Analyzing Sentiment in a Large Set of Web Data While Accounting for Negation

Heerschop, Bas; Iterson, P van; Hogenboom, Alexander; Frăsincar, Flavius; Kaymak, Uzay

doi:10.1007/978-3-642-18029-3_20

Cited by 20 publications

(12 citation statements)

References 10 publications

(14 reference statements)

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“…Hybrid approaches may realize the aggregation through a machine learning process as well [29]. In this sentiment scoring process, other aspects of content may be taken into account as well, such as negation [27,43], intensification [28], or the rhetorical roles of text segments [26,31].…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Typical approaches involve scanning a text for cues signaling its polarity, e.g., (parts of) words or other (latent) features of natural language text. Lexicon-based sentiment analysis methods have gained (renewed) attention in recent work [23][24][25][26][27][28][29], not in the least because their performance has been shown to be robust across domains and texts [30]. Such methods essentially rely on lexical resources containing words and their associated sentiment, i.e., sentiment lexicons, and their nature allows for intuitive ways of accounting for structural or semantic aspects of text in sentiment analysis [26,31].…”

Section: Introductionmentioning

confidence: 99%

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Multi-lingual support for lexicon-based sentiment analysis guided by semantics

Hogenboom

Heerschop

Frăsincar

et al. 2014

Decision Support Systems

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Section: Sentiment Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-lingual support for lexicon-based sentiment analysis guided by semantics

Hogenboom

Heerschop

Frăsincar

et al. 2014

Decision Support Systems

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“…Like the domain-dependent methods, this approach uses lexicon-based vectors to calculate the orientation of documents on the basis of the aggregation of the individual word scores (Turney, 2002). Such approaches have gained attention in more recent research because their performance is robust across texts and domains (Heerschop, van Iterson, Hogenboom, Frasincar, & Kaymak, 2011;Hogenboom et al, 2012;Taboada, Brooke, Tofiloski, Voll, & Stede, 2011), and they can be easily enhanced with the inclusion of multiple dictionaries (Taboada, Brooke, & Stede, 2009).…”

Section: Approaches To Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis

2016

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This study introduces the Sentiment Analysis and Cognition Engine (SEANCE), a freely available text analysis tool that is easy to use, works on most operating systems (Windows, Mac, Linux), is housed on a user's hard drive (as compared to being accessed via an Internet interface), allows for batch processing of text files, includes negation and partof-speech (POS) features, and reports on thousands of lexical categories and 20 component scores related to sentiment, social cognition, and social order. In the study, we validated SEANCE by investigating whether its indices and related component scores can be used to classify positive and negative reviews in two well-known sentiment analysis test corpora. We contrasted the results of SEANCE with those from Linguistic Inquiry and Word Count (LIWC), a similar tool that is popular in sentiment analysis, but is pay-to-use and does not include negation or POS features. The results demonstrated that both the SEANCE indices and component scores outperformed LIWC on the categorization tasks.Keywords Sentiment analysis . Affect detection . Opinion mining . Natural language processing . Automatic tools . Corpus linguisticsThe analysis of sentiment is an important component of a number of research disciplines, including psychology, education, sociology, business, political science, and economics.Measuring sentiment features automatically in a text is thus of value, to better understand how emotions, feelings, affect, and opinions influence cognition, economic choices, learner engagement, and political affiliation. However, the freely available natural language processing (NLP) tools that measure linguistic features related to sentiment, cognition, and social order are limited. The best-known example of an available sentiment analysis tool is Linguistic Inquiry and Word Count (LIWC), which comprises a number of dictionaries that capture conscious and unconscious psychological phenomena related to cognition, affect, and personal concerns. LIWC has proven extremely useful in a number of different disciplines and has had a large impact on our understanding of how lexical elements related to cognition, affect, and personal concerns can be used to better understand human behavior. However, it has several shortcomings with regard to usability and to the facile and broad measurements of its dictionaries. First, LIWC is not freely available (it costs a modest fee). Second, the LIWC indices are based on simple word counts (some of which are populated by fewer than eight words), and the program does not take into consideration issues of valence such as negations, nor part-of-speech (POS) tags, both of which can have important impacts on sentiment analysis. In addition, the indices reported by LIWC are standalone and do not report on larger constructs related to sentiment.This article introduces a new sentiment analysis tool called the Sentiment Analysis and Cognition Engine (SEANCE). SEANCE is a freely available text analysis tool that incorporates a number of freely available s...

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“…The overall semantic orientation of a text is then determined by aggregating (e.g., summing) the word scores, possibly while taking into account other aspects of content as well, e.g., negation [3,5], intensification [13], or rhetorical roles of text segments [2,4]. Lexiconbased approaches enable deep, yet computationally intensive linguistic analysis to be incorporated into the process of analyzing sentiment in natural language text [2] and have been shown to have a robust performance across domains and texts [14].…”

Section: Sentiment Analysismentioning

confidence: 99%

“…The use of sentiment lexicons -lists of words and their associated sentiment, possibly differentiated by Part-of-Speech (POS) and/or meaning [1] -has gained attention in recent research endeavors [2,3]. Such lexicon-based methods have been shown to have a more robust performance across domains and texts than pure machine learning approaches [14].…”

Section: Introductionmentioning

confidence: 99%

A Statistical Approach to Star Rating Classification of Sentiment

Hogenboom

Boon

Frăsincar

2012

Advances in Intelligent Systems and Computing

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Abstract. Automated analysis of the ever-increasing amount of reviews available through the Web can enable businesses to identify why people like or dislike (aspects of) products or brands, yet to this end, a reliable indication of the intended sentiment of reviews is of crucial importance. This sentiment is typically quantified in universal star ratings, which are not always available. We propose and compare the performance of several statistical methods of automatically classifying star ratings of reviews represented by means of a binary vector representation, with features signaling the presence of sentiment-carrying words. A nearest neighbor classifier maximizes recall, whereas a naïve Bayes classifier excels in terms of precision, accuracy, and the root mean squared error of the assigned number of stars.

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Analyzing Sentiment in a Large Set of Web Data While Accounting for Negation

Cited by 20 publications

References 10 publications

Multi-lingual support for lexicon-based sentiment analysis guided by semantics

Multi-lingual support for lexicon-based sentiment analysis guided by semantics

Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis

A Statistical Approach to Star Rating Classification of Sentiment

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