The general inquirer: A computer system for content analysis and retrieval based on the sentence as a unit of information

Stone, Philip J.; Bales, Robert F.; Namenwirth, J. Zvi; Ogilvie, Daniel M.

doi:10.1002/bs.3830070412

Cited by 184 publications

(71 citation statements)

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“…Much of this work, including the well-known LIWC [23], descends from Harvard's General Inquirer [27], a dictionary for measuring social science concepts in unstructured text. In recent years, researchers have applied more refined and targeted dictionary techniques (e.g., [6,9,11]).…”

Section: Processing Text For Social Informationmentioning

confidence: 99%

Phrases that signal workplace hierarchy

Gilbert

2012

Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work

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Hierarchy fundamentally shapes how we act at work. In this paper, we explore the relationship between the words people write in workplace email and the rank of the email's recipient. Using the Enron corpus as a dataset, we perform a close study of the words and phrases people send to those above them in the corporate hierarchy versus those at the same level or lower. We find that certain words and phrases are strong predictors. For example, "thought you would" strongly suggests that the recipient outranks the sender, while "let's discuss" implies the opposite. We also find that the phrases people write to their bosses do not demonstrate cognitive processes as often as the ones they write to others. We conclude this paper by interpreting our results and announcing the release of the predictive phrases as a public dataset, perhaps enabling a new class of status-aware applications.

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Section: Processing Text For Social Informationmentioning

confidence: 99%

Phrases that signal workplace hierarchy

Gilbert

2012

Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work

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“…Two semantic features that are new to solving this problem, LIWC (Pennebaker et al, 2007) and INQUIRER (Stone et al, 1962), are employed in this work. The intuition behind is that people usually embed semantic meanings, such as emotion and reasoning, into text.…”

Section: Introductionmentioning

confidence: 99%

Semantic Analysis and Helpfulness Prediction of Text for Online Product Reviews

Yang¹,

Yan²,

Qiu³

et al. 2015

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confere

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Predicting the helpfulness of product reviews is a key component of many ecommerce tasks such as review ranking and recommendation. However, previous work mixed review helpfulness prediction with those outer layer tasks. Using nontext features, it leads to less transferable models. This paper solves the problem from a new angle by hypothesizing that helpfulness is an internal property of text. Purely using review text, we isolate review helpfulness prediction from its outer layer tasks, employ two interpretable semantic features, and use human scoring of helpfulness as ground truth. Experimental results show that the two semantic features can accurately predict helpfulness scores and greatly improve the performance compared with using features previously used. Cross-category test further shows the models trained with semantic features are easier to be generalized to reviews of different product categories. The models we built are also highly interpretable and align well with human annotations.

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“…INQUIRER The General Inquirer (Stone et al, 1962) is another dictionary of 7,444 words, grouped in 182 general semantic categories. For instance, the word absurd is mapped to tags NEG and VICE.…”

Section: Featuresmentioning

confidence: 99%

Detecting (Un)Important Content for Single-Document News Summarization

Yang

Bao

Nenkova

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of documentsummary pairs. When used for singledocument summarization, our approach, combined with the "beginning of document" heuristic, outperforms a state-ofthe-art summarizer and the beginning-ofarticle baseline in both automatic and manual evaluations. These results represent an important advance because in the absence of cross-document repetition, single document summarizers for news have not been able to consistently outperform the strong beginning-of-article baseline.

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The general inquirer: A computer system for content analysis and retrieval based on the sentence as a unit of information

Cited by 184 publications

References 0 publications

Phrases that signal workplace hierarchy

Phrases that signal workplace hierarchy

Semantic Analysis and Helpfulness Prediction of Text for Online Product Reviews

Detecting (Un)Important Content for Single-Document News Summarization

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