Sentiment classification of online reviews to travel destinations by supervised machine learning approaches

Ye, Qiang; Zhang, Ziqiong; Law, Rob

doi:10.1016/j.eswa.2008.07.035

Cited by 539 publications

(260 citation statements)

References 14 publications

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“…One example in our project is the case where one's research may have developed and applied some metric for the measure of some attribute of a message (Blei and McAuliffe 2010). The classic example is training on 1-5 star movie reviews and then guessing star rating based on the text of the review (Ye, Zhang et al 2009). Another example of such a metric might be an evaluation of the trust shared between users.…”

Section: Supervised Ldamentioning

confidence: 99%

Modeling virtual organizations with Latent Dirichlet Allocation: A case for natural language processing

Groß

Murthy

2014

Neural Networks

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This paper explores a variety of methods for applying the Latent Dirichlet Allocation (LDA) automated topic modeling algorithm to the modeling of the structure and behavior of virtual organizations found within modern social media and social networking environments. As the field of Big Data reveals, an increase in the scale of social data available presents new challenges which are not tackled by merely scaling up hardware and software. Rather, they necessitate new methods and, indeed, new areas of expertise. Natural language processing provides one such method. This paper applies LDA to the study of scientific virtual organizations whose members employ social technologies. Because of the vast data footprint in these virtual platforms, we found that natural language processing was needed to 'unlock' and render visible latent, previously unseen conversational connections across large textual corpora (spanning profiles, discussion threads, forums, and other social media incarnations). We introduce variants of LDA and ultimately make the argument that natural language processing is a critical interdisciplinary methodology to make better sense of social 'Big Data' and we were able to successfully model nested discussion topics from forums and blog posts using LDA. Importantly, we found that LDA can move us beyond the state-of-the-art in conventional Social Network Analysis techniques.

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Section: Supervised Ldamentioning

confidence: 99%

Modeling virtual organizations with Latent Dirichlet Allocation: A case for natural language processing

Groß

Murthy

2014

Neural Networks

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“…MLA , (Ye et al 2009), (Barbosa and Feng 2010), (Pak and Paroubek 2010) quality of used training dataset (Weiss and Provost 2003;Batista et al 2004;Sheng et al 2008). Wrongly labelled training dataset (e.g., some positive documents in negative training dataset, and vice versa) causes a poor classification performance.…”

Section: Selection Of Data Properties For Comparisonmentioning

confidence: 99%

“…Sentiment classification provides organizations with a tool to transform data into 'actionable knowledge' that decision maker can use in pursuit of improved organizational performance. Customer review data can be used for development of market strategy and decision making for product/ service requirements for customer satisfaction, strategic analysis, and commercial planning Ye et al 2009;Li and Wu 2010;Yu et al 2013;Kang and Park 2014;Meisel and Mattfeld 2010;Yan et al 2015;García-Moya et al 2013). Government and public sector can also take advantages of analysing public sentiment from their blog and social media to obtain citizen feedback on new policy implementation (Ceron et al 2014;Cheong and Lee 2011).…”

Section: Introductionmentioning

confidence: 99%

Data properties and the performance of sentiment classification for electronic commerce applications

Choi

Lee

2017

Inf Syst Front

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Sentiment classification has played an important role in various research area including e-commerce applications and a number of advanced Computational Intelligence techniques including machine learning and computational linguistics have been proposed in the literature for improved sentiment classification results. While such studies focus on improving performance with new techniques or extending existing algorithms based on previously used dataset, few studies provide practitioners with insight on what techniques are better for their datasets that have different properties. This paper applies four different sentiment classification techniques from machine learning (Naïve Bayes, SVM and Decision Tree) and sentiment orientation approaches to datasets obtained from various sources (IMDB, Twitter, Hotel review, and Amazon review datasets) to learn how different data properties including dataset size, length of target documents, and subjectivity of data affect the performance of those techniques. The results of computational experiments confirm the sensitivity of the techniques on data properties including training data size, the document length and subjectivity of training /test data in the improvement of performances of techniques. The theoretical and practical implications of the findings are discussed.

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“…Automatically created lexicon is produced via association [1]. The latter approach involves a supervised classification technique which involves building classifiers from labeled instances of texts or sentences [3]. Naï ve Bayes, SVM and N-gram are some of the most popular sentiment classification techniques [5].…”

Section: Introductionmentioning

confidence: 99%

“…The lexicon-based approach requires a prefixed or predefined dictionary or lexicon and it involves computing the orientation for a document from the polarity of words or phrases in the document [2]. This can be done either manually, semi-automatically or automatically [3]. Manually created lexicon makes use of existing dictionary such the General Inquirer Dictionary which contains information about English word senses, including polarity tags such as positive, negative negation, overstatement, or understatement [4].…”

Section: Introductionmentioning

confidence: 99%

Impact of Financial News Headline and Content to Market Sentiment

Tan¹,

Phang²,

Chin³

et al. 2014

IJMLC

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Abstract-Business and financial news are important resources that investors referred to when monitoring the stock performance. News brings us the latest information about the stock market. Studies have shown that business and financial news have a strong correlation with future stock performance. Business and financial news can be used to extract sentiments and opinions that may assist in the stock price predictions. In this paper, we present a sentiment analyser for financial news articles using lexicon-based approach. We utilized two most important elements of news, the headline and the content as our test data. We use polarity lexicon to distinguish between positive and negative polarity of each term in the corpus. We further investigate on how news headline will affect the sentiment analysis by adjusting the weights of the news headline and news content's sentiment value. Three sets of experiments were carried out using headline only, content only and headline and content as test data. In the experiment, we used non-stemming tokens and stemming tokens when considering individual word found in the news article. The preliminary results are presented and discussed in this paper.

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Sentiment classification of online reviews to travel destinations by supervised machine learning approaches

Cited by 539 publications

References 14 publications

Modeling virtual organizations with Latent Dirichlet Allocation: A case for natural language processing

Modeling virtual organizations with Latent Dirichlet Allocation: A case for natural language processing

Data properties and the performance of sentiment classification for electronic commerce applications

Impact of Financial News Headline and Content to Market Sentiment

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