Sentiment analysis: A combined approach

By Rudy Prabowo, Mike Thelwall. J. Informetrics, 2009

887 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/c14f33e11bc1f2b5fee93b3ce4e8046433e26e11

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Citations in the corpus

Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena (2009, neutral): “A number of hybrid methods that blend NLP and machine learning techniques have also appeared in the literature [22].”
Lexicon-Based Methods for Sentiment Analysis (2011, methodology): “Other hybrid methods include those of Andreevskaia and Bergler (2008), Dang, Zhang, and Chen (2010), Dasgupta and Ng (2009), Goldberg and Zhu (2006), or Prabowo and Thelwall (2009).”
Sentiment Analysis: An Overview from Linguistics (2016, positive): “…are able to correctly determine the polarity of unseen data 80% of the time (Andreevskaia & Bergler 2008, Bloom et al 2007, Dinu & Iuga 2012, Prabowo & Thelwall 2009, Socher et al 2011, Yessenalina et al 2010, among many others) While machine learning approaches are desirable because of…”
Machine Learning-Based Sentiment Analysis for Twitter Accounts (2018, methodology): “Methods of sentiment analysis can be categorized predominantly [1] as machine-learning [2], Lexicon-based [3] and hybrid [4,5].”
Current State of Text Sentiment Analysis from Opinion to Emotion Mining (2017, methodology): “In another work, Prabowo and Thelwall [2009] try to build a semi-supervised hybrid classiﬁer by using both rule-based classiﬁers and SVM classiﬁcation.”
Survey on mining subjective data on the web (2011, methodology): “Read and Carroll (2009) further extended this approach, employing Semantic Spaces and Distributional Similarity as alternative weakly-supervised methods.”
Document-level sentiment classification: An empirical comparison between SVM and ANN (2013, methodology): “The most popular sentiment learning techniques are SVM and NB, and many authors have reported better accuracy by using SVM (Abbasi, 2010; Dang et al., 2010; O’Keefe & Koprinska, 2009; Pang et al., 2002; Prabowo & Thelwall, 2009; Ye, Zhang, & Law, 2009).”
Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach (2020, background): “Ensemble learning and deep learning can yield promising results for sentiment analysis tasks [25,58].”
Ontology-based sentiment analysis of twitter posts (2013, neutral): “Examples include, among others, product and services reviews (Kang, Yoo, & Han, 2012; Prabowo & Thelwall, 2009), articles on the Web (Melville, Gryc, & Lawrence, 2009) and news feeds (Moreo, Romero, Castro, & Zurita, 2012; Wanner, Rohrdantz, Mansmann, Oelke, & Keim, 2009).”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, methodology): “Thelwall and Buckley [127] classified sentiments using SentiStrength in two phases: mood setting and lexicon extension.”
Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis (2017, methodology): “Accordingly, in the last decade, sentiment analysis techniques have been used tomeasure the sentiments conveyed through the content of online reviews (Pang & Lee, 2005; Prabowo & Thelwall, 2009).”
Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews (2012, methodology): “…(Dave et al., 2003; Hu & Li, 2010; Hwang & Ko, 2008; Kang, Yoo, & Han, 2009; Kim, Jung, & Mayeng, 2008; Miao, Li, & Dai, 2009; Pang et al., 2002; Prabowo & Thelwall, 2009; Tan & Zhang, 2008; Turney, 2002; Ye et al., 2008; Zhu, Xu, & Wang, 2010), the positive and negative classification research…”
Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN Classifier (2016, background): “and accuracy, Precision and recall as explained below [9].”