The general inquirer: A computer approach to content analysis.

By P. Stone, D. Dunphy, Marshall S. Smith. 1967

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Citations in the corpus

Textual Sentiment in Finance: A Survey of Methods and Models (2014, neutral): “Almost half a century ago, Stone et al .”
How to Analyze Political Attention with Minimal Assumptions and Costs (2010, background): “That said, once (5)One of the most important early dictionary based systems is the General Enquirer (Stone et al., 1966).”
Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation (2017, background): “The term list includes 1,621 positive and negative single words from Osgood’s valence subset of the General Inquirer (Stone et al., 1966).”
How Translation Alters Sentiment (2016, background): “Wan (2008) translated Chinese customer reviews into English using a machine translation (MT) system.”
Developing a unified framework of the business model concept (2010, background): “Essentially, ‘there is no simple right way to do content analysis’ (Weber, 1990), and Stone et al. (1966) define it as ‘any research technique for making inferences by systematically and objectively identifying specified characteristics within text’.”
Towards multimodal sentiment analysis: harvesting opinions from the web (2011, methodology): “General Inquirer: Computer Approach to Content Analysis.”
CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON (2013, background): “We included all terms in the General Inquirer that were not too ambiguous (had at most three senses)—8132 word–sense pairs in all.”
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions (2011, methodology): “We then replaced sentiment words with a sentiment category identifier using the sentiment lexica of the Harvard Inquirer (Stone, 1966) and LIWC (Pennebaker et al., 2007).”
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013, methodology): “5.1 Dictionary Methods We begin with dictionary methods, perhaps the most intuitive and easy to apply automated method (Stone et al. 1966).”
Aspect-based sentiment analysis of movie reviews on discussion boards (2010, background): “The words were extracted and expanded from General Inquirer and the work of Hatzivassiloglou and McKeown [10] and Riloff et al. [12].”
Neural Network-Based Abstract Generation for Opinions and Arguments (2016, background): “- num of words - category in General Inquirer - unigram (Stone et al., 1966) - num of POS tags - num of positive/negative/neutral - num of named entities words (General Inquirer, - centroidness (Radev, 2001) MPQA (Wilson et al.”
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words (2018, background): “• All 4,206 terms in the positive and negative lists of the General Inquirer (Stone et al., 1966).”
Psychological aspects of natural language. use: our words, our selves. (2003, background): “Developed by Stone and colleagues in the early 1960s, the General Inquirer (Stone et al. 1966) is generally considered the " mother " of computerized text analysis.”
Words of wisdom: language use over the life span. (2003, methodology): “Like earlier approaches to computerized content analysis (Rosenberg, Schnurr, & Oxman, 1990; P. J. Stone, Dunphy, Smith, & Ogilvy, 1966 ), LIWC uses a word-count strategy whereby it searches for over 2,000 words or word stems within any given text file.”
SentiWS - A Publicly Available German-language Resource for Sentiment Analysis (2010, background): “The ﬁrst source is (Stone et al., 1966)’s General Inquirer (GI) lexicon.”