Effectiveness of web search results for genre and sentiment classification

Na, Jin‐Cheon; Thet, Tun Thura

doi:10.1177/0165551509104233

Cited by 11 publications

(6 citation statements)

References 21 publications

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“…Nascent studies in the marketing literature reveal some notable use of machine learning approaches to providing decision-support for problems in areas ranging from direct marketing (Cui and Wong, 2004;Ha et al, 2005) to strategic marketing (Martínez-López and Casillas, 2009;Orriols-Puig et al, 2013). Among the various applications, sentiment analysis is a case in point where machine learning applications have led to significant advancements (Dhaoui et al, 2017;Na and Thet, 2009). For example, expert application of machine learning based sentiment analysis provides insights for protecting and developing brands on social media against fans of rival brands (Ilhan et al, 2018), and machine learning models can automatically predict the helpfulness of online reviews in order to aid and enhance customers' online shopping experience (Singh et al, 2017).…”

Section: Machine Learning In Marketing and Content Classificationmentioning

confidence: 99%

Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type

Salminen

Yoganathan

Corporan³

et al. 2019

Journal of Business Research

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As complex data becomes the norm, greater understanding of machine learning (ML) applications is needed for content marketers. Unstructured data, scattered across platforms in multiple forms, impedes performance and user experience. Automated classification offers a solution to this. We compare three state-of-the-art ML techniques for multilabel classification-Random Forest, K-Nearest Neighbor, and Neural Network-to automatically tag and classify online news articles. Neural Network performs the best, yielding an F1 Score of 70% and provides satisfactory cross-platform applicability on the same organisation's YouTube content. The developed model can automatically label 99.6% of the unlabelled website and 96.1% of the unlabelled YouTube content. Thus, we contribute to marketing literature via comparative evaluation of ML models for multilabel content classification, and cross-channel validation for a different type of content. Results suggest that organisations may optimise ML to auto-tag content across various platforms, opening avenues for aggregated analyses of content performance.

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Section: Machine Learning In Marketing and Content Classificationmentioning

confidence: 99%

Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type

Salminen

Yoganathan

Corporan³

et al. 2019

Journal of Business Research

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“…In another study, Rosso [30] examined the use of genre as a document identifier to enhance the effectiveness of web searches. Similarly, Na and Thet [31] examined the effectiveness of web search results for text genre and sentiment classification.…”

Section: Literature Reviewmentioning

confidence: 99%

An ensemble scheme based on language function analysis and feature engineering for text genre classification

Onan

2016

Journal of Information Science

258

119

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Text genre classification is the process of identifying functional characteristics of text documents. The immense quantity of text documents available on the web can be properly filtered, organised and retrieved with the use of text genre classification, which may have potential use on several other tasks of natural language processing and information retrieval. Genre may refer to several aspects of text documents, such as function and purpose. The language function analysis (LFA) concentrates on single aspect of genres and it aims to classify text documents into three abstract classes, such as expressive, appellative and informative. Text genre classification is typically performed by supervised machine learning algorithms. The extraction of an efficient feature set to represent text documents is an essential task for building a robust classification scheme with high predictive performance. In addition, ensemble learning, which combines the outputs of individual classifiers to obtain a robust classification scheme, is a promising research field in machine learning research. In this regard, this article presents an extensive comparative analysis of different feature engineering schemes (such as features used in authorship attribution, linguistic features, character n-grams, part of speech n-grams and the frequency of the most discriminative words) and five different base learners (Naïve Bayes, support vector machines, logistic regression, k-nearest neighbour and Random Forest) in conjunction with ensemble learning methods (such as Boosting, Bagging and Random Subspace). Based on the empirical analysis, an ensemble classification scheme is presented, which integrates Random Subspace ensemble of Random Forest with four types of features (features used in authorship attribution, character n-grams, part of speech n-grams and the frequency of the most discriminative words). For LFA corpus, the highest average predictive performance obtained by the proposed scheme is 94.43%.

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“…Qiu et al [10] presented a framework where extraction of sentiment words and polarity assignment was done using a propagation approach. Many researchers focused on document level analysis in order to determining the sentiment orientation of a document such as work presented in [11]. However, these document analysis approaches are not as effective as performing the in-depth sentiment analysis of text reviews.…”

Section: Literature Reviewmentioning

confidence: 99%

SentiView: A visual sentiment analysis framework

Khan

Qamar

Javed

2014

International Conference on Information Society (I-Society 2014)

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In the past few years, micro-blogging platforms, such as twitter, are becoming most popular online social networks. Different opinions and news can be shared about various aspects and occasions using these micro-blogging platforms. Twitter is therefore considered as a rich source of data and it can be used for different text analysis and decision making tasks. The main focus of sentiment analysis is about text classification into positive/negative/neutral feelings based on the polarity of text. The opinions and thoughts on twitter feeds can be expressed in any language. Previous techniques have some limitations in the field of sentiment analysis such as low accuracy, sarcasm, and incorrect classification of tweets. The proposed research focuses on the existing difficulties and complications and presents a framework, for the sentiment detection of twitter feeds, which results in high accuracy and real time performance. There are various pre-processing steps that are applied on twitter feeds to refine them before feeding for sentiment classification. The pre-processing removes slangs and abbreviations with complete words. Three different classification techniques are then used; emoticon analysis, Bag of words and SentiWordNet. The experimental evaluation confirms that the proposed algorithm dynamically increases the precision, recall, f-measure and most importantly accuracy when compared with other similar techniques.

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Effectiveness of web search results for genre and sentiment classification

Cited by 11 publications

References 21 publications

Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type

Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type

An ensemble scheme based on language function analysis and feature engineering for text genre classification

SentiView: A visual sentiment analysis framework

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