Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques

By Jeonghee Yi, Tetsuya Nasukawa, Razvan C. Bunescu, Wayne Niblack. Third IEEE International Conference on Data Mining, 2003

768 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/310b72fbc3d384ca88ca994b33476b8a2be2e27f

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In Artificial Intelligence

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2. Bing Liu
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Citations in the corpus

• Sentiment Analysis and Subjectivity (2010, methodology): “Other related works on feature extraction mainly use the ideas of topic modeling and clustering to capture topics/features in reviews [58, 59, 62, 89, 93, 106].”
• Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews (2006, background): “…a sentence (e.g., Liu et al. (2003), Yu and Hatzivassiloglou (2003), Kim and Hovy (2004), Gamon et al. (2005)), a phrase (e.g., Wilson et al. (2005)), and a specific object (such as a product) mentioned in a document (e.g., Morinaga et al. (2002), Yi et al. (2003), Popescu and Etzioni (2005)).”
• Aspect-based sentiment analysis of movie reviews on discussion boards (2010, methodology): “[33] proposed a method for sentiment analysis of online reviews and news articles to extract positive and negative sentiments for specific subjects from a sentence.”
• Data Quality from Crowdsourcing: A Study of Annotation Selection Criteria (2009, background): “The success of most prior work relies on the quality of their knowledge bases; either lexicons defin-ing the sentiment polarity of words around a topic (Yi et al., 2003), or quality annotation data for statistical training.”
• Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena (2009, methodology): “NLP methods have been used to extract sentiment and opinion from texts such as camera [16] and pharmaceutical reviews [25].”
• Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary (2018, methodology): “In order to mitigate the shortcoming of the purely statistical approaches, they showed the analysis of grammatical sentence structures and phrases based on NLP techniques [6].”
• Sentiment classification: The contribution of ensemble learning (2014, background): “Other studies used sentence-level attempts to classify the positive or negative sentiments for each sentence [49,50].”
• Gated Neural Networks for Targeted Sentiment Analysis (2016, background): “…sentiment analysis (Hu and Liu 2004; Popescu and Etzioni 2007), which extracts product features and opinions towards them from user reviews, and topic-oriented sentiment analysis (Yi et al. 2003; Wang et al. 2011), which extracts features and/or sentiments towards certain topics or subjects.”
• Annotating Expressions of Opinions and Emotions in Language (2005, methodology): “…disregarding context (e.g., Hart (1984), Anderson and McMaster (1982), Hatzivassiloglou and McKeown (1997), Turney (2002), Gordon et al. (2003), Yi et al. (2003)), the observations in this section suggest that different usages of words, in context, need to be distinguished to understand…”
• Just How Mad Are You? Finding Strong and Weak Opinion Clauses (2004, background): “Others perform automatic analyses of product reputations (Morinaga et al., 2002; Nasukawa and Yi, 2003; Yi et al., 2003).”
• Large-Scale Sentiment Analysis for News and Blogs (system demonstration) (2007, methodology): “In [ 13 ], they follow up by employing a feature-term extractor.”
• Automatic construction of a context-aware sentiment lexicon: an optimization approach (2011, background): “Sentiment lexicon plays an important role in most, if not all, sentiment analysis applications, including opinion retrieval [17], opinion question answering and summarization [3], opinion mining [21, 4] and unsupervised sentiment classification [7, 22, 19].”
• Word Sense and Subjectivity (2006, methodology): “…analysis in applications such as review classification (e.g., (Turney, 2002; Pang and Lee, 2004)), mining texts for product reviews (e.g., (Yi et al., 2003; Hu and Liu, 2004; Popescu and Etzioni, 2005)), summarization (e.g., (Kim and Hovy, 2004)), information extraction (e.g., (Riloff et…”
• Approaches, Tools and Applications for Sentiment Analysis Implementation (2015, background): “[10], sentiments of words [11], subjective sentences [12], and topics [4; 13; 14).”