A Statistical Approach to Star Rating Classification of Sentiment

Hogenboom, Alexander; Boon, F. C.; Frăsincar, Flavius

doi:10.1007/978-3-642-30864-2_24

Cited by 19 publications

(19 citation statements)

References 14 publications

(17 reference statements)

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“…On the other hand, some approaches exploit (generic) sentiment lexicons when determining the subjectivity or polarity of natural language text. Both approaches may be combined in hybrid methods as well [29].…”

Section: Sentiment Analysismentioning

confidence: 99%

“…The text's overall semantic orientation is then determined by aggregating (e.g., summing) the individual word scores, as retrieved from the sentiment lexicon. 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%

See 2 more Smart Citations

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: Sentiment Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Hogenboom

Heerschop

Frăsincar

et al. 2014

Decision Support Systems

Self Cite

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“…Thus, what is being discussed is not the focus of sentiment analysis, but rather the sentiment toward the topics of discussion (Hogenboom, Boon, & Frasincar, 2012).…”

Section: 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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“…These vectors can contain information about semantic valences (e.g., negation or intensification) [5] and parts of speech [6], and can be split into two categories: domain dependent vectors (e.g. games review, movies review) and domain independent vectors (a general list of words).…”

Section: State Of the Artmentioning

confidence: 99%

Extracting Gamers' Opinions from Reviews

Sirbu

Secui

Dascălu

et al. 2016

2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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Abstract-Opinion mining and sentiment analysis are a trending research domain in Natural Language Processing focused on automatically extracting subjective information, feelings, opinions, ideas or emotions from texts. Our study is centered on identifying sentiments and opinions, as well as other latent linguistic dimensions expressed in on-line game reviews. Over 9500 entertainment game reviews from Amazon were examined using a Principal Component Analysis applied to word-count indices derived from linguistic resources. Eight affective components were identified as being the most representative semantic and sentiment-oriented dimensions for our dataset. These components explained 51.2% of the variance of all reviews. A Multivariate Analysis of Variance showed that five of the eight components demonstrated significant differences between positive, negative and neutral game reviews. These five components used as predictors in a Discriminant Function Analysis, were able to classify game reviews into positive, negative and neutral ratings with a 55% accuracy.

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A Statistical Approach to Star Rating Classification of Sentiment

Cited by 19 publications

References 14 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

Extracting Gamers' Opinions from Reviews

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