A knowledge‐based approach for polarity classification in <scp>T</scp>witter

Montejo-Ráez, Arturo; Martínez‐Cámara, Eugenio; Martín–Valdivia, María Teresa; Ureña‐López, L. Alfonso

doi:10.1002/asi.22984

Cited by 25 publications

(13 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thereafter, compare the calculated score with the minimum subjective score to identify and remove the objective sentence from review, as shown in Lines 9 to 11. The prior studies (Montejo‐Ráez, Martínez‐Cámara, Martín‐Valdivia, & Ureña‐López, ; Paltoglou, Gobron, Skowron, Thelwall, & Thalmann, ) utilized subjective score from 0.4 to 1.0 to determine the subjectivity of sentence; therefore, we take 0.6 as a minimum subjective score for subjective classification. At the end, the remaining subjective sentences of each review are joined by using a dot (.)…”

Section: Proposed Modelmentioning

confidence: 99%

Multiaspect‐based opinion classification model for tourist reviews

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Proposed Modelmentioning

confidence: 99%

Multiaspect‐based opinion classification model for tourist reviews

et al. 2019

View full text Add to dashboard Cite

show abstract

“…As large efforts are put by the researchers in the field of sentiment analysis during the last two decades, it results in several methods and models being proposed. These models based on two approaches: supervised learning [3] [8] and lexicon-based approach [9] [10] [11]. Lexicon is a collection of the predefined word where a polarity score is associated with each word [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

2019

IJITEE

View full text Add to dashboard Cite

Sentiment analysis is an area of natural language processing (NLP) and machine learning where the text is to be categorized into predefined classes i.e. positive and negative. As the field of internet and social media, both are increasing day by day, the product of these two nowadays is having many more feedbacks from the customer than before. Text generated through social media, blogs, post, review on any product, etc. has become the bested suited cases for consumer sentiment, providing a best-suited idea for that particular product. Features are an important source for the classification task as more the features are optimized, the more accurate are results. Therefore, this research paper proposes a hybrid feature selection which is a combination of Particle swarm optimization (PSO) and cuckoo search. Due to the subjective nature of social media reviews, hybrid feature selection technique outperforms the traditional technique. The performance factors like f-measure, recall, precision, and accuracy tested on twitter dataset using Support Vector Machine (SVM) classifier and compared with convolution neural network. Experimental results of this paper on the basis of different parameters show that the proposed work outperforms the existing work

show abstract

“…There are numerous publications about this topic and its applications; see (Liu, 2012;Feldman, 2013) for reviews which summarizes the general sentiment analysis problems and methods, and (Ravi and Ravi, 2015) for a more recent survey which also deals with cross-lingual sentiment analysis. Because tweets are forming a specific text genre there are many papers about the difficulties and method of SA in Twitter (Martínez-Cámara et al, 2012;Montejo-Ráez et al, 2014). In our paper, we comparatively evaluated task-specific techniques and their performance on various text genres and languages.…”

Section: Related Workmentioning

confidence: 99%

A comparative empirical study on social media sentiment analysis over various genres and languages

Hangya

Farkas

2016

Artif Intell Rev

View full text Add to dashboard Cite

People express their opinions about things like products, celebrities and services using social media channels. The analysis of these textual contents for sentiments is a gold mine for marketing experts as well as for research in humanities, thus automatic sentiment analysis is a popular area of applied artificial intelligence. The chief objective of this paper is to investigate automatic sentiment analysis on social media contents over various text sources and languages. The comparative findings of the investigation may give useful insights to artificial intelligence researchers who develop sentiment analyzers for a new textual source. To achieve this, we describe supervised machine learning based systems which perform sentiment analysis and we comparatively evaluate them on seven publicly available English and Hungarian databases, which contain text documents taken from Twitter and product review sites. We discuss the differences among these text genres and languages in terms of document-and target-level sentiment analysis. Keywords Natural language processing • Sentiment analysis • Data mining 1 Introduction Recently, the popularity of social media has increased. People post messages on a variety of topics, like products and political issues and a large amount of user generated data is created in textual form. Several applications have been developed for exploiting the knowledge and information present in user generated content, for example in predicting the results of elections (Sang and Bos, 2012), monitoring brands (Jansen et al, 2009) and disaster management (Varga et al, 2013). In this study, we focus on sentiment analysis (SA) whose task is to assign polarity labels (positive, negative and neutral) to textual elements.

show abstract

A knowledge‐based approach for polarity classification in Twitter

Cited by 25 publications

References 33 publications

Multiaspect‐based opinion classification model for tourist reviews

Multiaspect‐based opinion classification model for tourist reviews

Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

A comparative empirical study on social media sentiment analysis over various genres and languages

Contact Info

Product

Resources

About