¹,

Khan²

2019

ijacsa

207

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The entire world is transforming quickly under the present innovations. The Internet has become a basic requirement for everybody with the Web being utilized in every field. With the rapid increase in social network applications, people are using these platforms to voice them their opinions with regard to daily issues. Gathering and analyzing peoples' reactions toward buying a product, public services, and so on are vital. Sentiment analysis (or opinion mining) is a common dialogue preparing task that aims to discover the sentiments behind opinions in texts on varying subjects. In recent years, researchers in the field of sentiment analysis have been concerned with analyzing opinions on different topics such as movies, commercial products, and daily societal issues. Twitter is an enormously popular microblog on which clients may voice their opinions. Opinion investigation of Twitter data is a field that has been given much attention over the last decade and involves dissecting "tweets" (comments) and the content of these expressions. As such, this paper explores the various sentiment analysis applied to Twitter data and their outcomes.

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Section: Twitter Sentiment Analysis Using Hybrid Methodsmentioning

confidence: 88%

Section: Twitter Sentiment Analysis Using Hybrid Methodsmentioning

confidence: 99%

A Study on Sentiment Analysis Techniques of Twitter Data

Alsaeedi¹,

Khan²

2019

ijacsa

207

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“…Opinions in user‐generated reviews play an important role in influencing prospective tourists' choices (Asghar, Kundi, Ahmad, Khan, & Khan, ). The social media websites (e.g., Facebook, TripAdvisor, and Expedia) that feature user‐generated reviews have led to an abundance of opinions that are often too numerous for a tourist to read and interpret.…”

Section: Introductionmentioning

confidence: 99%

Multiaspect‐based opinion classification model for tourist reviews

et al. 2019

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Tourist reviews on social media websites reflect the tourist's opinions concerning various aspects of a tourist place or service (e.g., “comfortable room” and “terrible service” in hotel reviews). Extracting these aspects from reviews is a challenging task in opinion mining. Therefore, aspect‐based opinion mining has emerged as a new area of social review mining. Existing approaches in this area focus on extracting explicit aspects and classification of opinions around these aspects. However, the implicit and coreferential aspects during aspect extraction are often neglected, and the classification of multiaspect opinions is relatively less emphasized in prior art. In this paper, we propose a model, namely, “enhanced multiaspect‐based opinion classification” that addresses existing challenges by automatically extracting both explicit and implicit aspects and classifying the multiaspect opinions. In this model, first, a probabilistic co‐occurrence‐based method is proposed that utilizes the co‐occurrence between aspects and sentiment words to identify the coreferential aspects and merge them into groups. Second, an implicit aspect extraction method is proposed that associates the sentiment words with suitable aspects to build an aspect‐sentiment hierarchy. Third, a multiaspect opinion classification approach is proposed that employs multilabel classification algorithms to classify opinions into different polarity classes. The effectiveness of the proposed model is evaluated by conducting experiments on benchmark and real‐world datasets. The experimental results revealed the supremacy of multilabel classifiers by achieving 90% accuracy per label on classification when extracting 87% domain‐relevant aspects. A state‐of‐the‐art performance comparison is conducted that also verifies the advantages of the proposed model.

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“…• The challenges "negation handling", "modifiers handling", "emoticons detection", and "domain specific words handling" have been considered. Asghar et al (2018) • As an extension to the work in Asghar et al (2017), the challenge "slang classification" has been considered. Bahri et al (2018) • Sentiment analysis has been applied on sentencelevel.…”

Section: Salasmentioning

confidence: 99%

An enhanced feature‐based sentiment analysis approach

Saeed

Helal

Badr

et al. 2019

WIREs Data Min & Knowl

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In the last few years, online reviews where individuals express their thoughts, interests, experiences, and opinions have broadly spread over the internet. Sentiment analysis has evolved to analyze these online reviews and provide valuable insights for both individuals and organizations that may help them in making decisions. Unfortunately the performance of sentiment analysis process is affected by the nature of online reviews' content that may contain emoticons and negation words. Moreover, spam reviews have been written for the purpose of deceiving others. Therefore, there is a need to develop an approach that considers these issues. In this paper, an enhanced approach for sentiment analysis is proposed which aims to enhance the performance of classifying reviews based on their features and assigning accurate sentiment score to features. This enhanced approach is achieved by handling negation, detecting emoticons, and detecting spam reviews using a combination of different types of properties which leads to achieving better predictive performance. The proposed approach has been verified against three datasets of different sizes. The results indicate that the proposed approach achieves a maximum accuracy of about 99.06% in detecting spam reviews and a maximum accuracy of about 97.13% in classifying reviews. This article is categorized under: Algorithmic Development > Text Mining Technologies > Classification Technologies > Machine Learning

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T‐SAF: Twitter sentiment analysis framework using a hybrid classification scheme

Cited by 87 publications

References 21 publications

A Study on Sentiment Analysis Techniques of Twitter Data

A Study on Sentiment Analysis Techniques of Twitter Data

Multiaspect‐based opinion classification model for tourist reviews

An enhanced feature‐based sentiment analysis approach

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