A Study of Information Retrieval Weighting Schemes for Sentiment Analysis

By Georgios Paltoglou, Mike Thelwall. Annual Meeting of the Association for Computational Linguistics, 2010

368 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4ac9bf53e4c5fae5ff6feacf96f2e2c474fcc6ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Duyu Tang
2. Bing Qin
3. Ting-Wei Liu
4. Lei Zhang
5. Bing Liu
6. Mohit Bansal
7. Yoshua Bengio
8. Erik Cambria

Citations in the corpus

• Learning Word Vectors for Sentiment Analysis (2011, background): “Paltoglou and Thelwall (2010) perform an extensive analysis of such weighting variants for sentiment tasks.”
• Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier (2016, unknown): “In a paper [16] by Paltoglou, G.”
• Document-level sentiment classification: An empirical comparison between SVM and ANN (2013, methodology): “The number of times a term occurs in a document (i.e., term frequency) is also used as a weighting scheme for textual data (Li et al., 2009; Paltoglou & Thelwall, 2010).”
• A Survey of Opinion Mining and Sentiment Analysis (2012, methodology): “In [74], feature weighting schemes are used to enhance classification accuracy.”
• Sentiment analysis: A review and comparative analysis of web services (2015, background): “This level is associated with the task called document-level sentiment classification [104,58,18,31,108,102,66,41].”
• A review of natural language processing techniques for opinion mining systems (2017, methodology): “Information retrieve methods are commonly used to construct feature space for machine learning approaches, such as unigram and bigram with tf-idf weights [35, 36, 37].”
• Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior (2013, background): “Recent sentiment analysis algorithms are able to detect positive and negative sentiment strength in short informal texts with a reasonable degree of success [1, 54].”
• Document Modeling with Gated Recurrent Neural Network for Sentiment Classification (2015, background): “Many of them focus on designing hand-crafted features (Qu et al., 2010; Paltoglou and Thelwall, 2010) or learning discriminate features from data, since the performance of a machine learner is heavily dependent on the choice of data representation (Bengio et al.”
• Graph-based term weighting for text categorization (2015, methodology): “Then, we briefly describe the classification models that are used in the core of a TC system.”