Using appraisal groups for sentiment analysis

By Casey Whitelaw, Navendu Garg, Shlomo Engelson Argamon. International Conference on Information and Knowledge Management, 2005

669 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/0470303953de1d19765423f719d8314e3cb91278

How researchers see this paper

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

In Artificial Intelligence

1. Saif M. Mohammad
2. Maite Taboada
3. Mike Thelwall
4. Erik Cambria
5. Mohit Bansal
6. Yejin Choi
7. Janyce Wiebe
8. Richard Socher

Citations in the corpus

• Fine-grained subjectivity and sentiment analysis: recognizing the intensity, polarity, and attitudes of private states (2008, methodology): “Clues learned from annotated data include distributionally similar adjectives and verbs, and n-grams from (Wiebe et al., 2004). The adjectives and verbs were learned from Wall Street Journal (WSJ) data using Dekang Lin’s (1998) method for clustering words according to their distributional similarity”
• Articles: Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis (2009, background): “…or product and movie reviews (e.g., Turney 2002; Pang, Lee, and Vaithyanathan 2002; Dave, Lawrence, and Pennock 2003; Beineke, Hastie, and Vaithyanathan 2004; Mullen and Collier 2004; Bai, Padman, and Airoldi 2005; Whitelaw, Garg, and Argamon 2005; Kennedy and Inkpen 2006; Koppel and Schler 2006).”
• Current State of Text Sentiment Analysis from Opinion to Emotion Mining (2017, methodology): “In fact, adjectives can be used both as main features, such as in works by Mullen and Collier [2004] and Whitelaw et al. [2005], and as filters for selecting other features.”
• Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums (2008, methodology): “Document-level polarity categorization attempts to classify sentiments in movie reviews, news articles, or Web forum postings [Wiebe et al. 2001; Pang et al. 2002; Mullen and Collier 2004; Pang and Lee 2004; Whitelaw et al. 2005].”
• FinBERT: Financial Sentiment Analysis with Pre-trained Language Models (2019, methodology): “We can divide the recent efforts into two groups: 1) Machine learning methods with features extracted from text with "word counting" [1, 19, 28, 30], 2) Deep learning methods, where text is represented by a sequence of embeddings [2, 25, 32].”
• Document-level sentiment classification: An empirical comparison between SVM and ANN (2013, background): “Whitelaw, Garg, and Argamon (2005) proposed considering adjectival expressions as an important indication of the sentiment polarity in textual reviews.”
• Sentiment analysis using product review data (2015, background): “Sentiment analysis [1-8], which is also known as opinion mining, studies people’s sentiments towards certain entities.”
• Weakly Supervised Joint Sentiment-Topic Detection from Text (2012, methodology): “, from manually built appraisal groups [5], to semiautomatically [16] or fully automatically [17] constructed lexicons.”
• A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, methodology): “Evaluation was performed on sentiment classification of Stanford Twitter Dataset [169], emotion recognition on the ISEAR dataset, and personality detection from Matthews et al. [215] dataset.”
• Opinion Mining and Sentiment Analysis (2008, methodology): “Adjectives have been employed as features by a number of researchers [217, 304].”
• Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews (2006, methodology): “…terms (i.e., terms expressing a positive sentiment (e.g., happy ) or a negative sentiment (e.g., terrible )) and exploiting them to do polarity classification (e.g., Hatzivassiloglou and McKeown (1997), Turney (2002), Kim and Hovy (2004), Whitelaw et al. (2005), Esuli and Se-bastiani (2005)).”
• Delta TFIDF: An Improved Feature Space for Sentiment Analysis (2009, background): “Alternatively, words can be counted as booleans as shown in (Pang, Lee, & Vaithyanathan 2002) and (Whitelaw, Garg, & Argamon 2005), or weighted by their IDF score like (Kim et al. 2006).”
• SentiWS - A Publicly Available German-language Resource for Sentiment Analysis (2010, background): “Apart from that the authors recently took interest in representing more fine-grained emotions, aside from pure polarity (Whitelaw et al., 2005).”
• Cyber Hate Classification: 'Othering' Language And Paragraph Embedding (2018, methodology): “[41] found that character n-grams have been shown to be appropriate for abusive language tasks due to their ability to capture variations of words associated with hate.”
• The evolution of sentiment analysis - A review of research topics, venues, and top cited papers (2016, methodology): “2005], “Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales” [Pang and Lee 2005], “Using appraisal groups for sentiment analysis” [Whitelaw et al. 2005], and “Determining the semantic orientation of terms through gloss classification” [Esuli and Se”
• Sentiment analysis algorithms and applications: A survey (2014, neutral): “[64].”