Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents

By Nobuhiro Kaji, Masaru Kitsuregawa. 2007

288 citations recorded by the source corpus.

How researchers see this paper

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

1. B. Liu
2. Lei Zhang
3. Soujanya Poria
4. Erik Cambria
5. Bing Liu
6. Yejin Choi
7. Percy Liang
8. Rada Mihalcea

Citations in the corpus

• Attention-based LSTM for Aspect-level Sentiment Classification (2016, background): “With the abundance of sentiment lexicons (Rao and Ravichandran, 2009; Perez-Rosas et al., 2012; Kaji and Kitsuregawa, 2007), the lexicon-based features were built for sentiment analysis (Mohammad et al.”
• CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises (2014, methodology): “If the application domain is more focused (e.g., sentiment extraction) the later is used (Kaji and Kitsuregawa 2007), which we also adopt.”
• Sentiment Analysis and Subjectivity (2010, unknown): “Other related work include [42, 100].”
• Opinion Mining and Sentiment Analysis (2008, unknown): “orientationin the literature) or subjectivity status [12, 45, 89, 90, 91, 92, 119, 131, 143, 146, 258, 287, 289, 290, 291, 300, 304, 306].”
• Connotation Frames: A Data-Driven Investigation (2015, background): “Most prior work on sentiment lexicons focused on the overall polarity of words without taking into account their semantic arguments (Wilson et al., 2005; Baccianella et al., 2010; Wiebe et al., 2005; Velikovich et al., 2010; Kaji and Kitsuregawa, 2007; Kamps et al., 2004; Takamura et al., 2005; Adre”
• Aspect and sentiment unification model for online review analysis (2011, methodology): “, SentiWordNet) can be used or a new set of words may be obtained by using sentiment propagation techniques [14, 17, 20].”
• Learning Sentiment Lexicons in Spanish (2012, background): “Most efforts to date, though, have focused on automatic procedures of lexicon construction, such as (Kaji and Kitsuregawa, 2007) for Japanese, (Lu et al., 2010; Xu et al., 2010) for Chinese, or (Banea et al., 2008a) for Romanian.”
• A Survey of Opinion Mining and Sentiment Analysis (2012, unknown): “Other related work includes [43, 44].”
• Towards multimodal sentiment analysis: harvesting opinions from the web (2011, background): “• Polarized words: As shown in previous work on text-based sentiment analysis[15, 26], using a dictionary of positively or negatively polarized words, sentiment polarity can be effectively differentiated.”
• Opinion Word Expansion and Target Extraction through Double Propagation (2011, background): “…categorized as corpora-based (Hatzivassiloglou and McKeown 1997; Wiebe 2000; Wiebe et al. 2004; Turney and Littman 2003; Kanayama and Nasukawa 2006; Kaji and Kitsuregawa 2007) and dictionary-based (Hu and Liu 2004; Kim and Hovy 2004; Kamps et al. 2004; Esuli and Sebastiani 2005; Takamura, Inui,…”
• Ontology-based sentiment analysis of twitter posts (2013, unknown): “The former approach (Kaji & Kitsuregawa, 2007; Neviarouskaya, Prendinger, & Ishizuka, 2009; Taboada, Brooke, Tofiloski, Voll, & Stede, 2011) is based on opinion words, namely, words that are commonly used in expressing positive or negative sentiment.”
• Current State of Text Sentiment Analysis from Opinion to Emotion Mining (2017, methodology): “Further attempts to create a useful lexicon were concerned with clustering of words or phrases in sentiment clusters including the works of Andreevskaia and Bergler [2006], Esuli and Sebastiani [2005, 2006a, 2006b], Finn and Kushmerick [2006], Takamura et al. [2007], and Kaji and Kitsuregawa [2007].”
• Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013, methodology): “Corpus-based methods have been used to bootstrap lexicons for ENGLISH (Turney, 2002) and other languages, including ROMANIAN (Banea et al., 2008) and JAPANESE (Kaji and Kitsuregawa, 2007).”
• Sentiment Analysis and Opinion Mining (2012, methodology): “A more sophisticated approach was proposed in Kamps et al. (2004), which used a WordNet distance-based method to determine the sentiment orientation of a given adjective. The distance d (t1, t2) between terms t1 and t2 is the length of the shortest path that connects t1 and t2 in WordNet. The orient”
• Attentional Encoder Network for Targeted Sentiment Classification (2019, background): “, 2011), mainly focus on extracting a set of features like sentiment lexicons features and bag-of-words features to train a sentiment classifier (Rao and Ravichandran, 2009; Kaji and Kitsuregawa, 2007; PerezRosas et al., 2012; Mohammad et al., 2013).”
• Twitter Sentiment Analysis (2015, methodology): “Other approaches include extracting polar sentences by using structural clues from HTML documents (Kaji and Kitsuregawa, 2007), recognising opinionated text based on the density of other clues in the text (Wiebe and Wilson, 2002).”
• Semi-Supervised Polarity Lexicon Induction (2009, background): “Kaji and Kitsuregawa (2007) outline a method of building sentiment lexicons for Japanese us-ing structural cues from HTML documents.”